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Cultivar mixtures of processing tomato in an organic agroecosystem

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Cultivar mixtures of processing tomato in an organic agroecosystem

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Felipe H. Barrios-Masias & Marita I. Cantwell & Louise E. Jackson

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Published online: 15 December 2010

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# The Author(s) 2010. This article is published with open access at Springerlink.com

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Abstract At an organic farm in California, managed biodiversity was manipulated by establishing a mustard cover crop (MCC) and fallow during winter, and after incorporation, tomato mixtures of one, three, and five cultivars were planted in the spring (1-cv, 3-cv, and 5-cv, respectively). It was hypothesized that cultivar mixtures may increase yields over a monoculture if disease pressure or nitrogen (N) availability is affected by the previous cover crop. The monoculture (1-cv) of the grower's preferred cultivar was compared with mixtures of it and other high-yielding cultivars in the region. Soil nitrogen, soil microbial biomass carbon (MBC), soil emissions of carbon dioxide (CO2) and nitrous oxide (N2O), crop nutrient uptake, biomass, fruit quality, intercepted photosynthetically active radiation (PAR), and disease symptoms were measured. The MCC reduced soil N leaching potential during winter and immobilized soil N early in the tomato season as suggested by higher soil MBC and CO2 emissions. Tomatoes had higher PAR, aboveground biomass, fruit yields, and harvest index in the winter fallow than in

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the fallow plots after transplanting. All cultivar mixtures had fairly similar yield and shoot biomass within fallow and MCC, probably explained by the low genetic diversity among California modern tomato cultivars. However, at mid-season (75 days after planting (DAP)), the 3-cv mixture had higher shoot and fruit biomass, by 46% and 63%, than the monoculture in the MCC, indicating some plasticity under lower N availability. In the fallow treatment, soil CO2 emissions were lower in the 3-cv mixture than the monoculture at 77 and 100 DAP. Tomatoes in the 3-cv mixture were redder than the monoculture. The 3-cv mixture thus had some minor advantages compared with the monoculture, but overall, there was little evidence of higher ecosystem functions from mixtures vs. monoculture. Further research on mixtures of processing tomatoes may only be warranted for conditions of higher environmental stress than occur in California organic farms or if specific genotypic traits become available such as for disease resistance or improved nutrient uptake.

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the winter MCC, likely due to higher N availability in

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F. H. Barrios-Masias: L. E. Jackson (*) Department of Land, Air and Water Resources, University of California Davis, One Shields Avenue, Davis, CA 95616, USA e-mail: lejackson@ucdavis.edu

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M. I. Cantwell Department of Plant Sciences, University of California Davis, One Shields Avenue, Davis, CA 95616, USA

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Keywords Brassica cover crop . Fruit quality . Nitrogen . Soil . Solanum lycopersicum L. . Sclerotium rolfsii Sacc

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Introduction

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In organically managed agroecosystems, higher temporal and spatial biodiversity within production fields can be achieved by crop rotations, mosaic cropping,

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and inter- and intraspecific crop mixtures (Skelsey et al. 2005; Smith et al. 2008). These practices are commonly used on organic farms due to their beneficial effects on nitrogen (N) fixation, nutrient uptake and retention, transfer of nutrients between taxa, pest control, and productivity (Pimentel et al. 2005; Berntsen et al. 2006; Smukler et al. 2008). Many organic farms rely on the sustainable intensification of farm management practices that rely on renewable resources, ecological stability, and biodiversity (USDA-NOP 2009), to increase productivity and lessen environmental degradation (Vandermeer et al. 2002; Kremen 2005; Jackson et al. 2007).

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Cultivar mixtures are a type of within-field diversification (Jensen 1952; de Vallavieille-Pope 2004). At present, their use has focused on disease management and yield. For example, wheat, barley, and rice are planted in intraspecific mixtures to prevent disease outbreaks and spread in the USA (WASS 2005), Germany (Finckh et al. 2000), and China (Meung et al. 2003). In a review of 100 studies of crop mixtures (mostly grains and legumes), yields were often slightly greater than the mean of the component cultivars (Smithson and Lenne 1996). Yield stability of crop mixtures can exceed that of their individual components across a range of soil types (Cowger and Weisz 2008). Nonetheless, farmers may be unwilling to risk lower yields in mixtures than in monocultures (Jensen 1952). It is often difficult to demonstrate that higher productivity occurs for mixtures than for the best-yielding monoculture, partly because so many different mixtures must be tested (Cardinale et al. 2006). Nevertheless, trait diversity in a mixture can improve the crop response to biotic or abiotic stress especially in low-input systems, where yields may be more susceptible to variation in resource availability (Newton et al. 2009).

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Cultivar selection for mixtures depends on plant characteristics such as agronomic compatibility (Lopez and Mundt 2000), genotypic diversity (Mundt 2002), high yields, and marketability. The usual number of genotypes in a blend, i.e., cultivar mixture, tends to be around three (Mundt 2002). Modern, highyielding cultivars can perform well in mixtures especially when intraspecific interactions among different cultivars within a species are low and harvest can occur simultaneously (Phillips and Wolfe 2005). Competition for dominance and niche complementarity among cultivars can start from early plant development.

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A cultivar's response in a mixture depends on the composition of a particular mixture and the surrounding environment (Gallandt et al. 2001). Thus, a specific cultivar can perform differently in various mixtures than in monoculture, such as for harvest index (Brim and Schutz 1968).

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Cover crops are another means for increasing the temporal diversity in communities. Some of the benefits of cover crops are the control of weeds and diseases, higher yields, reduction of run-off and soil erosion, increased water infiltration in the soil, and uptake and immobilization of nutrients that otherwise could be lost from the system (Dabney et al. 2001; Snapp et al. 2005; Smith et al. 2008; Wang et al. 2008). Brassica cover crops, in addition, have been reported to have biofumigation effects after incorporation in the soil that decrease disease incidence in subsequent crops (Brown and Morra 1997). Nutrient uptake by crops and soil N retention are affected by the nutrient release from cover crops, which varies temporally, and depends on the quality of the cover crop residues, e.g., C/N ratio (Wilke and Snapp 2008). Thus, the choice of a cover crop can affect ecosystem functions not only during their growth period, but also through the following cash crop season.

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The central question of this study was: what are the benefits of a cultivar mixture compared with a monoculture in an organic agroecosystem? We made the following hypothesis based on the literature reviewed above: tomato cultivar mixtures may increase crop trait diversity when compared with the grower's preferred cultivar monoculture, and thus favor higher yields, especially if problems with disease occur, and if soil N availability is affected by a previous cover crop. This hypothesis was tested at an organic farm, with a set of commercially important processing tomato cultivars that were grown after either winter cover crop or fallow treatments to alter nutrient availability during the summer tomato growing season.

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The specific objectives were to: (1) measure yield responses and nutrient uptake of a locally prevalent tomato cultivar, when grown in three different tomato communities, and after a winter cover crop vs. winter fallow treatment; and (2) assess the effects of temporal and spatial biodiversity (by the use of cover crop and cultivar mixtures) on nutrient availability and N losses, yields, tomato disease, and fruit quality.

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The discussion considers factors that could improve the benefits that may be derived from cover crops and mixtures of cultivars on organic farms.

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Field description

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Our study involved participatory research with Rominger Brothers Farms, on a 26.5 ha field that had been in organic production for 12 years in Yolo County, California. The main commodities are processing tomato (Lycopersicon esculentum Mill. = Solanum lycopersicum L.) and oats (Avena sativa L.) as hay. Fall/winter cover crops are grown in most years. Processing tomatoes are grown every year on alternating fields, following organic standards (California Certified Organic Farmers http://www.ccof. org/), and utilize conventional tillage. The entire farm is mapped as a Tehama silt loam, a fine-silty, mixed, superactive, thermic Typic Haploxeralfs (USDA-SCS 1972). Total rainfall from November 2005 to April 2006 was 839 mm with over 40% of rain concentrated between December 17 and January 5. Average minimum and maximum temperatures during Fall, 2005, and Winter, 2006, i.e., the cover crop period, fluctuated between 6.0°C and 17.6°C, respectively. During the tomato season, the minimum and maximum average temperatures were 15.4°C and 34.1°C, respectively, with a minimum of 8.9°C and a maximum of 42.2°C between mid-June and mid-September of 2006, and no rainfall occurred (CIMIS 2009).

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Cover crop and cultivar mixtures

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The experiment was conducted on a 0.8-ha plot and received the same management as the rest of the field, e.g., irrigation and weeding. During Summer/Fall, 2005, the entire field was laser leveled, compost applied at a rate of 17 Mg per ha (C/N ratio of 9.7; composed of turkey manure and wine-grape solid residue), beds were prepared (1.52 m from furrow to furrow), and a mustard cover crop (MCC) (Brassica nigra [L.] Koch) was planted on November 3 with three planting lines per bed. A total of 16 plots, eight fallow and eight MCC plots (main plots), of 16 m long by 9 m wide (six beds per plot) were established

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in a randomized complete block design. The cover crop was mowed on April 26, 2006, and mustard residue was lightly incorporated in the top 10 cm of soil after 19 days, with sprinkler irrigation in between. Also the fallow fields were not tilled until this date. After cover crop incorporation, main plots were divided in three 5-m subplots (15 m total) along the 16 m length; a 0.5-m buffer strip from the main plot was left on each edge along the bed. Thus, a total of 48 subplots (5 m long by 9 m wide) were established in eight blocks with two main plots per block and three subplots per main plot. Each cultivar mixture (see below) was replicated eight times, once in every fallow and MCC main plot, for a total of 16 plots per mixture.

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The processing tomato cultivars used in this experiment were: \"AB-2\", \"CXD-179\", \"H-2601\", \"H-8892\", and \"Red Spring\". The cultivar mixtures (subplot treatments) consisted of the \"choice cultivar\" (AB-2) grown by the farmer in the entire field (1-cv); a mixture of the \"choice cultivar\" plus two more cultivars (CXD-179 and H-8892) used by the same farmer on his other fields (3-cv); and these three cultivars plus two more (H-2601 and Red Spring) that were currently used by organic growers in California for a total of five cultivars (5-cv). All cultivars had the following characteristics, based on discussions with growers and nursery managers: high yielding and marketable for processing, grown commercially with similar amounts and timing of inputs, mid-maturity varieties, i.e., ~125 days from planting to harvest, and fruit quality that met industry standards. Cultivars within mixtures were arranged in subsets depending on the mixture, and the subsets were repeated continuously through the bed. The three cultivar mixtures (1-cv, 3-cv, and 5-cv) were manually transplanted after a mechanical planter had marked the planting rows. A total of 5760 seedlings were transplanted on May 18 and 19 on two rows per bed with plants separated 50 cm within each row, i.e., four plants per linear m on a bed or 2.66 plants m−2 , the same density as used by the farmer. Between planting and harvest on September 7 and 8, field management included a mechanical weeding/cultivation, manual weeding, a sulfur application as a mite and disease preventive measure, and furrow irrigations at intervals of about 11 days for a total of nine irrigations of 88±22 mm event−1 (Smukler et al. 2011).

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Soil sampling

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During the cover crop period, soil was sampled on January 19, 2006, and a week previous to incorporation on April 17 (-110 and -32 days after planting (DAP), respectively). During the tomato season, soil was sampled on May 25, July 17, and September 4 (7, 59, and 108 DAP, respectively). Sampling at 59 and 108 DAP was done on only three blocks (six main plots and 18 subplots total), due to time and labor limitations. Soil samples of about 500 g were taken from three depths (0-15, 15-30, and 30-60 cm), and were the composites of two cores per treatment. Wellmixed soil subsamples were measured for KClextractable nitrate (NO<sub>3</sub><sup>-</sup>) and ammonium (NH<sub>4</sub><sup>+</sup>) with colorimetric determination using modifications of Miranda et al. (2001) and Foster (1995). Potentially mineralizable N (PMN) was determined from 7-day incubated soil at 37°C under anaerobic conditions, followed by KCl extraction and NH<sub>4</sub><sup>+</sup>-N colorimetric determination (Waring and Bremner 1964). Microbial biomass carbon (MBC) was analyzed for the 0-15- and 15-30-cm depths using the fumigation extraction method, and total MBC was calculated by multiplying the flush of C by 2.64 (Vance et al. 1987). PMN and MBC were not determined for the January 19 sampling. Gravimetric soil moisture was determined at every sampling event.

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Carbon dioxide (CO_2) and nitrous oxide (N_2O) gas emissions were sampled after three irrigation events of the tomato crop at 28, 77, and 100 DAP using closed, capped chambers for 30 min (Rolston 1986). Chambers were placed on the bed shoulder between the furrow and each individual cultivar of the mixture treatment. This location aimed to capture gas emissions from zones of high root and microbial activity during rapid changes in soil moisture after an irrigation event. Air samples were taken immediately after placing the chamber (0 min) and at 30 min after with air-tight glass syringes. Gas samples were stored in vacutainers for <1 week. N<sub>2</sub>O concentrations were analyzed on a gas chromatograph (HP 6890, Hewlett Packard, Palo Alto, CA), and CO<sub>2</sub> concentrations were determined using a GC with a thermal conductivity detector (HP 5890, Hewlett Packard, Palo Alto, CA). Air and 1 and 7 cm soil depth temperatures were recorded with a digital thermometer (Fisher Scientific Inc., USA).

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Plant sampling and measurements

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Cover crop aboveground biomass was sampled in both MCC and fallow treatments at -32 DAP. Three subplots of 60 by 50 cm were sampled in each main plot (MCC or fallow). The biomass of all three MCC planting rows was included by sampling 60 cm across the bed top. Biomass was separated into mustard, volunteer oats, and weeds, and a composite sample was made from the three subplots. Plants were rinsed and oven dried at 60°C.

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Tomatoes were sampled three times during the growing season at 39, 75, and 111 DAP. For the first two samplings, a set of either three (in the 1-cv and 3-cv) or five (in the 5-cv) plants per subplot were sampled. The \"choice cultivar\" (AB-2) within each mixture was always sampled individually. The other cultivars were sampled as a composite mixture. For the first two samplings, only four blocks were sampled, but all eight blocks were included at the time of harvest. At harvest, sampling was done on two beds per subplot along a 2.25 m strip (nine plants for the 1-cv and 3-cv mixtures), or 2.50 m strip (ten plants for the 5-cv mixture) for a total of 18 or 20 plants per subplot, respectively. Plants were clipped at the soil surface, sorted into vegetative and reproductive structures, and subsamples were oven dried at 60° C, weighed, and ground for nutrient analyses. For vield evaluation, fruits from each cultivar were recorded as total fruit and also sorted into only machine harvestable fruits. All plant samples were processed at the DANR Analytical Laboratory, UC Davis, for total N using a Nitrogen Gas Analyzer (LECO FP-528, St. Joseph, MI), and total K and P by 2% acetic acid extraction and microwave acid

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digestion, respectively, and quantitative determination by atomic absorption spectrometry or inductively coupled plasma-atomic emission spectrometry (Meyer and Keliher 1992).

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Canopy light interception was measured at 35, 69, and 95 DAP using a portable-tube solarimeter with sensors for photosynthetically active radiation (PAR; AccuPAR-80, v. 4.5, Decagon Devices, Inc. Pullman, Washington). Measurements were taken at canopy intervals of 12.5 cm for the first sampling and 25 cm for the last two samplings. The bed length sampled was 1.25 m (i.e., five plants) in the 1-cv and 5-cv mixtures and 1.50 m (i.e., six plants) for the 3-cv mixture to include equal representation of all cultivars in a mixture.

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Disease evaluation for Sclerotium rolfsii Sacc. (Southern blight), a major problem in the entire field, consisted of individual assessment of plants in relation to disease symptoms at 74 DAP and harvest (111 DAP). Plants were scored on a scale from 1 to 5, where 1 was 0% of the individual plant affected, 2 (25%), 3 (50%), 4 (75%), and 5 (100% or dead).

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Postharvest quality and fruit composition were evaluated at 105 DAP by collecting a total of 25-50 fruits per cultivar from at least six plants within a mixture. Fruits were stored at 10°C for 6 days, sorted, washed, weighed, and selected for evaluations (12 fruits per rep×4 reps). From each cultivar within a mixture, 12 fruits of marketable quality (no serious defects and no decay) and weights between 40 and 80 g were evaluated for fruit color, firmness, soluble solids (SS), titratable acidity, and pH (Mitcham et al. 2003). Fruit color was determined as L^*a^*b^* color values with a reflectance colorimeter (Minolta CR300 color meter) and expressed as hue angle (hue decreases as fruit develops red color). Firmness was tested with a nondestructive tomato firmness device (Qualitest durometer) with values expressed as percent of maximum force (13 Newtons). SS were determined from samples frozen at -20°C, which were partially thawed, blended, filtered and measured on a digital refractometer. Titratable acidity (TA) was determined from 10-mL juice, titrated with 0.1 N NaOH, and pH was read directly on a pH meter.

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Statistical analysis

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The experimental design was a complete randomized block design with a split-plot treatment structure.

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Analysis of variance (ANOVA) was performed using the GLM procedure of SAS, Version 9.1 (SAS Institute, Cary, NC). The error term used for main plots, i.e., fallow and MCC treatments, was specified as \"e = main plot \\times block\". Shapiro–Wilk W test for normal distribution and Levene's test for homogeneity of variance were used to test that data fulfilled the ANOVA assumptions. Data was transformed as necessary when assumptions were not met. Tukey–Kramer HSD test was used to determine significant differences among treatments at P < 0.05.

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Results

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Winter fallow and mustard cover crop treatments

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Available inorganic N as NH<sub>4</sub><sup>+</sup>-N and NO<sub>3</sub><sup>-</sup>-N differed between the fallow and MCC treatments, with greatest differences between treatments after cover crop incorporation and tomato transplanting (Fig. 1a). The NO_3^- pool was <1 g N m<sup>-2</sup> (0–60-cm depth) during the winter period, and increased in the spring and summer to >3 g N m<sup>-2</sup>, with mean values as high as 5.4 g N m<sup>-2</sup> in the mid-tomato season. This high value corresponds to pools of 2.2, 1.8, and 1.4 g N m<sup>-2</sup> at 0–15-, 15–30-, and 30–60-cm depths, respectively. More specifically, the winter MCC decreased NO<sub>3</sub>-N prior to cover crop incorporation and at 7 DAP. Ammonium was highest in the winter (2.5 g N m<sup>-2</sup>), and then decreased to approximately 1 g N m<sup>-2</sup> prior to cover crop incorporation and after tomato transplanting. At 7 DAP, soil NH<sub>4</sub>+N was slightly higher for the MCC treatment, and later remained in the same range for both fallow and MCC treatments.

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Soil MBC during the tomato growing season was generally higher in the MCC treatment (Fig. 1b). In the 0–15-cm soil layer, MBC values fluctuated more than at the 15–30-cm depth (data not shown). Results are presented as the composite values for both depths. Differences were found at 7 DAP, with >20% increase in MBC for the MCC treatment, but not later in the season. Potentially mineralizable N decreased with time from 8.8 g N m<sup>-2</sup> before cover crop incorporation (–32 DAP) to 4.1 g N m<sup>-2</sup> by tomato harvest (108 DAP), but was not different between fallow and MCC treatments on any date (data not shown). Soil CO<sub>2</sub> and N<sub>2</sub>O emissions were higher early in the

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Fig. 1 Effects of a cover crop (winter mustard and winter fallow) on soil NH<sub>4</sub><sup>+</sup>-N and NO<sub>3</sub><sup>-</sup>-N at 0–60-cm depth (a), and soil microbial biomass carbon (MBC) at 0–30-cm depth (b) during the cover crop and the following tomato crop. Five sampling dates expressed as days after transplanting (DAP) of

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tomato season (28 DAP) than at mid-season (77 DAP) and before harvest (100 DAP) (Fig. 2). The MCC treatment had higher CO_2 emissions than the fallow at 28 DAP (139.9 and 222.5 mg CO_2-C m<sup>-2</sup> h<sup>-1</sup> for fallow and MCC treatments, respectively; P<0.05).

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Aboveground plant biomass incorporated in the spring was 4.6\\pm0.4~{\\rm Mg~ha}^{-1} in the MCC plots and 0.8\\pm0.1~{\\rm Mg~ha}^{-1} in the fallow plots, which contained weeds and volunteer oats. This resulted in total aboveground plant N of 66.0\\pm4.1 and 18.0\\pm2.0~{\\rm kg~N~ha}^{-1} in MCC and fallow treatments, respectively. Both biomass and N content were higher in the cover crop treatment (P<0.0001). The C/N ratio of the cover crop was about 30 at the time of sampling.

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The tomatoes in the fallow treatment intercepted more PAR on all three sampling dates during the tomato growing season (Table 1). Harvestable fruit and total fruit biomass at 111 DAP were higher in the fallow treatment by 44% and 39%, respectively (Table 1). Shoot biomass was similar in both treatments, but harvest index was significantly higher for the fallow treatment. Plant nutrient content (N, P and K) were similar on all dates, but at harvest, N tended to be higher (P<0.10) for the winter fallow treatment (fallow, 11.5 g N m<sup>-2</sup> and MCC, 9.6 g N m<sup>-2</sup>), and P content of non-harvestable fruit tended also to be higher (fallow, 0.6 g P m<sup>-2</sup> and MCC, 0.5 g P m<sup>-2</sup>; P<0.10). Plants lost to S. rolfsii Sacc. were not different between main plots with a survival rate of 89% for

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fallow and 83% for the MCC (data not shown). Fruit were redder in the fallow treatment than the MCC (fallow, 38.9 hue and MCC, 39.8 hue; P<0.05), and fruit firmness tended to be higher in the MCC (fallow, 75.2% and MCC, 77.2%; P=0.06). Fruit weight, % SS, pH, and TA were similar between tomatoes grown in fallow and MCC treatments (data not shown).

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Tomato cultivar mixtures

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Soil NO<sub>3</sub><sup>-</sup>- and NH<sub>4</sub><sup>+</sup>-N, potentially mineralizable N and MBC did not differ among cultivar mixtures (data not shown). Soil gas emissions of the cultivar mixtures were not different, but there were main plot × subplot interactions (Fig. 2). Within the fallow treatment, CO<sub>2</sub> emissions were higher in the monoculture than in the 3-cv mixture in the last two spot samplings (77 and 100 DAP). N<sub>2</sub>O emissions were higher before harvest (100 DAP) in the 5-cv mixture, but were quite low overall.

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At harvest, vegetative growth and yields were similar for the three cultivar mixtures within each of the two winter treatments. Separate analyses by main plot were performed due to main plot*subplot interactions. Within the fallow treatment, cultivar mixtures had similar PAR interception and biomass production (Fig. 3). Within the MCC, some differences were observed. At 39 DAP, biomass in the MCC monoculture was higher than the MCC 5-cv

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Fig. 2 Soil CO_2 (a, b) and N_2O (c, d) emissions of one (1-cv), three (3-cv), and five (5-cv) tomato cultivar mixtures following a winter fallow (a, c) or winter mustard cover crop (b, d). Data obtained from three spot sampling dates during the tomato growing season (days after transplanting (DAP)). Data show the mean \\pm standard error. Means followed by different letters are significantly different at P < 0.05 (n=3). Uppercase letters for comparisons between sampling dates, and lowercase letters for mixtures within a sampling date

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mixture. At 75 DAP, shoot and fruit biomass for the MCC 3-cv mixture were 46% and 63% higher than the MCC monoculture, but there were no differences by harvest (Fig. 3). Canopy PAR interception for the

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MCC monoculture was significantly higher than the MCC 3-cv mixture at final harvest (Fig. 3).

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Postharvest quality and compositional analyses showed differences in color and fruit weights among

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Table 1 Percent photosynthetically active radiation (PAR) intercepted, tomato shoot and fruit biomass, harvest index and N uptake at early and mid-crop season, and harvest time for processing tomato mixtures after the two winter treatments

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DAPaVariablesCover crop treatment
Fallow mean ± SEMustard mean ± SE
35PAR intercepted (%)20±1.0 a15±0.9 b
69PAR intercepted (%)46±1.0 a39±1.3 b
95PAR intercepted (%)47±1.2 a43±1.3 b
39Shoot biomass (g m-2)70 \\pm 6.854±4.7
75Shoot biomass (g m-2)246 \\pm 17.4275 \\pm 19.7
111Shoot biomass (g m-2)294±9.3274 \\pm 13.2
75Total fruit (g m-2)122±12.5126 \\pm 13.8
111Total fruit (g m-2)352±15.4 a252±15.1 b
111Harvestable fruit (g m-2)234±14.7 a162±15.6 b
111Aboveground N (g N m-2)12±0.410 \\pm 0.4
111Harvest index0.36 \\pm 0.02 a0.30±0.01 b
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Data show the mean \\pm standard error for all cultivar mixture treatments, since no differences were observed amongst them. Means followed by different letters are significantly different at P < 0.05. n = 24, except on DAP 39 and 75 when n = 12

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&lt;sup>a</sup> Days after transplanting

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Fig. 3 Aboveground biomass (shoots and fruits; a, b) and percent interception of photosynthetically active radiation (c, d) of cultivar mixtures conformed of one (1-cv), three (3-cv), and five (5-cv) tomato cultivars. Data shown by mainplots: winter fallow (a, c) and winter mustard cover crop (b, d). Data obtained from three sampling dates during the tomato growing season (days after transplanting (DAP)). Data represent the mean ± standard error. Means followed by different letters are significantly different at P<0.05. n=8 except on DAP 39 and 75 when n=4

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cultivar mixtures. The fruit in the 3-cv mixture were redder than those in the monoculture (1-cv, 40.3 hue; 3-cv, 38.5 hue; and 5-cv, 39.4 hue). The average fruit weight in the monoculture was higher than fruit in both mixtures (1-cv, 63.4 g; 3-cv, 49.5 g; and 5-cv, 50.2 g). Fruit firmness, % SS, pH, and TA were not different between the mixtures (data not shown).

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\"Choice cultivar\"

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The choice cultivar performed similarly within the mixtures under the fallow treatment (Fig. 4). But under the MCC treatment, the choice cultivar showed marked differences within cultivar mixtures in the first two biomass samplings. At 39 DAP, total plant biomass of the choice cultivar (g plant−1 ) in the MCC monoculture (1-cv) was higher than in the MCC 5-cv mixture. At 75 DAP, the choice cultivar in the MCC 3-cv and 5-cv mixtures had higher vegetative aboveground biomass than the choice cultivar in MCC monoculture. Fruit biomass of this cultivar was also higher in the MCC 3-cv mixture than in the MCC monoculture. By harvest, no differences were found for shoot, harvestable fruit, and total fruit for the

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'choice cultivar' in the different mixtures. The choice cultivar fruit were redder in the fallow than the MCC treatment (39.6 and 41.3 hue, respectively).

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Discussion

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At this organic farm, cultivar mixtures did not result in increased yields compared with the monoculture chosen by the farmer, even under conditions of low N availability and disease pressure. Unexpectedly, the 1-cv, 3-cv, and 5-cv treatments were quite similar in terms of nutrient uptake, light interception, shoot and fruit biomass. This suggests that trait diversity was low, with little differentiation in resource use or disease resistance. The cover crop increased soil N retention before and after its incorporation, reducing potential losses of soil NO3 − -N, but decreasing tomato yields due to an apparent initial period of high microbial N immobilization after cover crop incorporation. Organic farms typically rely on higher biodiversity to enhance ecological functions (Hole et al. 2005; IFOAM 2005; Badgley and Perfecto 2007), but this study suggests that the application of

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Fig. 4 Biomass accumulation of the 'choice cultivar' in tomato mixtures of one (1-cv or choice cultivar in monoculture), three (3-cv), and five (5-cv) cultivars at three different days after transplanting (DAP). Shoot biomass (a, b) and total and harvestable fruits (c, d). Data shown by mainplots: winter fallow (a, c) and winter mustard cover crop (b, d). Data obtained from three sampling dates during the tomato growing season (days after transplanting (DAP)). No fruits present at 39 DAP. Data represent the mean ± standard error. Means followed by different letters are significantly different at P<0.05. n=4 on 39 and 75 DAP. n=8 on 111 DAP

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these concepts requires an understanding of the specific traits that enhance complementarity, the complexity of plant–plant and plant–soil interactions, and the potential benefits under fluctuating environmental conditions.

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Cultivar mixtures

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The lack of significant differences between the mixtures and the monoculture in crop performance and resource utilization may be the result of reduced genetic diversity in California modern tomato cultivars. Processing tomatoes are specifically bred for a compact canopy and determinate growth to facilitate mechanical harvest, and improved varieties usually result from crosses of already existing varieties (Tanksley and McCouch 1997; Jones et al. 2007). Thus, a narrow genetic diversity may have limited the potential for niche differentiation and complementarity that could have given mixtures an advantage over the monoculture.

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Commercial cultivars are developed for their performance in monocultures but not for their

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agronomic response in cultivar mixtures (Worster and Mundt 2007; Newton et al. 2009). Testing the best performing mixture becomes challenging because of the large number of possible combinations that a group of cultivars can generate. In this study, the five cultivars would have generated 26 different combinations. The mixtures were assembled from currently used cultivars with high agronomic compatibility, but with no prior knowledge of the cultivars' performance in mixtures. Similarly, for wheat mixtures assembled based on cultivar characteristics and monoculture performance, yields never exceeded the highest yielding monoculture even under N limitation at an organic farm, even though component cultivars differed in early seasonal vigor, height, leaf area index, and time to maturity (Kaut et al. 2009). Some studies have found that different spatial arrangements of agricultural crops in mixtures (wheat and clover) also have an effect on biomass productivity (Worster and Mundt 2007), but it is unlikely that it would have occurred here due to the similarity of the cultivars used.

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Identifying the best-yielding monoculture is easier than finding a mixture that outperforms the monocul-

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ture (Schmid et al. 2008). Choosing an overyielding monoculture rather than a cultivar mixture also depends on a grower's decision to either: (1) achieve the highest yield under higher risk, given that some resource deficiency or disease may occur; (2) secure a more stable yield in a fluctuating environmental situation, e.g., drought or heatwaves; or (3) assure high yields under conditions of consistently low environmental stress (Ceccarelli 1996; Phillips and Wolfe 2005; Kaut et al. 2009). For instance, mixtures with traits related to different flowering times in maize secured yields from terminal drought (Tilahun 1995). Cultivar diversity can increase yield stability by different cultivar response to stress conditions (Cowger and Weisz 2008; Newton et al. 2009). The tomato cultivars used in this study, however, were bred for high yields under the high-input, low-risk conditions of conventional, irrigated agriculture in California's Central Valley. Thus, traits that confer stress tolerance may be largely absent in all of the cultivars. One exception might be the differential response when N availability was limited due to delayed cover crop incorporation. The higher aboveground biomass of the MCC 3-cv mixture at 75 DAP may be the type of desired response in environments where nutrient source and temporal availability fluctuate, although it did not translate into higher productivity by harvest. In this case, the choice cultivar temporarily had higher biomass in the mixture than in the monoculture, suggesting that reduced competition may have allowed rapid exploitation of the limited soil N, generating a positive growth response. This suggests that research on competition and trait complementarity among components/cultivars in mixtures may reap benefits compared with exclusively focusing on overyielding monocultures in low-input systems (Vandermeer et al. 2002; Jackson et al. 2007).

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All mixtures were equally affected by S. rolfsii, a soilborne pathogen for which no specific resistance has been reported from any of these cultivars. In fact, mixtures may be more conducive to suppression of airborne pathogens than soil pathogens, because innoculum is more pervasive due to long-term presence in the soil, and to continuous root-soil pathogen contact from the onset of crop establishment (Mundt 2002; de Vallavieille-Pope 2004; Newton et al. 2009).

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The University of California statewide processing tomato variety trials conducted on conventionally managed farms show that monocultures of the five cultivars used in this study can vary in yield depending on location and year (UCCE 2009). For instance, the 'choice cultivar' was better yielding than two of the other four cultivars used in this study in 2007, yielded the same as two of these cultivars in 2006, and it yielded less than two of these cultivars for the year 2005, based on an average of at least five locations. Within years, cultivar yields varied by as much as 65%, but as low as 35%, depending on location. Thus, no single cultivar excelled across the range of environmental conditions for California processing tomatoes under conventional, high-input, irrigated agriculture. Overall, the cultivars appear to vary somewhat in response to annual variation in weather, local soils, or slight differences in management, but apparently not enough to generate differences in these mixtures.

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In theory, stability of yields is higher when plants have complementary traits (Tilman et al. 2002). Greater productivity and more efficient resource use by a diverse plant community can occur due to high niche differentiation, which tends to increase with the number of plant taxa present (Loreau et al. 2001; Tilman et al. 2001). Canopy architecture, timing of fruit set, and above- and belowground allocation patterns can increase niche differentiation (Cowger and Weisz 2008). While not observed here, there is still the possibility that an advantage of tomato

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mixtures could occur with a more diverse set of cultivars especially in organic farming systems, which have greater fluctuation in N availability (Papendick and Elliott 1984), and less means available for disease control than conventional systems.

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Fallow and mustard cover crop

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The cover crop during the winter in this Mediterranean climate effectively reduced the potential for NO<sub>3</sub><sup>-</sup>-N leaching up until the time of incorporation. Late rains in the spring forced the grower to delay the incorporation of the MCC, and the maturity of the plants was the most likely reason for increased soil N immobilization potential, as suggested by higher MBC and CO<sub>2</sub> emissions from the soil (Wyland et al. 1995).

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The tomato crop performed better under winter fallow, likely due to higher N availability early in the tomato growing season. Early timing of N release from a cover crop was more important for corn yield, which has early N demand, compared with wheat for which N demand may be later (Smith et al. 2008). Negative effects of cover crops on N availability for the succeeding crop are often related to higher C/N ratio (Wyland et al. 1995; Thorup-Kristensen et al. 2003; Berntsen et al. 2006; DuPont et al. 2009); a delay of 3 weeks in the incorporation of a nonleguminous cover crop can increase the C/N ratio by as much as 50% (Vaughan and Evanylo 1998). Organic tomato yields decreased with delayed incorporation of a grass-legume cover crop, while legumes alone as a cover crop with a lower C/N ratio, did not affect yields (Madden et al. 2004).

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Despite these problems that occurred in an unusually wet spring, cover crops must still be considered as a key element for organic vegetable production (Martini et al. 2004; Lenzi et al. 2009). Practices to deal with large amounts of cover crop biomass include earlier mowing followed by immediate incorporation, which would have allowed decomposition to occur earlier in the year. Also, it may have been advantageous to delay tomato planting to match cover crop N release with crop N demand.

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Biofumigation by Brassica spp. incorporation did not reduce the total plants lost to disease, as has also been seen in other field studies which considered soilborne pathogens like Verticillium dahliae Kleb., Fusarium spp., and Pseudomonas spp. (Scott and Knudsen 1999; Hartz et al. 2005). In mustard-

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incorporated soils, S. rolfsii populations were not different than non-planted soils (Njoroge et al. 2008). Positive effects on disease suppression depend on the amount of biomass, method of incorporation, tissue disruption, and timing related to pathogen life-cycle stage (Morra and Kirkegaard 2002; Matthiessen and Kirkegaard 2006; Snapp et al. 2007). Thus, the biofumigation effect of a cover crop is dependent on careful and precise management, and may need to be combined with other practices, e.g., rotations with nosusceptible crops, for a reduction in plant disease.

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Fruit quality

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Increased harvest quality benefits growers because of price premium incentives that processors establish in relation to color, pH and %SS. Among the mixtures, fruit were redder (lower hue value) in the 3-cv mixture, and this suggests that harvest quality can be improved. Cultivar traits, light exposure, and temperatures all affect tomato color (McCollum 1954; Dumas et al. 2003). Light exposure was measured as PAR interception, but no differences were found between mixtures. Tomatoes in the fallow treatment, however, had higher intercepted PAR and fruit were redder. Reduced canopy shading exposes fruits to direct sunlight, and fruit may reach temperatures above 32°C, which reduces color development (McCollum 1954; Dumas et al. 2003).

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Processing tomato cultivars differ in their final red color as they ripen from a pink to a red color (Garcia and Barrett 2006). Slightly different ripening times among tomato cultivars might cause a response in either direction, leading potentially to higher, more constant, or lower quality depending on the ratio of different components in a mixture. Mixtures of varieties that can be held in the field with high quality fruit would be advantageous for growers who are constrained in time by a contract with a processing facility.

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Conclusions

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This study suggests that processing tomato mixtures in California lack enough trait diversity to provide benefits, compared with monoculture, and this differs from work on other crop mixtures, e.g., wheat, barley, and maize. Developing mixtures that outperform the monoculture will likely require planning to combine cultivars

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with explicit traits that result in specific community interactions, niche complementarity, disease resistance, or higher resource utilization, such as spatial or temporal nutrient demands. Future research on mixtures of processing tomatoes for organic farms should target specific genotypic traits to meet such constraints.

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Acknowledgments We thank Bruce Rominger from Rominger Brothers Farms for his help and willingness to conduct this research, Sean Smukler for his support and field data collection, and the USDA CSREES Organic Agriculture Research and Education Initiative Award 04-51106-02242 for funding.

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Open Access This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

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    Assessment of best management practices for nutrient cycling: A case study on an organic farm in a Mediterranean-type climate

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    S.M. Smukler, A.T. O'Geen, and L.E. Jackson

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    Abstract: The effectiveness of best management practices (BMPs) designed primarily to protect surface water quality was assessed on a farm certified for organic tomato production to consider potential environmental quality and production tradeoffs. The BMPs included winter cover crops typically used in organic farming to cycle nutrients and reduce stormwater runoff; tailwater ponds designed to capture runoff; and tailwater return systems, which recycle runoff back to the field. The study took place at a 44 ha (108 ac) farm in Yolo County, California, over a two-year period. Monitoring throughout the winter rainy season showed cover crops successfully reduced runoff and loads of several constituents during the storm events, when compared to fallow. Total discharge was reduced by 44%, total suspended solids was reduced by 83%, ammonium was reduced by 33%, and dissolved organic carbon (DOC) was reduced by 58%. Estimates of leaching losses of DOC in the cover cropped fields, however, were 70% higher than the fallow fields in the winter rainy season and were 30% higher than the fallow fields in the summer irrigation season. During the summer irrigation season, the tailwater pond alone was highly effective in reducing losses of total suspended solids and volatile suspended solids to the neighboring riparian zone by 97% and 89%, respectively. The tailwater pond had no effect on dissolved reactive phosphorous and actually increased concentrations of nitrate-nitrogen (NO<sub>3</sub>-N) in effluent by 40% and DOC by 20%. As was expected, the NO,-N leaching measured by anion exchange resin bags and nitrous oxide emissions measured by static closed chambers was higher for the tailwater pond than the fallow field. Despite these differences, losses via NO, -N leaching and nitrous oxide emissions accounted for only 24.7 and 0.48 kg N ha<sup>-1</sup> y<sup>-1</sup> (22.0 and 0.40 lb N ac<sup>-1</sup>), respectively, for the entire farm, even including ponds and ditches. When field and plot values were extrapolated to the entire tomato production area to understand the relative potential tradeoffs, results indicate that BMPs could be implemented without an impact on tomato marketable yields; the tailwater pond's higher nitrous oxide emissions would not significantly increase the overall emissions for tomato production given its relatively small size; and using tailwater ponds in combination with cover crops would decrease total suspended solids (TSS) losses compared to cover crops alone, with only minor increases in NO<sub>3</sub>-N and DOC losses. Adding a tailwater return system to this combination of BMPs could help minimize these NO<sub>3</sub>-N and DOC losses. Use of cover crops with a tailwater pond and tailwater return system are a combination of BMPS that can thus be recommended for organic production when considering multiple environmental outcomes.

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    Key words: best management practices—cover crops—nutrient cycling—organic farming—tailwater pond—tradeoffs

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    Farmers who aim to produce food and fiber with fewer environmental impacts need a better understanding of the potential tradeoffs among management

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    options (Jordan et al. 2007). California has begun regulating nonpoint source pollution through mandatory runoff monitoring established by the Irrigated Lands

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    Regulatory Program. The monitoring is designed to identify at-risk watersheds and implement best management practices (BMPs), which synchronize the availability of nutrients with crop demand, prevent their movement off farm, or capture and recycle them. In California's Mediterranean-type climate, runoff commonly occurs during the heavy winter rains of the cool nonproduction season and during irrigation operations in the hot dry summer production season. In furrow-irrigated vegetable production, surface runoff can exceed 50% of applied water if poorly managed (Bjorneberg et al. 2002). Best management practices have been mainly designed and implemented for high-input conventional farms but are also important for the rapidly expanding organic vegetable production sector. It is unclear, however, if current BMPs designed to protect water quality will perform in the context of California's even more recent environmental mandate, AB32, to reduce greenhouse gas emissions.

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    Organic systems are limited to nonsynthetic inputs (e.g., compost, manures, and cover crops) and require mechanical cultivation for weed control, and thus, can be especially sensitive to asynchronous nutrient availability, leading to crop yield reduction and sediment and nutrient losses (Berry et al. 2002; Watson et al. 2002; Willson et al. 2001). Nitrogen losses via surface runoff and leaching can be relatively low in organic systems (Sileika and Guzys 2003; Aronsson et al. 2007), but phosphorus losses can be a concern when the use of manures for meeting nitrogen (N) crop demands results in soil phosphorus that exceeds crop demand (Nelson and Janke 2007). Sediment losses are also a concern given that chemical control of weeds is not an option to reduce erosion risk in organic systems (Shipitalo and Edwards 1998).

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    Winter cover crops, annual or perennial plantings grown during California's rainy season, are a key nutrient cycling management tool for organic production and are a recommended BMP for protecting water-

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    Sean M. Smukler is an assistant professor in the faculty of Land and Food Systems at the University of British Columbia, Vancouver, British Columbia. Anthony T. O'Geen is a soil resource specialist and Louise E. Jackson is a professor and specialist in Cooperative Extension in the Department of Land, Air and Water Resources, University of California, Davis, California.

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    ways for any type of agricultural production system. Winter cover crops have been shown to decrease N leaching (Jackson et al. 1993; Tonitto et al. 2006; Wyland et al. 1996), increase soil organic matter (Kong et al. 2005; Lee and Phillips 1993), provide subsequent crops with residue-derived nutrients (Tonitto et al. 2006), and effectively reduce erosion (Dabney 1998; Mutchler and Mcdowell 1990) and stormwater runoff (Joyce et al. 2002).

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    Winter cover crops may, however, result in some unintended environmental impacts or even reduce short-term agricultural productivity, but these tradeoffs have not been adequately evaluated. For example, higher soil C and N availability from cover crops may increase soil carbon dioxide (CO<sub>2</sub>) and nitrous oxide (N<sub>2</sub>O) emissions (Baggs et al. 2000; Johnson et al. 2005). These emissions, without significant concomitant C sequestration, could make organic farming a net contributor to global warming (Jackson et al. 2004; Sarkodie-Addo et al. 2003). Increased C availability may also contribute to dissolved organic carbon (DOC) in surface runoff and leachate, causing problems for drinking water treatment. Should winter rains extend into the spring, direct seeding into the winter cover crop or winter cover crop incorporation can be delayed. Delay of seeding or incorporation can result in reduced yields of the subsequent summer crop (Clark et al. 1999) and large quantities of undecomposed residue that can affect summer furrow irrigation. While cover crops may increase soil infiltration and water holding capacity (Dabney et al. 2001), additional BMPs may be required to ensure that runoff leaving organic farm fields is not negatively impacting adjacent waterways.

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    Tailwater ponds, another BMP recommended for agriculture, may be an effective compliment to winter cover crops to reduce soil and nutrient losses (Hartz 2006). Smallscale ponds can be combined with pumping systems that return tailwater (i.e., effluent) to the field for irrigation purposes. By recycling effluent, tailwater return systems can reduce runoff loses to the environment and improve irrigation efficiency, thus reducing costs (Schwankl et al. 2007). The effectiveness of tailwater ponds and tailwater return systems to protect water quality in the context of California agriculture is largely assumed (Schwankl et al. 2007) and, for organic farms, is virtually unknown.

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    Beyond the impact on water quality, potential environmental tradeoffs need to be better understood before tailwater return systems are constructed on a wide scale. High water content and anaerobiosis (Harrison and Matson 2003; Harrison et al. 2005; Johnson et al. 2005) in soils in and around the ponds may result in a substantial increase in a farm's N<sub>2</sub>O production. Tailwater ponds may also lead to downward seepage of nutrients, which can contaminate groundwater, depending on a pond's management (e.g., improper sealing). Installation of the ponds also requires land to be taken out of production, reducing the overall yield of a farm.

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    Many of these potential tradeoffs result from ecological processes that occur at various scales over varying time periods, making accurate assessment challenging. To evaluate tradeoffs, it is therefore necessary to select indicators of these processes that can be monitored in a way that can capture spatial and temporal variation effectively (DeFries et al. 2004; Dale and Polasky 2007). The specific objectives of this study were to (1) quantify and monitor the magnitude, timing, and pathway of nutrient losses from organic processing tomato (Solanum lycopersicum) production in California's Central Valley; (2) evaluate the effectiveness of cover crops, tailwater ponds, and tailwater return systems to minimize these losses; and (3) assess the relative environmental quality and agricultural production tradeoffs and potential synergies that should be considered when implementing these BMPs.

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    Materials and Methods

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    Site Description. The 44 ha (108 ac) farm, located at the western edge of the Sacramento Valley (figure 1) has been in organic tomato (Solanum lycopersicum L.) production since 1993 and relies mainly on cover crops and compost for nutrient inputs (table 1). The farm is located on an alluvial fan along the riparian corridor of Chickahominy Slough, on a Tehama silt loam, (fine-silty, mixed, superactive, thermic Typic Haploxeralfs) (Andrews 1972).

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    The farm is divided into two fields: North Field is 26.5 ha (65.5 ac), and South Field is 14.7 ha (36.3 ac). These fields are in an alternate year rotation between oat (Avena sativa) production and processing tomatoes (figure 2). The fields have a slope of 1% to 1.5%. Irrigation and winter runoff drain into a network of ditches that occupy 0.02 ha

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    (0.05 ac) at the eastern end of the farm and then drain into tailwater ponds that occupy 0.06 ha (0.15 ac), where a pump returns the water to the top of the field to be mixed with either pumped groundwater or water delivered through California's aqueduct system and reused for crop irrigation.

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    The Mediterranean-type climate has hot, dry, nearly rainless summers and cool, wet winters, typically with several larger storm events that can be >20 mm d<sup>-1</sup> (>0.79 in day<sup>-1</sup>). Thus, the results of this two-year experiment are reported by two seasons, either Irrigated (April through October) or Rainfed (November through March). The average minimum and maximum air temperatures between the beginning of the experiment in March of 2005 and its end in April of 2007, were 8.7°C and 23.6°C (47.6°F and 74.8°F), respectively. In the first year of the experiment (April 2005 to March 2006), rainfall was unusually high (863 mm [34.0 in]), and in the following year (April 2006 to March 2007), rainfall was unusually low with 213 mm (8.4 in), compared to average precipitation (508 mm [20.0 in]) for the previous five years. In fact, no runoff was recorded during the wet season of the second year (Rainfed Y2).

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    The experiment began in April of 2005, when a tomato crop was planted on the South Field (figure 2). At that time, the North Field was still in oats, planted the previous fall. After the tomatoes were harvested in the fall of 2005, the South Field was planted in oats, and the grower divided the North Field into sections to compare a mustard cover crop (Brassica nigra [L.] Koch) with winter fallow. In the spring of 2006, the mustard cover crop was mowed, incorporated by discing, and a tomato crop was planted across the entire North Field. In June 2006, the oats in the South Field were harvested and a summer cowpea (Vigna unguiculata [L.] Walp. ssp. unguiculata) cover crop was planted for only a month and then was incorporated by discing.

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    Field Sampling. Tomato yields were sampled within three days of the grower's harvest. To capture yield variability across the field, transects were oriented north-south of each main sampling plot (393 m [1,289 ft] in the North Field or 250 m [820 ft] in the South Field). Along each transect, a 1 \\times 3 m² (3.28 \\times 9.84 ft²) subplot was established at 30 m (98.4 ft) intervals (five or nine subplots depending on the width of the field). At each

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    Figure 1

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    Location of the farm site, map of the farm, tailwater pond configuration and runoff sampling points. During Irrigated Y1, sampling took place at (1) the main irrigation pipe and (2) tomato field discharge point using automated samplers of the South Field, and grab samples were taken at (3) the discharge point for the sediment trap of the South Field. During the Rainfed Y1 and Irrigated Y2 seasons, automated samplers collected discharge at (4) the exit point of the fallow section of the North Field, which was divided (dotted line) in two; (5) the exit point of the mustard cover crop section of the North Field; (6) the exit point for North Field discharge into the sediment pond; and subsequently, (7) the exit point of the tailwater pond, where the irrigation effluent is pumped back to the west end (top) of the North Field.

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    \n \n Table 1\n \n \n Compost, weeds, and aboveground cover crop inputs for the 2006 tomato crop.\n \n

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    MassTotal CTotal NNO3NРK
    Input(Mg ha-1)(kg ha-1)(kg ha-1)C:N ratio(kg ha-1)(kg ha-1)(kg ha-1)
    Compost152,892.3297.99.727.734.8264.3
    Fallow (weeds)1.7712.112.855.8_4.122.6
    Mustard cover crop5.22,236.046.448.2_11.475.5
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    Notes: C = carbon. N = nitrogen. NO_3^--N = nitrate-nitrogen. P = phosphorus. K = potassium. — = no data.

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    sampling point, individual tomato plants were cut at the base, and the fruit was separated by hand. Fruit quality was evaluated in the field by classification into split reds, pinks, greens, sunburn, mold or rot, blossom end rot, insect damage, and undamaged red tomatoes (USDA 1997). Marketable tomatoes were those considered likely to be harvestable by mechanized equipment, specifically, split red tomatoes, pink tomatoes, sunburn tomatoes, insect damaged tomatoes, and undamaged tomatoes. All weeds were identified to species and were harvested for aboveground biomass from each plot. Biomass of fruits, tomato vegetative material, and weed bio-

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    mass were weighed in the field (fresh weight) and then were subsampled and dried at 60°C (140°F), before being ground and analyzed for total N, phosphorus, and potassium (K) at the University of California Agriculture and Natural Resources Analytical Laboratory. The nutrient content of the mustard cover crop treatment was measured just before incorporation in March of 2006 (table 1).

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    The quantities of nutrients added to the fields through the incorporation of compost were estimated using the farmer's records of application rates and analysis of piles within a week of application. Compost piles were sampled randomly before incorporation and

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    were analyzed for N, phosphorus, and K. Bulk density of the compost was measured using the core method (Blake and Hartge 1986). Compost for the 2005 crop was applied before the experiment started and was not analyzed.

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    Surface runoff and tailwater were monitored at the field scale (>10 ha [>24.7 ac]) at catchment points (figure 1), while leachate and emissions of CO, and N2O were monitored using a stratified random sampling approach at the plot level (16 m<sup>2</sup> [172 ft<sup>2</sup>]). During the first season (Irrigated Y1), irrigation influent and field discharge were continuously monitored on the South Field using ISCO 6700 (Teledyne Technologies, Lincoln, Nebraska) autosamplers fitted with low-profile area velocity flow meters. The autosamplers collected a 250 mL (8.5 oz) subsample every 4 h, whenever there was at least 5 cm (2 in) of water present in the channel or pipe, and composited subsamples daily.

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    The tailwater pond system utilized a two-stage treatment of water effluent from the drainage ditch. The first was a smaller sediment trap, and the second was a larger detention pond, after which water flowed

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    Total monthly precipitation and irrigation and daily mean temperatures by season for the two-year experiment. The cool wet winters and warm dry summers shown here are typical of California's Mediterranean-type climate. The first rainfed season (Rainfed Y1) was an unusually wet winter, and the second rainfed season (Rainfed Y2) was an unusually dry winter. Corresponding timelines of the crop rotations for the North Field and South Field are illustrated below. The North Field was divided into two sections following oats in the fall of the first year. Irrigated Y1 and Irrigated Y2 refer to the irrigated crop growing seasons of both years.

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    into the main tailwater pond (figure 1). In the first year, only the sediment trap was sampled as the effluent water exiting from the tailwater pond on the South Field was removed by subsurface pumping at the bottom of the pond, making it impossible to monitor. Effluent from the South Field sediment trap was sampled daily by hand (grab samples).

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    Each year autosamplers were positioned to collect runoff from the different fields and crop mixes (figure 1). Irrigation influent to the fields was monitored with daily grab samples, and irrigation flow rates were calculated using records of hourly pump use and mean flow rates for the pump system, as determined with autosamplers. Influent into

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    the tailwater pond (water discharged from the field) was calculated from a weighted average of the flow from the autosampler in the two sections (tomatoes/mustard and tomatoes/fallow). The remaining field area was not part of the cover crop trial. Discharge from this area was diverted from the experiment via an additional ditch and was excluded from runoff calculations.

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    Plots were selected in March of each year to monitor changes in soil properties, soil solution, soil CO<sub>2</sub> and N<sub>2</sub>O emissions, and yields. Plots were stratified randomly in a geographic information system within the boundaries of the North Field, South Field, drainage ditches, and tailwater pond (figure

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    1). At the beginning of the experiment, six plots were established in each of the large sampling sites (North Field and South Field), and three were established in the smaller sites (drainage ditches and tailwater ponds). These plots were abandoned in irrigated year 2 (Irrigated Y2), and plots were rerandomized within each sampling site—this time including the new sites in the North Field, which was divided into the mustard cover crop and winter fallow treatments, each with three sampling plots. Thus in Irrigated Y2, three plots occurred in each site: North Field tomatoes/mustard, North Field tomatoes/ fallow, South Field, North Field drainage ditches, and North Field tailwater pond.

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    At the beginning of each of the four sampling seasons (figure 2), three soil cores (6.3 cm [2.48 in] diameter) were taken at random in each plot every 15 cm (5.9 in) to a depth of 75 cm (29.5 in), composited, and put on ice for analysis of ammonium (NH,+-N) and nitrate-nitrogen (NO3-N) (see below for laboratory analysis methods). Soil from the 0 to 15 and 15 to 30 cm (0 to 5.9 and 5.9 to 11.8 in) depths was analyzed for an additional suite of soil properties (see below for laboratory analysis methods). Bulk density was determined at 0 to 6, 9 to 15, and 18 to 24 cm (0 to 2.36, 3.54 to 5.91, and 7.1 to 9.5 in) depths, using rings of 345 cm<sup>3</sup> (21.1 in<sup>3</sup>) volume to remove intact soil cores (Blake and Hartge 1986).

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    Soil solute leaching was assessed for all sites in two ways: ceramic cup suction lysimeters (Soil Moisture Equipment Corp; Zoarelli et al. 2007) and anion exchange resin bags for cumulative NO3-N losses (Wyland and Jackson 1993). After sampling for soil inorganic N at the beginning of each of the four sampling seasons, 7.6 cm (3.0 in) diameter resin bags were buried at 75 cm (29.5 in). Resin bags were placed within a 1 cm (0.39 in) deep polyvinyl chloride ring of the same inner opening diameter designed to protect the bag and facilitate collection and then were packed into a shelf dug into the side of augured hole. Resin bags were collected at the end of each season and were extracted with 2 molar potassium chloride, which was analyzed for NO<sub>3</sub>-N. Resin (AG 1-X8) was assumed to recover 84.7% of the cumulated NO<sub>3</sub>-N losses up to a threshold of 3.7 g NO_3^--N kg^{-1} resin (0.74 oz NO_3^--N lb^{-1}) (Wyland and Jackson 1993) or a maximum of 95 kg NO<sub>2</sub>-N ha<sup>-1</sup> (84.6 lb NO<sub>2</sub>-N ac<sup>-1</sup>) for the size of resin bag used in this study.

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    Lysimeters were installed to 30 and 60 cm (11.8 and 23.6 in) depths to capture the variability in movement of soil water given the potential asynchrony of irrigation or rainfall and sampling. A vacuum of 75 kPa (0.74 atm) was applied to the lysimeters and was sampled weekly during summer irrigation and winter rainfed seasons.

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    Cumulative leaching past the 30 and 60 cm (11.8 and 23.6 in) lysimeters was estimated by multiplying observed concentrations of analyzed constituents by calculated soil solution deep percolation (DP) for each sampling period, using the simplified one-dimensional water balance equation:

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    DP = I - IR + P - ETc \\pm VR, \\qquad (1)

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    where P = precipitation (mm); I = water applied by irrigation (mm); IR = water exiting the field as irrigation runoff; ETc = crop evapotranspiration (mm); VR = variation of soil water reserve, based on gravimetric soil moisture (\\theta_m) measurements (mm); and DP = deep percolation (mm) (Wagenet 1986). Daily irrigation, runoff, and precipitation data were collected using the autosamplers. The ETc was modeled using the Basic Irrigation Scheduling (BIS) model for each crop (Snyder et al. 2007) using the crop coefficient (Kc) and reference evapotranspiration (ETo). The ETo was calculated using the modified Penman-Monteith method (Allen et al. 1998), and climate data was acquired from a nearby weather station (California Irrigation Management Information System [CIMIS]). Values for Kc were determined using the BIS model. The VR was calculated from the change in gravimetric soil moisture between sampling periods. The water balance for drainage ditches was calculated using the differences in flow rates from the autosamplers that monitored the irrigation water exiting the different field sections. Calculations assumed similar ETc to the fields given that narrow ditches (<20 cm [<7.9 in] wide) were bordered by tomatoes on one side and weeds on the other.

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    Annual soil emissions of carbon dioxide-carbon (CO_2-C) and nitrous oxide-nitrogen (N_2O-N) were estimated from gas samples taken randomly from the surface of beds between tomato plants and in ditches and tailwater ponds within each plot when water was not present, or if present, within 6 cm of water's edge. Gaseous emissions were measured one day each month. Gas samples of

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    CO2-C and N2O-N were collected using cylindrical vented static chambers 12.3 cm (4.84 in) in diameter and 11.0 cm (4.3 in) tall with a total interior volume of 1,307 cm3 (79.8 in3) (Hutchinson and Livingston 1993). At the same time, CO<sub>2</sub>-C was also monitored using a LI-COR 8100-102 portable survey chamber 10 cm (3.94 in) in diameter (LI-COR Biosciences, Lincoln, Nebraska), which was placed within 30 cm (11.8 in) of the static chambers. LI-COR 8100 samples were taken and analyzed at three-minute intervals. The instrument has a measurement range of 0 to 3,000 ppm and reported accuracy reading of 1.5% (LI-COR Biosciences, Lincoln, Nebraska). Polyvinyl chloride collars for the static chambers were pounded into the soil surface between 6 to 24 h before sampling and then were removed to avoid disturbance by farming operations. Nitrous oxide from vacutainers was analyzed on a gas chromatograph with a radioisotope nickel-63 (63Ni) electron capture detector (HP 6890, Hewlett Packard, Palo Alto, California). Concentrations of CO<sub>2</sub>-C were determined from vacutainer samples using a gas chromatograph with a thermal conductivity detector (HP 5890, Hewlett Packard, Palo Alto, California).

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    Laboratory Analysis. Within 24 h, soil samples were homogenized in the laboratory on ice and were analyzed for gravimetric moisture and potassium chloride-extractable NO, -N and NH, +-N colorimetrically (Foster 1995; Miranda et al. 2001). Soils sampled at the beginning of each year were air dried for further analysis. Electrical conductivity (EC) (Rhoades 1982) and pH were determined with a 1:1 ratio of soil to deionized water (USSL 1954). Air-dried soil samples and oven-dried plant samples were analyzed for total C and N using a dynamic flash combustion system coupled with a gas chromatograph (Department Agriculture and Natural Resources Analytical Lab 2007). Soils were also analyzed for Olsen phosphorus (Olsen and Sommers 1982) and soil texture by laser diffraction (Eshel et al. 2004).

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    All water samples were immediately put on ice and were transported back to the laboratory for filtering after each irrigation or rain event. Unfiltered samples were analyzed for EC and pH. For runoff samples, to ensure thorough mixing of suspended solids, 50 mL (1.7 oz) was pipetted from the sample while it was being vortexed and then the 50 mL was suction filtered through preweighed

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    0.7 \\mu m (2.76 \\times 10^{-5} in) pore-size glass fiber filters, which were weighed and dried at 60°C (140°F). Filtered samples were frozen for temporary storage. Total suspended solids (TSS) were calculated from differences in prefilter and postfilter dry weights (Clesceri et al. 1998). Dried filters were then subsampled (cut in half), reweighed, placed in crucibles and ignited in a muffle furnace at 550°C (1,022°F) for 30 minutes. Volatile suspended solids were calculated from the difference in pre- and postignition of the dried half-filter weights. Water samples were analyzed for NO<sub>3</sub>-N and NH<sub>4</sub>+-N dissolved reactive phosphate (DRP) colorimetrically (Murphy and Riley 1958), and DOC on a Dohrmann Phoenix 8000 UV-persulfate oxidation analyzer (Tekmar-Dohrmann, Cincinnati, Ohio).

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    Daily loads of constituents in runoff samples were calculated for influent and discharge from either composited autosampler samples or grab samples using a flow-weighted average. Thus, mean concentrations for each day were multiplied by the total flow for the day and were divided by the area of discharge. Daily loads were summed for the duration of each irrigation or storm event.

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    Management Tradeoff Analysis: To estimate the relative benefits, tradeoffs, and identify potential synergies of implementing BMPs for organic processing tomatoes, plot and field data for production and environmental indicators were extrapolated to the entire area of the farm that was in tomato production during rainfed year one (Rainfed Y1) and Irrigated Y2. Tomato production using a winter fallow (tomatoes/fallow) was contrasted with BMP options that include winter cover crops, winter cover crops and tailwater ponds, or winter cover crops, tailwater ponds, and a tailwater return system. Mean results for marketable tomato yield, TSS, NO<sub>3</sub>-N, DRP, DOC leaching and/ or runoff loads, and N2O and CO2 soil emissions for the North Field, ditches and tailwater pond were multiplied by their respective areas, summed for the two seasons, and divided by their total summed areas. This produced a per hectare annual rate for each indicator. The N2O-N was converted to CO<sub>2</sub> equivalents using a conversion factor that takes into account emissions of N<sub>2</sub>O are 298 times greater than equal emissions of CO2 over 100-year time period (Forster et al. 2007).

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    Table 2Soil properties taken at the o to 15 cm and 15 to 30 cm depth for each of the sampling areas for the 2005 and 2006 seasons. Means and standard errors of two years of sampling are shown (sample size = 3 to 6 per site).

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    SiteDepth
    (cm)    		Bulk
    density
    (g cm-3)    		рНEC (µS cm-1)Total N
    (Mg ha-1)    		Total C
    (Mg ha-1)    		Olsen-P
    (mg kg-1)    		Sand
    (g kg-1)    		Silt
    (g kg-1)    		Clay
    (g kg-1)
    South field0 to 151.2 ± 0.07.2 ± 0.0138.4 ± 18.52.5 ± 0.121.9 ± 1.333.7 ± 3.6148.6 ± 39.3707.0 ± 21.7144.5 ± 20.5
    15 to 301.3 \\pm 0.07.2 \\pm 0.1144.7 ± 8.52.6 \\pm 0.120.5 ± 1.128.3 ± 4.5140.6 ± 48.4670.9 ± 29.5188.4 ± 21.0
    North field0 to 151.3 ± 0.07.4 \\pm 0.1120.2 ± 7.92.5 ± 0.122.4 ± 1.031.4 ± 1.2119.0 ± 31.8726.3 ± 23.5154.7 ± 16.6
    15 to 301.4 ± 0.07.2 \\pm 0.1123.4 ± 8.32.6 ± 0.120.2 ± 0.628.9 ± 2.392.3 ± 29.6741.6 ± 31.5166.1 ± 17.8
    Tailwater pond0 to 151.2 ± 0.17.2 \\pm 0.1148.4 ± 11.12.3 ± 0.219.7 ± 2.228.6 ± 5.180.9 ± 39.8753.6 ± 23.4165.5 ± 25.4
    15 to 301.2 ± 0.17.3 \\pm 0.1163.5 ± 22.32.2 ± 0.217.8 ± 2.326.5 ± 6.058.8 ± 28.0753.0 ± 13.3188.3 ± 30.5
    Ditches0 to 151.3 ± 0.07.3 \\pm 0.1133.7 ± 10.52.5 \\pm 0.220.9 ± 2.044.9 ± 6.4131.5 ± 40.5700.1 ± 28.1168.4 ± 12.9
    15 to 301.5 ± 0.17.3 \\pm 0.1130.0 ± 16.32.9 \\pm 0.219.3 ± 1.638.0 ± 6.5200.5 ± 70.4665.2 ± 58.0134.3 ± 12.9
    ", + "rows": [ + { + "cells": [ + { + "text": "Site", + "is_header": true, + "structural_notes": null + }, + { + "text": "Depth
    (cm)", + "is_header": true, + "structural_notes": null + }, + { + "text": "Bulk
    density
    (g cm-3)", + "is_header": true, + "structural_notes": null + }, + { + "text": "рН", + "is_header": true, + "structural_notes": null + }, + { + "text": "EC (µS cm-1)", + "is_header": true, + "structural_notes": null + }, + { + "text": "Total N
    (Mg ha-1)", + "is_header": true, + "structural_notes": null + }, + { + "text": "Total C
    (Mg ha-1)", + "is_header": true, + "structural_notes": null + }, + { + "text": "Olsen-P
    (mg kg-1)", + "is_header": true, + "structural_notes": null + }, + { + "text": "Sand
    (g kg-1)", + "is_header": true, + "structural_notes": null + }, + { + "text": "Silt
    (g kg-1)", + "is_header": true, + "structural_notes": null + }, + { + "text": "Clay
    (g kg-1)", + "is_header": true, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "South field", + "is_header": false, + "structural_notes": null + }, + { + "text": "0 to 15", + "is_header": false, + "structural_notes": null + }, + { + "text": "1.2 ± 0.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "7.2 ± 0.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "138.4 ± 18.5", + "is_header": false, + "structural_notes": null + }, + { + "text": "2.5 ± 0.1", + "is_header": false, + "structural_notes": null + }, + { + "text": "21.9 ± 1.3", + "is_header": false, + "structural_notes": null + }, + { + "text": "33.7 ± 3.6", + "is_header": false, + "structural_notes": null + }, + { + "text": "148.6 ± 39.3", + "is_header": false, + "structural_notes": null + }, + { + "text": "707.0 ± 21.7", + "is_header": false, + "structural_notes": null + }, + { + "text": "144.5 ± 20.5", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "15 to 30", + "is_header": false, + "structural_notes": null + }, + { + "text": "1.3 \\pm 0.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "7.2 \\pm 0.1", + "is_header": false, + "structural_notes": null + }, + { + "text": "144.7 ± 8.5", + "is_header": false, + "structural_notes": null + }, + { + "text": "2.6 \\pm 0.1", + "is_header": false, + "structural_notes": null + }, + { + "text": "20.5 ± 1.1", + "is_header": false, + "structural_notes": null + }, + { + "text": "28.3 ± 4.5", + "is_header": false, + "structural_notes": null + }, + { + "text": "140.6 ± 48.4", + "is_header": false, + "structural_notes": null + }, + { + "text": "670.9 ± 29.5", + "is_header": false, + "structural_notes": null + }, + { + "text": "188.4 ± 21.0", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "North field", + "is_header": false, + "structural_notes": null + }, + { + "text": "0 to 15", + "is_header": false, + "structural_notes": null + }, + { + "text": "1.3 ± 0.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "7.4 \\pm 0.1", + "is_header": false, + "structural_notes": null + }, + { + "text": "120.2 ± 7.9", + "is_header": false, + "structural_notes": null + }, + { + "text": "2.5 ± 0.1", + "is_header": false, + "structural_notes": null + }, + { + "text": "22.4 ± 1.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "31.4 ± 1.2", + "is_header": false, + "structural_notes": null + }, + { + "text": "119.0 ± 31.8", + "is_header": false, + "structural_notes": null + }, + { + "text": "726.3 ± 23.5", + "is_header": false, + "structural_notes": null + }, + { + "text": "154.7 ± 16.6", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "15 to 30", + "is_header": false, + "structural_notes": null + }, + { + "text": "1.4 ± 0.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "7.2 \\pm 0.1", + "is_header": false, + "structural_notes": null + }, + { + "text": "123.4 ± 8.3", + "is_header": false, + "structural_notes": null + }, + { + "text": "2.6 ± 0.1", + "is_header": false, + "structural_notes": null + }, + { + "text": "20.2 ± 0.6", + "is_header": false, + "structural_notes": null + }, + { + "text": "28.9 ± 2.3", + "is_header": false, + "structural_notes": null + }, + { + "text": "92.3 ± 29.6", + "is_header": false, + "structural_notes": null + }, + { + "text": "741.6 ± 31.5", + "is_header": false, + "structural_notes": null + }, + { + "text": "166.1 ± 17.8", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "Tailwater pond", + "is_header": false, + "structural_notes": null + }, + { + "text": "0 to 15", + "is_header": false, + "structural_notes": null + }, + { + "text": "1.2 ± 0.1", + "is_header": false, + "structural_notes": null + }, + { + "text": "7.2 \\pm 0.1", + "is_header": false, + "structural_notes": null + }, + { + "text": "148.4 ± 11.1", + "is_header": false, + "structural_notes": null + }, + { + "text": "2.3 ± 0.2", + "is_header": false, + "structural_notes": null + }, + { + "text": "19.7 ± 2.2", + "is_header": false, + "structural_notes": null + }, + { + "text": "28.6 ± 5.1", + "is_header": false, + "structural_notes": null + }, + { + "text": "80.9 ± 39.8", + "is_header": false, + "structural_notes": null + }, + { + "text": "753.6 ± 23.4", + "is_header": false, + "structural_notes": null + }, + { + "text": "165.5 ± 25.4", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "15 to 30", + "is_header": false, + "structural_notes": null + }, + { + "text": "1.2 ± 0.1", + "is_header": false, + "structural_notes": null + }, + { + "text": "7.3 \\pm 0.1", + "is_header": false, + "structural_notes": null + }, + { + "text": "163.5 ± 22.3", + "is_header": false, + "structural_notes": null + }, + { + "text": "2.2 ± 0.2", + "is_header": false, + "structural_notes": null + }, + { + "text": "17.8 ± 2.3", + "is_header": false, + "structural_notes": null + }, + { + "text": "26.5 ± 6.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "58.8 ± 28.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "753.0 ± 13.3", + "is_header": false, + "structural_notes": null + }, + { + "text": "188.3 ± 30.5", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "Ditches", + "is_header": false, + "structural_notes": null + }, + { + "text": "0 to 15", + "is_header": false, + "structural_notes": null + }, + { + "text": "1.3 ± 0.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "7.3 \\pm 0.1", + "is_header": false, + "structural_notes": null + }, + { + "text": "133.7 ± 10.5", + "is_header": false, + "structural_notes": null + }, + { + "text": "2.5 \\pm 0.2", + "is_header": false, + "structural_notes": null + }, + { + "text": "20.9 ± 2.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "44.9 ± 6.4", + "is_header": false, + "structural_notes": null + }, + { + "text": "131.5 ± 40.5", + "is_header": false, + "structural_notes": null + }, + { + "text": "700.1 ± 28.1", + "is_header": false, + "structural_notes": null + }, + { + "text": "168.4 ± 12.9", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "15 to 30", + "is_header": false, + "structural_notes": null + }, + { + "text": "1.5 ± 0.1", + "is_header": false, + "structural_notes": null + }, + { + "text": "7.3 \\pm 0.1", + "is_header": false, + "structural_notes": null + }, + { + "text": "130.0 ± 16.3", + "is_header": false, + "structural_notes": null + }, + { + "text": "2.9 \\pm 0.2", + "is_header": false, + "structural_notes": null + }, + { + "text": "19.3 ± 1.6", + "is_header": false, + "structural_notes": null + }, + { + "text": "38.0 ± 6.5", + "is_header": false, + "structural_notes": null + }, + { + "text": "200.5 ± 70.4", + "is_header": false, + "structural_notes": null + }, + { + "text": "665.2 ± 58.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "134.3 ± 12.9", + "is_header": false, + "structural_notes": null + } + ] + } + ], + "cells": [], + "footnote_ids": [] + }, + { + "kind": "paragraph", + "id": "doc:c89f0ddfae5e8934", + "text": "

    Statistical Analysis. Concentrations and corre

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    loads from water sampling were log transformed and checked for assumptions of normality with the Shapiro-Wilk test and equality of variance with the Levene test using the open source statistical package R version 2.11.1. (Helsel and Hirsch 1993). Means of each rainfall or irrigation event were considered replications and were compared through the entire season for each constituent. If assumptions of normality were met, a paired t-test was performed for either equal or unequal variances to test for treatment differences for concentration and load of each constituent for each season (Helsel and Hirsch 1993). For constituents that did not meet assumptions of normality and equality of variance, a Wilcoxon signed-rank test was used to test for treatment differences (Helsel and Hirsch 1993).

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    To account for the differences in relative size of the fields, ditches, and tailwater ponds and distances between plots, a mixed model analysis of variance (ANOVA) was employed that incorporated a spatial covariance structure (Casanoves et al. 2005). The ANOVA tests that were significant were followed by Tukey's Honestly Significant Post Hoc Test (Zar 1974). Briefly, the mixed linear models were run after checking assumptions using the proc mixed statement in SAS version 9.3.1 (SAS Institute, Cary, North Carolina) combined with a power correlation function (POW model), which enables X,Y global positioning system coordinates to be used as a covariate. The POW model uses a onedimensional isotropic (same in all directions) power covariance, based in this case, on geographic information system coordinates and assumes no correlation between plots and homogeneous residual variances (Self and Liang 1987; Wolfinger 1993). The power

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    correlation model is represented as \\rho_{u}^{dxij} \\rho_{u}^{dyij}, where d^{xij} and d^{yij} are the distances between plot i and plot j in the x and y directions and \\rho_{y} and \\rho_{y} are the unknown correlation parameters in the x and y directions (Casanoves et al. 2005). The degrees of freedom were adjusted as suggested by Kenward and Roger (1997). This methodology has been utilized and tested against other spatial and nonspatial models in agricultural systems and has been shown to be an effective means of dealing with spatial covariance (Bajwa and Mozaffari 2007; Bajwa and Vories 2007; Casanoves et al. 2005; Goncalves et al. 2007). The model, however, is unable to simultaneously account for repeated measurements; therefore, means were compared for each site without adjustment for variation over time.

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    Results and Discussion

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    Soil Properties. Soil properties of the four locations were similar across the farm sampling sites (table 2). All soils had a silt loam texture, total carbon (C) ranged from 19.7 to 22.4 Mg ha<sup>-1</sup> (8.7 to 10.0 tn ac<sup>-1</sup>), and pH ranged from 7.2 to 7.4 at 0 to 15 cm (0 to 5.9 in) depth (table 2). The consistency of soil properties between sites indicates that a similar soil type occurred across the farm. Thus soil properties likely did not confound the analysis of the environmental outcomes of the BMPs.

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    Runoff. Winter cover cropping improved the water quality of stormwater runoff during Rainfed Y1, but in rainfed year two (Rainfed Y2), no runoff was detected due to low rainfall. Compared to the fallow, water quality constituents in winter runoff (Rainfed Y1) were lower in cover cropped fields: 44% lower for EC and 80% lower for TSS (mg L<sup>-1</sup> [ppm]) (table 3). Phosphorus as DRP (mg L<sup>-1</sup> [ppm]), however, was 86%

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    higher in discharge water from the covercropped field compared to the fallow. Higher concentrations of DRP may be a result of increased mobilization from the mustard cover crop. Other Brassica species have been shown to increase phosphorus availability through increased citric and malic acid in the rhizosphere (Eichler-Lobermann et al. 2008; Hoffland et al. 1992; Marschner et al. 2007).

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    Sediment and nutrient loads were calculated for the stormwater runoff based on mean discharge for the five winter storm events (table 4). In Rainfed Y1, total discharge loads (kg ha<sup>-1</sup> [lb ac<sup>-1</sup>]) were lower for cover cropped than fallow fields: 83% lower for TSS, 33% lower for NH<sub>4</sub><sup>+</sup>-N, and 58% for DOC. Despite the large quantity of C in the cover crop biomass, there was no increase in DOC in runoff in either winter storm events or in the subsequent irrigation. Low DOC in runoff following the cover crop indicates gradual decomposition and possibly leaching losses.

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    During Irrigated Y1, a total of 944 mm (37.2 in) of water (figure 2) was applied on the entire farm in 10 events, 35% of which discharged into the sediment trap (tomatoes discharge) (table 4). In Irrigated Y2, a mean total of 799 mm (31.5 in) of water was applied (figure 2) to the two North Field sections in 9 events, 25% of which discharged from the field section that had a prior mustard cover crop during the winter (tomatoes/ mustard) and 42% discharging from the field section that had been fallow (tomatoes/fallow). Mean irrigation discharge rates for the tomatoes/mustard and tomatoes/fallow rotations were not statistically different. Nor were there any differences in the concentrations or loads of measured constituents, except for pH, which was significantly higher in the discharge from the tomatoes/mustard field during the summer season.

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    Table 3

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    Mean concentration and standard errors of constituents analyzed from influent and discharge effluent from entire fields and paired sections of the North Field (F = fallow and M = mustard cover crop) during the two-year study by season. Means are given for the total number (n) of either irrigation or rainfall events. Discharge was not detected (ND) during the rainfed season in the second year due to unusually low precipitation. Measured constituents are pH, electrical conductivity (EC), total suspended solids (TSS), volatile suspended solids (VSS), nitrate (NO_3^--N), ammonium (NH_4^+-N), dissolved reactive phosphorus (DRP), and dissolved organic carbon (DOC).

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    SeasonTreatmentnрНEC
    (μS cm-1)    		TSS
    (g L-1)    		VSS
    (g L-1)    		NO3 N
    (mg L-1)    		NH4+-N
    (mg L-1)    		DRP
    (mg L-1)    		DOC
    (mg L-1)
    Irrigated Y1Irrigation influent107.9 ± 0.1795.9 ± 46.20.04 ± 0.010.015 ± 0.001.7 ± 0.30.1 ± 0.00.1 \\pm 0.02.3 ± 0.5
    (South Field)Tomatoes discharge107.7 \\pm 0.1834.6 ± 33.87.27 ± 1.030.254 ± 0.051.6 ± 0.20.1 \\pm 0.00.5 \\pm 0.13.0 \\pm 0.4
    Rainfed Y1Fallow storm discharge56.7 ± 0.0115.1 ± 33.6**0.07 ± 0.01*0.002 ± 0.000.1 ± 0.00.1 ± 0.00.2 ± 0.0†7.4 ± 0.0
    (North Field)Mustard storm discharge56.7 \\pm 0.164.5 ± 16.7**0.01 ± 0.00*0.006 \\pm 0.000.1 \\pm 0.00.1 \\pm 0.00.4 ± 0.1†5.8 \\pm 0.1
    Irrigated Y2Irrigation influent97.9 ± 0.1600.0 ± 18.20.02 ± 0.010.017 ± 0.001.8 ± 0.30.1 ± 0.00.0 ± 0.01.9 ± 0.4
    (North Field)Tomatoes (F) discharge98.1 ± 0.1*644.0 ± 22.710.90 ± 3.850.259 ± 0.092.2 \\pm 0.20.2 \\pm 0.10.3 \\pm 0.03.9 \\pm 0.9
    Tomatoes (M) discharge98.3 ± 0.0*611.4 ± 29.83.99 \\pm 0.930.271 \\pm 0.151.6 \\pm 0.30.1 \\pm 0.00.3 \\pm 0.03.3 \\pm 0.4
    Rainfed Y2Oats discharge0NDNDNDNDNDNDNDND
    (South/North)Fallow discharge0NDNDNDNDNDNDNDND
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    (μS cm-1)", + "is_header": true, + "structural_notes": null + }, + { + "text": "TSS
    (g L-1)", + "is_header": true, + "structural_notes": null + }, + { + "text": "VSS
    (g L-1)", + "is_header": true, + "structural_notes": null + }, + { + "text": "NO3 N
    (mg L-1)", + "is_header": true, + "structural_notes": null + }, + { + "text": "NH4+-N
    (mg L-1)", + "is_header": true, + "structural_notes": null + }, + { + "text": "DRP
    (mg L-1)", + "is_header": true, + "structural_notes": null + }, + { + "text": "DOC
    (mg L-1)", + "is_header": true, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "Irrigated Y1", + "is_header": false, + "structural_notes": null + }, + { + "text": "Irrigation influent", + "is_header": false, + "structural_notes": null + }, + { + "text": "10", + "is_header": false, + "structural_notes": null + }, + { + "text": "7.9 ± 0.1", + "is_header": false, + "structural_notes": null + }, + { + "text": "795.9 ± 46.2", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.04 ± 0.01", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.015 ± 0.00", + "is_header": false, + "structural_notes": null + }, + { + "text": "1.7 ± 0.3", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.1 ± 0.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.1 \\pm 0.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "2.3 ± 0.5", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "(South Field)", + "is_header": false, + "structural_notes": null + }, + { + "text": "Tomatoes discharge", + "is_header": false, + "structural_notes": null + }, + { + "text": "10", + "is_header": false, + "structural_notes": null + }, + { + "text": "7.7 \\pm 0.1", + "is_header": false, + "structural_notes": null + }, + { + "text": "834.6 ± 33.8", + "is_header": false, + "structural_notes": null + }, + { + "text": "7.27 ± 1.03", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.254 ± 0.05", + "is_header": false, + "structural_notes": null + }, + { + "text": "1.6 ± 0.2", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.1 \\pm 0.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.5 \\pm 0.1", + "is_header": false, + "structural_notes": null + }, + { + "text": "3.0 \\pm 0.4", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "Rainfed Y1", + "is_header": false, + "structural_notes": null + }, + { + "text": "Fallow storm discharge", + "is_header": false, + "structural_notes": null + }, + { + "text": "5", + "is_header": false, + "structural_notes": null + }, + { + "text": "6.7 ± 0.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "115.1 ± 33.6**", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.07 ± 0.01*", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.002 ± 0.00", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.1 ± 0.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.1 ± 0.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.2 ± 0.0†", + "is_header": false, + "structural_notes": null + }, + { + "text": "7.4 ± 0.0", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "(North Field)", + "is_header": false, + "structural_notes": null + }, + { + "text": "Mustard storm discharge", + "is_header": false, + "structural_notes": null + }, + { + "text": "5", + "is_header": false, + "structural_notes": null + }, + { + "text": "6.7 \\pm 0.1", + "is_header": false, + "structural_notes": null + }, + { + "text": "64.5 ± 16.7**", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.01 ± 0.00*", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.006 \\pm 0.00", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.1 \\pm 0.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.1 \\pm 0.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.4 ± 0.1†", + "is_header": false, + "structural_notes": null + }, + { + "text": "5.8 \\pm 0.1", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "Irrigated Y2", + "is_header": false, + "structural_notes": null + }, + { + "text": "Irrigation influent", + "is_header": false, + "structural_notes": null + }, + { + "text": "9", + "is_header": false, + "structural_notes": null + }, + { + "text": "7.9 ± 0.1", + "is_header": false, + "structural_notes": null + }, + { + "text": "600.0 ± 18.2", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.02 ± 0.01", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.017 ± 0.00", + "is_header": false, + "structural_notes": null + }, + { + "text": "1.8 ± 0.3", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.1 ± 0.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.0 ± 0.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "1.9 ± 0.4", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "(North Field)", + "is_header": false, + "structural_notes": null + }, + { + "text": "Tomatoes (F) discharge", + "is_header": false, + "structural_notes": null + }, + { + "text": "9", + "is_header": false, + "structural_notes": null + }, + { + "text": "8.1 ± 0.1*", + "is_header": false, + "structural_notes": null + }, + { + "text": "644.0 ± 22.7", + "is_header": false, + "structural_notes": null + }, + { + "text": "10.90 ± 3.85", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.259 ± 0.09", + "is_header": false, + "structural_notes": null + }, + { + "text": "2.2 \\pm 0.2", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.2 \\pm 0.1", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.3 \\pm 0.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "3.9 \\pm 0.9", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "Tomatoes (M) discharge", + "is_header": false, + "structural_notes": null + }, + { + "text": "9", + "is_header": false, + "structural_notes": null + }, + { + "text": "8.3 ± 0.0*", + "is_header": false, + "structural_notes": null + }, + { + "text": "611.4 ± 29.8", + "is_header": false, + "structural_notes": null + }, + { + "text": "3.99 \\pm 0.93", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.271 \\pm 0.15", + "is_header": false, + "structural_notes": null + }, + { + "text": "1.6 \\pm 0.3", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.1 \\pm 0.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.3 \\pm 0.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "3.3 \\pm 0.4", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "Rainfed Y2", + "is_header": false, + "structural_notes": null + }, + { + "text": "Oats discharge", + "is_header": false, + "structural_notes": null + }, + { + "text": "0", + "is_header": false, + "structural_notes": null + }, + { + "text": "ND", + "is_header": false, + "structural_notes": null + }, 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null + }, + { + "text": "ND", + "is_header": false, + "structural_notes": null + }, + { + "text": "ND", + "is_header": false, + "structural_notes": null + }, + { + "text": "ND", + "is_header": false, + "structural_notes": null + }, + { + "text": "ND", + "is_header": false, + "structural_notes": null + }, + { + "text": "ND", + "is_header": false, + "structural_notes": null + } + ] + } + ], + "cells": [], + "footnote_ids": [ + "doc:94e1795c0f266a5a", + "doc:12a9147a93f05ccd" + ] + }, + { + "kind": "paragraph", + "id": "doc:667e465d8fc31f08", + "text": "

    Note: Significant difference were calculated using a paired t-test.

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    Table 4

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    Loads of constituents analyzed from the paired tomato fields (F = fallow and M = mustard cover crop), oat field, and tailwater pond during the two-year study by season. Loads are calculated from mean concentrations weighted by flow rates divided by the area from which the water discharged. Mean loads and standard errors are given as an event mean, where n is the total number of either irrigation or rainfall events. Discharge was not detected (ND) during the rainfed season in the second year (Rainfed Y2) due to unusually low precipitation. Measured constituents are total suspended solids (TSS), volatile suspended solids (VSS), nitrate (NO_3^--N), ammonium (NH_4^+-N), dissolved reactive phosphorus (DRP), and dissolved organic carbon (DOC).

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    TreatmentnVolume
    (mm
    event-1)    		TSS
    (kg ha-1
    event-1)    		VSS
    (kg ha-1
    event-1)    		NO3--N
    (kg ha-1
    event-1)    		NH4+-N
    (g ha-1
    event-1)    		DRP
    (g ha-1
    event-1)    		DOC
    (kg ha-1
    event-1)
    Irrigated Y1 (South Field)orone ,orone ,ovone ,ovoiic yorone ,ovone ,- Overley
    Irrigation influent1094.4 ± 15.7408.4 ± 161.313.9 ± 3.01.7 ± 0.492.8 ± 37.022.4 ± 6.21.9 ± 0.7
    Tomatoes discharge1033.2 ± 6.32,384.6 ± 81.081.5 ± 17.40.6 \\pm 0.228.0 ± 14.470.0 ± 20.00.9 \\pm 0.2
    Rainfed Y1 (North Field)
    Fallow discharge59.6 ± 3.35.0 ± 1.3*0.2 ± 0.10.01 ± 0.018.3 ± 3.2*22.3 ± 7.60.7 ± 0.2**
    Mustard discharge55.4 ± 1.80.9 ± 0.4*0.5 \\pm 0.20.01 ± 0.045.6 ± 2.7*19.7 ± 5.30.3 ± .1**
    Tailwater pond discharge5NDNDNDNDNDNDND
    Irrigated Y2 (North Field)
    Irrigation influent988.8 ± 22.014.4 ± 7.919.0 ± 9.01.8 ± 0.535.2 ± 14.635.4 ± 14.91.5 ± 0.4
    Tomatoes (F) discharge937.9 ± 9.42,440.5 ± 849.293.6 ± 36.80.9 \\pm 0.322.6 ± 8.0118.4 ± 42.01.0 \\pm 0.2
    Tomatoes (M) discharge923.1 ± 3.1902.3 ± 211.168.1 ± 39.10.4 \\pm 0.116.3 ± 5.360.8 ± 10.50.7 \\pm 0.1
    Tailwater pond influent932.3 \\pm 9.71,046.1 ± 443.2**51.8 ± 23.1**0.6 \\pm 0.321.1 ± 9.0134.4 ± 49.61.0 \\pm 0.3
    Tailwater pond discharge932.3 ± 6.730.7 ± 11.3**4.4 ± 2.3**0.7 \\pm 0.250.9 ± 30.8117.3 ± 32.21.4 ± 0.4
    Rainfed Y2 (South/North Field)
    Oats discharge0NDNDNDNDNDNDND
    Fallow discharge0NDNDNDNDNDNDND
    Tailwater pond discharge0NDNDNDNDNDNDND
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+ { + "text": "94.4 ± 15.7", + "is_header": false, + "structural_notes": null + }, + { + "text": "408.4 ± 161.3", + "is_header": false, + "structural_notes": null + }, + { + "text": "13.9 ± 3.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "1.7 ± 0.4", + "is_header": false, + "structural_notes": null + }, + { + "text": "92.8 ± 37.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "22.4 ± 6.2", + "is_header": false, + "structural_notes": null + }, + { + "text": "1.9 ± 0.7", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "Tomatoes discharge", + "is_header": false, + "structural_notes": null + }, + { + "text": "10", + "is_header": false, + "structural_notes": null + }, + { + "text": "33.2 ± 6.3", + "is_header": false, + "structural_notes": null + }, + { + "text": "2,384.6 ± 81.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "81.5 ± 17.4", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.6 \\pm 0.2", + "is_header": false, + "structural_notes": null + }, + { + "text": "28.0 ± 14.4", + "is_header": false, + "structural_notes": null + }, + { + "text": "70.0 ± 20.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.9 \\pm 0.2", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "Rainfed Y1 (North Field)", + "is_header": false, + "structural_notes": null + }, + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "Fallow discharge", + "is_header": false, + "structural_notes": null + }, + { + "text": "5", + "is_header": false, + "structural_notes": null + }, + { + "text": "9.6 ± 3.3", + "is_header": false, + "structural_notes": null + }, + { + "text": "5.0 ± 1.3*", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.2 ± 0.1", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.01 ± 0.01", + "is_header": false, + "structural_notes": null + }, + { + "text": "8.3 ± 3.2*", + "is_header": false, + "structural_notes": null + }, + { + "text": "22.3 ± 7.6", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.7 ± 0.2**", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "Mustard discharge", + "is_header": false, + "structural_notes": null + }, + { + "text": "5", + "is_header": false, + "structural_notes": null + }, + { + "text": "5.4 ± 1.8", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.9 ± 0.4*", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.5 \\pm 0.2", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.01 ± 0.04", + "is_header": false, + "structural_notes": null + }, + { + "text": "5.6 ± 2.7*", + "is_header": false, + "structural_notes": null + }, + { + "text": "19.7 ± 5.3", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.3 ± .1**", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "Tailwater pond discharge", + "is_header": false, + "structural_notes": null + }, + { + "text": "5", + "is_header": false, + "structural_notes": null + }, + { + "text": "ND", + "is_header": false, + "structural_notes": null + }, + { + "text": "ND", + "is_header": false, + "structural_notes": null + }, + { + "text": "ND", + "is_header": false, + "structural_notes": null + }, + { + "text": "ND", + "is_header": false, + "structural_notes": null + }, + { + "text": "ND", + "is_header": false, + "structural_notes": null + }, + { + "text": "ND", + "is_header": false, + "structural_notes": null + }, + { + "text": "ND", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "Irrigated Y2 (North Field)", + "is_header": false, + "structural_notes": null + }, + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "Irrigation influent", + "is_header": false, + "structural_notes": null + }, + { + "text": "9", + "is_header": false, + "structural_notes": null + }, + { + "text": "88.8 ± 22.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "14.4 ± 7.9", + "is_header": false, + "structural_notes": null + }, + { + "text": "19.0 ± 9.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "1.8 ± 0.5", + "is_header": false, + "structural_notes": null + }, + { + "text": "35.2 ± 14.6", + "is_header": false, + "structural_notes": null + }, + { + "text": "35.4 ± 14.9", + "is_header": false, + "structural_notes": null + }, + { + "text": "1.5 ± 0.4", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "Tomatoes (F) discharge", + "is_header": false, + "structural_notes": null + }, + { + "text": "9", + "is_header": false, + "structural_notes": null + }, + { + "text": "37.9 ± 9.4", + "is_header": false, + "structural_notes": null + }, + { + "text": "2,440.5 ± 849.2", + "is_header": false, + "structural_notes": null + }, + { + "text": "93.6 ± 36.8", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.9 \\pm 0.3", + "is_header": false, + "structural_notes": null + }, + { + "text": "22.6 ± 8.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "118.4 ± 42.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "1.0 \\pm 0.2", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "Tomatoes (M) discharge", + "is_header": false, + "structural_notes": null + }, + { + "text": "9", + "is_header": false, + "structural_notes": null + }, + { + "text": "23.1 ± 3.1", + "is_header": false, + "structural_notes": null + }, + { + "text": "902.3 ± 211.1", + "is_header": false, + "structural_notes": null + }, + { + "text": "68.1 ± 39.1", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.4 \\pm 0.1", + "is_header": false, + "structural_notes": null + }, + { + "text": "16.3 ± 5.3", + "is_header": false, + "structural_notes": null + }, + { + "text": "60.8 ± 10.5", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.7 \\pm 0.1", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "Tailwater pond influent", + "is_header": false, + "structural_notes": null + }, + { + "text": "9", + "is_header": false, + "structural_notes": null + }, + { + "text": "32.3 \\pm 9.7", + "is_header": false, + "structural_notes": null + }, + { + "text": "1,046.1 ± 443.2**", + "is_header": false, + "structural_notes": null + }, + { + "text": "51.8 ± 23.1**", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.6 \\pm 0.3", + "is_header": false, + "structural_notes": null + }, + { + "text": "21.1 ± 9.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "134.4 ± 49.6", + "is_header": false, + "structural_notes": null + }, + { + "text": "1.0 \\pm 0.3", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "Tailwater pond discharge", + "is_header": false, + "structural_notes": null + }, + { + "text": "9", + "is_header": false, + "structural_notes": null + }, + { + "text": "32.3 ± 6.7", + "is_header": false, + "structural_notes": null + }, + { + "text": "30.7 ± 11.3**", + "is_header": false, + "structural_notes": null + }, + { + "text": "4.4 ± 2.3**", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.7 \\pm 0.2", + "is_header": false, + "structural_notes": null + }, + { + "text": "50.9 ± 30.8", + "is_header": false, + "structural_notes": null + }, + { + "text": "117.3 ± 32.2", + "is_header": false, + "structural_notes": null + }, + { + "text": "1.4 ± 0.4", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "Rainfed Y2 (South/North Fie", + "is_header": false, + "structural_notes": null + }, + { + "text": "ld)", + "is_header": false, + "structural_notes": null + }, + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "Oats discharge", + "is_header": false, + "structural_notes": null + }, + { + "text": "0", + "is_header": false, + "structural_notes": null + }, + { + "text": "ND", + "is_header": false, + "structural_notes": null + }, + { + "text": "ND", + "is_header": false, + "structural_notes": null + }, + { + "text": "ND", + "is_header": false, + "structural_notes": null + }, + { + "text": "ND", + "is_header": false, + "structural_notes": null + }, + { + "text": "ND", + "is_header": false, + "structural_notes": null + }, + { + "text": "ND", + "is_header": false, + "structural_notes": null + }, + { + "text": "ND", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "Fallow discharge", + "is_header": false, + "structural_notes": null + }, + { + "text": "0", + "is_header": false, + "structural_notes": null + }, + { + "text": "ND", + "is_header": false, + "structural_notes": null + }, + { + "text": "ND", + "is_header": false, + "structural_notes": null + }, + { + "text": "ND", + "is_header": false, + "structural_notes": null + }, + { + "text": "ND", + "is_header": false, + "structural_notes": null + }, + { + "text": "ND", + "is_header": false, + "structural_notes": null + }, + { + "text": "ND", + "is_header": false, + "structural_notes": null + }, + { + "text": "ND", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "Tailwater pond discharge", + "is_header": false, + "structural_notes": null + }, + { + "text": "0", + "is_header": false, + "structural_notes": null + }, + { + "text": "ND", + "is_header": false, + "structural_notes": null + }, + { + "text": "ND", + "is_header": false, + "structural_notes": null + }, + { + "text": "ND", + "is_header": false, + "structural_notes": null + }, + { + "text": "ND", + "is_header": false, + "structural_notes": null + }, + { + "text": "ND", + "is_header": false, + "structural_notes": null + }, + { + "text": "ND", + "is_header": false, + "structural_notes": null + }, + { + "text": "ND", + "is_header": false, + "structural_notes": null + } + ] + } + ], + "cells": [], + "footnote_ids": [ + "doc:7b5ab79982579ec1" + ] + }, + { + "kind": "paragraph", + "id": "doc:5d2a9aa82c65f1d5", + "text": "

    Note: Significant difference were calculated using a paired t-test.

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    † Wilcoxon signed ranked test p < 0.05.

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    * p < 0.05 ** p < 0.01

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    * p <0.05 ** p < 0.001

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    Figure 3

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    A sediment trap was monitored in 2005 (Irrigated Y1) and a tailwater pond was monitored in 2006 (Irrigated Y2) for influent (discharge from the agricultural fields) and effluent (water leaving ponds) concentrations: (a and b) pH, (c and d) total suspended solids (TSS), (e and f) volatile suspended solids (VSS), (g and h) nitrate (N0_3-N), and (i and j) dissolved organic carbon (DOC). In 2005, only the sediment trap was sampled, as discharge from the South Field tailwater pond was inaccessible due to its transfer through a subsurface pump to the top of the field. Means and standard errors of constituents with significant differences (paired t-test) are shown (*p < 0.01, **p < 0.001).

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    Sediment concentrations (figure 3) and loads (table 4) in the irrigation water discharging from the fields were effectively reduced by the sediment trap in irrigated Y1 and tailwater pond in irrigated Y2. Removal efficiencies by just the sediment trap for TSS and volatile suspended solids (VSS) on the South Field in Irrigated Y1 were 71% and 54%, respectively. Even greater reductions resulted from the tailwater pond in the North Field in Irrigated Y2, 97% and 89%, respectively. The total outflow (tailwater pond discharge table 4) load of TSS (kg ha<sup>-1</sup> [lb ac<sup>-1</sup>]) was on average 35-fold lower than inflow loads (tailwater pond influent table 4) for the nine irrigation events, a reduction for the entire season of 9.4 Mg ha<sup>-1</sup> (4.1 tn ac<sup>-1</sup>). Loads for VSS were on average 12-fold lower than the influent (i.e., a total of 431 kg ha<sup>-1</sup> [384 lb ac<sup>-1</sup>] difference for the season).

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    These sediment losses via runoff were relatively high on this farm even though the amount of irrigation discharge was not. Irrigation runoff was only, on average, 34% of the applied irrigation. This was considerably lower than found in a comprehensive study of 49 furrow-irrigated farms on silt loam soils in Idaho, where discharge ranged from 43% to 53% of the total applied irrigation (Berg and Carter 1980). Although the Idaho farms were on similar slopes (1% to 2%), sediment losses averaged only 5 Mg ha<sup>-1</sup> season<sup>-1</sup> (2.2 tn ac<sup>-1</sup> season<sup>-1</sup>), more than four times lower than observed here (24 Mg ha<sup>-1</sup> [10.7 tn ac^{-1}] in the first irrigation seasons). In the Idaho study, losses increased to 37 Mg ha<sup>-1</sup> (16.5 tn ac<sup>-1</sup>) on slopes of 2.5% and up to 141 Mg ha^{-1} (62.8 tn ac^{-1}) on 4% slopes. Differences in tillage practices, soil type, the amount of surface residue, or higher irrigation inflow rates could explain the disparity in sediment losses between these studies. Much greater inflow rates, for example, were likely required to move water down the long furrow lengths (390 to 790 m [1,280 to 2,592 ft]) of this farm compared to the shorter furrow lengths 132 to 313 m (433 to 1,026 ft) of the farms observed in the Idaho study.

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    Given the substantial loads due to the irrigation practices on this farm, large quantities of sediment would have been lost to the neighboring waterways despite the use of winter cover crops. Sediment loads in irrigation runoff were orders of magnitude higher than those of winter runoff. These results suggest that a tailwater pond would be a critical BMP addition to protect water quality.

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    The tailwater pond alone, however, did not completely prevent nutrient runoff into the nearby waterways. Comparing the water quality indicators in irrigation discharged from the field to water exiting the tailwater pond, we observed no differences in EC, NH_4^+-N, or DRP. In fact, NO_3^--N and DOC concentrations actually increased by 40% and 20%, respectively. This increase in NO<sub>3</sub>-N is somewhat surprising given that N removal rates from polluted water are thought, in principle, to be proportional to concentration (Kadlec and Knight 1996). It is possible that this might be a result of mineralization and nitrification of organic N deposited in the pond, given the low concentrations of NO<sub>3</sub>-N in the irrigation runoff. Summer NO -N losses via runoff were only 8.1 and 3.3 kg ha^{-1} y^{-1} (7.2 and 2.9 lb ac^{-1} yr^{-1}) for tomatoes grown following winter fallow and mustard cover crops, respectively, compared to, for example, a heavily manured grazed dairy pasture with annual losses of 92 kg NO<sub>3</sub>-N ha<sup>-1</sup> (81.9 lb NO<sub>3</sub>-N ac<sup>-1</sup>) (Tanner et al. 2005). Overall NO<sub>3</sub>-N concentrations in runoff were far lower (<2.2 mg NO, -N L<sup>-1</sup> [ppm NO, -N]) than the World Health Organization drinking water standard of 10 mg NO<sub>3</sub>--N L<sup>-1</sup> (ppm NO<sub>3</sub>--N) (Yamamura et al. 2004). Removal rates for NO<sub>3</sub>-N in constructed wetlands can be as high as 44%, but these rates have been associated with much higher concentrations in effluent from dairy pastures (Tanner et al. 2005). The design of the tailwater pond (Kadlec 2005) and its effects on denitrification, sedimentation (Saunders and Kalff 2001), and hydraulic efficiency, may have limited the potential for microbial and plant N immobilization, due to short residence times in these ponds and their small sizes (0.2% of the field's watershed area). Hydraulic residence time, the average time that water remains in the pond, expressed as mean volume divided by mean outflow rate in the tailwater pond was less than two days, whereas recommended hydraulic residence times are as long as 12 days for treatment wetlands (Davis 1994). Submergent and emergent vegetation can also contribute to increased removal rates of some constituents in other types of ponds (Kadlec 2005), but the ruderal plant species along the bottom and the edge of these tailwater ponds were not adapted to long-term inundation.

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    Concentrations of DRP in the effluent were also not reduced by detention in the tailwater pond. In tailwater ponds, phospho-

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    rus that is sorbed to crystalline and poorly crystalline iron hydroxides may be released at low redox potentials (Bjorneberg et al. 2002). In fact, Tanner et al. (2005) found that DRP increased by 70% after flowing through a constructed treatment pond. Although we did not measure total phosphorus, if we assume that eroded sediments contain approximately 0.1% total phosphorus (Bjorneberg et al. 2002), the tailwater pond could have trapped as much as 24 kg phosphorus ha<sup>-1</sup> (21.4 lb phosphorus ac<sup>-1</sup>) during a single irrigation season given the mass of TSS that was retained.

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    Without a tailwater return system, improving the effectiveness of a tailwater pond through increased detention time or greater plant cover may require tough decisions as to which pollutants should be managed. The short detention time may not have been long enough to adequately decrease NO, -N and DRP, but it appears to have been sufficient for microbes to break down and dissolve particulate C into solution, thereby increasing the DOC concentration in the effluent. Increasing the retention times by expanding the size of the tailwater pond or slowing the flow through baffles may reduce eutrophication. But it may not decrease DOC, which can cause toxic byproducts during the municipal drinking water treatment process used in this region (Fujii et al. 1998). Additionally increasing the size of the pond requires added investment and reduces the area of tomato production.

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    Leaching of Nitrate. Leaching of NO<sub>3</sub>-N, as measured by the anion exchange resin bags was similar (approximately 24.7 kg NO, -N ha-1 [22 lb NO, -N ac-1]) for the fields, ditches, and tailwater ponds in Rainfed Y1 (figure 4). There were no differences observed between the winter cover crop and fallow fields. Interestingly, in the Rainfed Y2 season, despite the low rainfall, NO3-N in the resin bags was two-fold higher in the tailwater pond and three-fold higher in ditches than in the fields. The higher NO<sub>2</sub>-N leaching during the drier Rainfed Y2 season in these areas that accumulate water was likely due to slightly higher moisture content and enhanced nitrification compared to the fields, particularly during the warm fall and spring (Burger and Jackson 2003; Stark and Firestone 1995). In addition, there was little to no nutrient removal via runoff, and absence of anaerobic conditions would have

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    limited denitrification, causing inorganic N to accumulate in the surface soil.

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    Significant differences in NO<sub>3</sub>-N concentrations in lysimeters varied among fields, ditches, and the tailwater pond by season and by depth (table 5), but differences in cumulative losses were found only in Rainfed Y1 (figure 4). Estimated DP, used to calculate cumulative leaching losses, was 157 mm (6.2 in) during the Irrigated Y1 season with tomatoes. During the Rainfed Y1 season, DP ranged from 92 to 112 mm (3.62 to 4.41 in). In the Irrigated Y2 season, DP ranged from 169 mm (6.7 in) for the tomatoes/fallow compared to 326 mm (12.8 in) for the tomatoes/mustard treatment. No DP could be estimated for the Rainfed Y2 season due to low rainfall. Estimates of NO<sub>3</sub>-N leaching using lysimeter concentrations and the water balance method were lower than those obtained using the anion exchange resin bags, especially during the Irrigated Y1 season (figure 4). Others however, have shown the opposite, with higher values in lysimeters than resin bags (Wyland et al. 1996). Accurately assessing soil solute chemistry is often dependent on matching the methodology with the soil type (Siemens and Kaupenjohann 2004). Here, the collection schedule did not always match with the irrigation schedule, and some wet-dry cycles were inadvertently missed, probably underestimating cumulative losses using the lysimeter method. Furthermore, cumulative losses calculated based on estimates of water percolation are subject to error if the return to field capacity is not correctly identified (Webster et al. 1993).

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    Leaching of Other Constituents. In soil solution collected in the lysimeters, pH and EC were very similar and consistent among treatments throughout the two years. The only difference was higher EC values in soil solution for lysimeters at 30 cm (12 in) in the tomato field and ditches, compared to the tailwater pond in the Irrigated Y1 season (table 5).

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    Leachate NH<sub>4</sub><sup>+</sup>-N and calculated NH<sub>4</sub><sup>+</sup>-N load were low overall (data not shown). Seasonal cumulative DRP leached below the 60 cm (23.6 in) depth also showed the same pattern. Concentrations of DOC tended to be lowest in the lysimeters in the tailwater pond (table 5). Results indicate that cover cropping may be a source of DOC during the winter rains. Dissolved organic C concentrations in RainfedY1 were clearly

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    Table 5 Mean lysimeter concentrations and standard errors by season and depth during the two-year study for the paired tomato fields (F= fallow and M = mustard cover crop). Lysimeters were sampled weekly during irrigation and precipitation periods.

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    SeasonSiteDepth
    (cm)    		pHEC
    (µS cm–1)    		NO3
    ––N
    (mg L–1)    		DRP
    (mg L–1)    		DOC
    (mg L–1)
    Irrigated Y1Oats*NDNDNDNDND
    Tomatoes307.4±0.1904.6 ± 33.1A5.0 ± 1.2A0.1 ± 0.05.2 ± 0.7AB
    607.5±0.0894.3 ± 44.73.4 ± 1.00.0 ± 0.04.9 ± 0.9b
    Ditches307.4±0.01,069.0 ± 14.0A1.1 ± 0.4B0.7 ± 0.513.7 ± 8.6A
    607.3±0.21,039.7 ± 216.92.6 ± 1.70.1 ± 0.122.5 ± 7.1a
    Tailwater pond307.4±0.2519.0 ± 196.0B1.6 ± 0.7B0.0 ± 0.03.8 ± 2.1B
    607.1±0.0615.5 ± 40.60.9 ± 0.20.0 ± 0.02.6 ± 1.1b
    Rainfed Y1Oats307.0±0.1905.8 ± 172.86.1 ± 1.9AB0.1 ± 0.113.3 ± 2.5B
    607.1±0.0890.0 ± 103.83.9 ± 1.60.0 ± 0.08.8 ± 1.6
    Fallow307.2±0.0788.4 ± 145.11.1 ± 0.5B0.1 ± 0.130.2 ± 10.8AB
    607.3±0.11,026.9 ± 116.48.9 ± 3.40.0 ± 0.013.0 ± 3.2
    Mustard307.0±0.11,226.3 ± 258.73.2 ± 1.8AB0.2 ± 0.170.1 ± 17.5A
    607.1±0.2839.1 ± 140.36.4 ± 0.90.0 ± 0.014.7 ± 6.1
    Ditches307.0±0.0871.5 ± 243.58.4 ± 3.1A0.0 ± 0.07.2 ± 1.1c
    607.4±0.1916.5 ± 81.510.7 ± 1.80.0 ± 0.05.1±0.2
    Tailwater pond†NDNDNDNDND
    Irrigated Y2Oats*NDNDNDNDND
    Tomatoes (F)307.6±0.3783.7 ± 79.57.2 ± 4.10.2 ± 0.013.9 ± 1.4A
    607.7±0.1721.1 ± 94.84.0 ± 1.0a0.1 ± 0.010.1 ± 1.3a
    Tomatoes (M)307.3±0.1726.7 ± 158.44.2 ± 0.30.3 ± 0.111.9 ± 2.2A
    607.5±0.1918.1 ± 98.44.1 ± 0.6a0.1 ± 0.016.3 ± 0.5a
    Ditches307.6±0.2789.8 ± 71.64.6 ± 0.80.2 ± 0.17.8 ± 0.4AB
    607.7±0.2800.1 ± 36.03.2 ± 0.6a0.1 ± 0.013.1 ± 1.2a
    Tailwater pond307.5±0.1652.9 ± 47.81.2 ± 0.30.1 ± 0.05.4 ± 0.7B
    607.6±0.1594.6 ± 62.10.6 ± 0.2b0.0 ± 0.05.6 ± 1.4b
    Rainfed Y2Oats307.4±0.1320.4 ± 200.21.0 ± 0.40.1 ± 0.05.3 ± 1.8
    607.8±0.0675.5 ± 95.41.3 ± 0.50.0 ± 0.04.8 ± 1.4
    Fallow307.4±0.11,230.7 ± 146.33.1 ± 2.70.1 ± 0.04.0 ± 1.1
    607.6±0.1647.8 ± 105.02.7 ± 2.00.2 ± 0.12.7 ± 1.0
    Ditches307.6±0.0672.3 ± 64.44.3 ± 1.50.1 ± 0.13.7 ± 0.9
    607.6±0.0738.5 ± 28.04.8 ± 0.50.1 ± 0.02.5 ± 0.2
    Tailwater pond307.5±0.1578.6 ± 41.51.0 ± 0.60.0 ± 0.01.8 ± 0.1
    607.5±0.1585.4 ± 31.71.6 ± 1.30.0 ± 0.02.0 ± 0.2
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"structural_notes": null + }, + { + "text": "904.6 ± 33.1A", + "is_header": false, + "structural_notes": null + }, + { + "text": "5.0 ± 1.2A", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.1 ± 0.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "5.2 ± 0.7AB", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "60", + "is_header": false, + "structural_notes": null + }, + { + "text": "7.5±0.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "894.3 ± 44.7", + "is_header": false, + "structural_notes": null + }, + { + "text": "3.4 ± 1.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.0 ± 0.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "4.9 ± 0.9b", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "Ditches", + "is_header": false, + "structural_notes": null + }, + { + "text": "30", + "is_header": false, + "structural_notes": null + }, + { + "text": "7.4±0.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "1,069.0 ± 14.0A", + "is_header": false, + "structural_notes": null + }, + { + "text": "1.1 ± 0.4B", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.7 ± 0.5", + "is_header": false, + "structural_notes": null + }, + { + "text": "13.7 ± 8.6A", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "60", + "is_header": false, + "structural_notes": null + }, + { + "text": "7.3±0.2", + "is_header": false, + "structural_notes": null + }, + { + "text": "1,039.7 ± 216.9", + "is_header": false, + "structural_notes": null + }, + { + "text": "2.6 ± 1.7", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.1 ± 0.1", + "is_header": false, + "structural_notes": null + }, + { + "text": "22.5 ± 7.1a", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "Tailwater pond", + "is_header": false, + "structural_notes": null + }, + { + "text": "30", + "is_header": false, + "structural_notes": null + }, + { + "text": "7.4±0.2", + "is_header": false, + "structural_notes": null + }, + { + "text": "519.0 ± 196.0B", + "is_header": false, + "structural_notes": null + }, + { + "text": "1.6 ± 0.7B", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.0 ± 0.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "3.8 ± 2.1B", + "is_header": false, + "structural_notes": null + } + ] + }, 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172.8", + "is_header": false, + "structural_notes": null + }, + { + "text": "6.1 ± 1.9AB", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.1 ± 0.1", + "is_header": false, + "structural_notes": null + }, + { + "text": "13.3 ± 2.5B", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "60", + "is_header": false, + "structural_notes": null + }, + { + "text": "7.1±0.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "890.0 ± 103.8", + "is_header": false, + "structural_notes": null + }, + { + "text": "3.9 ± 1.6", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.0 ± 0.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "8.8 ± 1.6", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "Fallow", + "is_header": false, + "structural_notes": null + }, + { + "text": "30", + "is_header": false, + "structural_notes": null + }, + { + "text": "7.2±0.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "788.4 ± 145.1", + "is_header": false, + "structural_notes": null + }, + { + "text": "1.1 ± 0.5B", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.1 ± 0.1", + "is_header": false, + "structural_notes": null + }, + { + "text": "30.2 ± 10.8AB", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "60", + "is_header": false, + "structural_notes": null + }, + { + "text": "7.3±0.1", + "is_header": false, + "structural_notes": null + }, + { + "text": "1,026.9 ± 116.4", + "is_header": false, + "structural_notes": null + }, + { + "text": "8.9 ± 3.4", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.0 ± 0.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "13.0 ± 3.2", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "Mustard", + "is_header": false, + "structural_notes": null + }, + { + "text": "30", + "is_header": false, + "structural_notes": null + }, + { + "text": "7.0±0.1", + "is_header": false, + "structural_notes": null + }, + { + "text": "1,226.3 ± 258.7", + "is_header": false, + "structural_notes": null + }, + { + "text": "3.2 ± 1.8AB", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.2 ± 0.1", + "is_header": false, + "structural_notes": null + }, + { + "text": "70.1 ± 17.5A", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "60", + "is_header": false, + "structural_notes": null + }, + { + "text": "7.1±0.2", + "is_header": false, + "structural_notes": null + }, + { + "text": "839.1 ± 140.3", + "is_header": false, + "structural_notes": null + }, + { + "text": "6.4 ± 0.9", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.0 ± 0.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "14.7 ± 6.1", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "Ditches", + "is_header": false, + "structural_notes": null + }, + { + "text": "30", + "is_header": false, + "structural_notes": null + }, + { + "text": "7.0±0.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "871.5 ± 243.5", + "is_header": false, + "structural_notes": null + }, + { + "text": "8.4 ± 3.1A", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.0 ± 0.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "7.2 ± 1.1c", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "60", + "is_header": false, + "structural_notes": null + }, + { + "text": "7.4±0.1", + "is_header": false, + "structural_notes": null + }, + { + "text": "916.5 ± 81.5", + "is_header": false, + "structural_notes": null + }, + { + "text": "10.7 ± 1.8", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.0 ± 0.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "5.1±0.2", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "Tailwater pond†", + "is_header": false, + "structural_notes": null + }, + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "ND", + "is_header": false, + "structural_notes": null + }, + { + "text": "ND", + "is_header": false, + "structural_notes": null + }, + { + "text": "ND", + "is_header": false, + "structural_notes": null + }, + { + "text": "ND", + "is_header": false, + "structural_notes": null + }, + { + "text": "ND", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "Irrigated Y2", + "is_header": false, + "structural_notes": null + }, + { + "text": "Oats*", + "is_header": false, + "structural_notes": null + }, + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "ND", + "is_header": false, + "structural_notes": null + }, + { + "text": "ND", + "is_header": false, + "structural_notes": null + }, + { + "text": "ND", + "is_header": false, + "structural_notes": null + }, + { + "text": "ND", + "is_header": false, + "structural_notes": null + }, + { + "text": "ND", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "Tomatoes (F)", + "is_header": false, + "structural_notes": null + }, + { + "text": "30", + "is_header": false, + "structural_notes": null + }, + { + "text": "7.6±0.3", + "is_header": false, + "structural_notes": null + }, + { + "text": "783.7 ± 79.5", + "is_header": false, + "structural_notes": null + }, + { + "text": "7.2 ± 4.1", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.2 ± 0.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "13.9 ± 1.4A", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "60", + "is_header": false, + "structural_notes": null + }, + { + "text": "7.7±0.1", + "is_header": false, + "structural_notes": null + }, + { + "text": "721.1 ± 94.8", + "is_header": false, + "structural_notes": null + }, + { + "text": "4.0 ± 1.0a", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.1 ± 0.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "10.1 ± 1.3a", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "Tomatoes (M)", + "is_header": false, + "structural_notes": null + }, + { + "text": "30", + "is_header": false, + "structural_notes": null + }, + { + "text": "7.3±0.1", + "is_header": false, + "structural_notes": null + }, + { + "text": "726.7 ± 158.4", + "is_header": false, + "structural_notes": null + }, + { + "text": "4.2 ± 0.3", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.3 ± 0.1", + "is_header": false, + "structural_notes": null + }, + { + "text": "11.9 ± 2.2A", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "60", + "is_header": false, + "structural_notes": null + }, + { + "text": "7.5±0.1", + "is_header": false, + "structural_notes": null + }, + { + "text": "918.1 ± 98.4", + "is_header": false, + "structural_notes": null + }, + { + "text": "4.1 ± 0.6a", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.1 ± 0.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "16.3 ± 0.5a", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "Ditches", + "is_header": false, + "structural_notes": null + }, + { + "text": "30", + "is_header": false, + "structural_notes": null + }, + { + "text": "7.6±0.2", + "is_header": false, + "structural_notes": null + }, + { + "text": "789.8 ± 71.6", + "is_header": false, + "structural_notes": null + }, + { + "text": "4.6 ± 0.8", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.2 ± 0.1", + "is_header": false, + "structural_notes": null + }, + { + "text": "7.8 ± 0.4AB", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "60", + "is_header": false, + "structural_notes": null + }, + { + "text": "7.7±0.2", + "is_header": false, + "structural_notes": null + }, + { + "text": "800.1 ± 36.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "3.2 ± 0.6a", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.1 ± 0.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "13.1 ± 1.2a", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "Tailwater pond", + "is_header": false, + "structural_notes": null + }, + { + "text": "30", + "is_header": false, + "structural_notes": null + }, + { + "text": "7.5±0.1", + "is_header": false, + "structural_notes": null + }, + { + "text": "652.9 ± 47.8", + "is_header": false, + "structural_notes": null + }, + { + "text": "1.2 ± 0.3", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.1 ± 0.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "5.4 ± 0.7B", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "60", + "is_header": false, + "structural_notes": null + }, + { + "text": "7.6±0.1", + "is_header": false, + "structural_notes": null + }, + { + "text": "594.6 ± 62.1", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.6 ± 0.2b", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.0 ± 0.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "5.6 ± 1.4b", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "Rainfed Y2", + "is_header": false, + "structural_notes": null + }, + { + "text": "Oats", + "is_header": false, + "structural_notes": null + }, + { + "text": "30", + "is_header": false, + "structural_notes": null + }, + { + "text": "7.4±0.1", + "is_header": false, + "structural_notes": null + }, + { + "text": "320.4 ± 200.2", + "is_header": false, + "structural_notes": null + }, + { + "text": "1.0 ± 0.4", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.1 ± 0.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "5.3 ± 1.8", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "60", + "is_header": false, + "structural_notes": null + }, + { + "text": "7.8±0.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "675.5 ± 95.4", + "is_header": false, + "structural_notes": null + }, + { + "text": "1.3 ± 0.5", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.0 ± 0.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "4.8 ± 1.4", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "Fallow", + "is_header": false, + "structural_notes": null + }, + { + "text": "30", + "is_header": false, + "structural_notes": null + }, + { + "text": "7.4±0.1", + "is_header": false, + "structural_notes": null + }, + { + "text": "1,230.7 ± 146.3", + "is_header": false, + "structural_notes": null + }, + { + "text": "3.1 ± 2.7", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.1 ± 0.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "4.0 ± 1.1", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "60", + "is_header": false, + "structural_notes": null + }, + { + "text": "7.6±0.1", + "is_header": false, + "structural_notes": null + }, + { + "text": "647.8 ± 105.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "2.7 ± 2.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.2 ± 0.1", + "is_header": false, + "structural_notes": null + }, + { + "text": "2.7 ± 1.0", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "Ditches", + "is_header": false, + "structural_notes": null + }, + { + "text": "30", + "is_header": false, + "structural_notes": null + }, + { + "text": "7.6±0.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "672.3 ± 64.4", + "is_header": false, + "structural_notes": null + }, + { + "text": "4.3 ± 1.5", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.1 ± 0.1", + "is_header": false, + "structural_notes": null + }, + { + "text": "3.7 ± 0.9", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "60", + "is_header": false, + "structural_notes": null + }, + { + "text": "7.6±0.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "738.5 ± 28.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "4.8 ± 0.5", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.1 ± 0.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "2.5 ± 0.2", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "Tailwater pond", + "is_header": false, + "structural_notes": null + }, + { + "text": "30", + "is_header": false, + "structural_notes": null + }, + { + "text": "7.5±0.1", + "is_header": false, + "structural_notes": null + }, + { + "text": "578.6 ± 41.5", + "is_header": false, + "structural_notes": null + }, + { + "text": "1.0 ± 0.6", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.0 ± 0.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "1.8 ± 0.1", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "", + "is_header": false, + "structural_notes": null + }, + { + "text": "60", + "is_header": false, + "structural_notes": null + }, + { + "text": "7.5±0.1", + "is_header": false, + "structural_notes": null + }, + { + "text": "585.4 ± 31.7", + "is_header": false, + "structural_notes": null + }, + { + "text": "1.6 ± 1.3", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.0 ± 0.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "2.0 ± 0.2", + "is_header": false, + "structural_notes": null + } + ] + } + ], + "cells": [], + "footnote_ids": [ + "doc:b28a36b6e9f8a720", + "doc:b562ea13218b3993" + ] + }, + { + "kind": "paragraph", + "id": "doc:b03351961fe35d91", + "text": "

    Note: Letters (A and B) indicate significant differences at the 0 to 30 cm depth and (a and b) at the 30 to 60 cm depth among sites within each season using a covariance analysis of variance followed by Tukey's Honestly Significant Post Hoc Test.

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    higher in the mustard winter cover crop than for the oats and ditches (p < 0.01) but did not differ from the fallow. Calculated DOC leaching losses below the 60 cm depth for the cover crop (40.7 kg C ha–1 [36.3 lb C ac–1]) were also higher (p < 0.001) than oats and ditches (17.0 and 7.627 kg C ha–1 [15.1 and 6.79 lb C ac–1], respectively) with the fallow field in between (24.0 kg C ha–1 [21.4 lb C ac–1]).

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    During the Irrigated Y1 season, lysimeter DOC concentrations in the ditches were

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    higher than the other sites (p < 0.01), and loads that leached below 60 cm (23.6 in) were 33.8 kg C ha–1 (30.1 lb C ac–1) compared to 6.9 kg C ha–1 (6.2 lb C ac–1) for the field. In the subsequent irrigation season (Irrigated Y2), DOC concentrations were again higher in the fields than the tailwater pond but did not otherwise differ. Dissolved organic C loads, however, were substantially higher in the tomatoes/mustard (54.1 kg C ha–1 [48.2 lb C ac–1]) than in the tomatoes/ fallow (14.2 kg C ha–1 [12.7 lb C ac–1]) treat-

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    ment. When the seasons are summed, the DOC loads for the tomatoes/fallow rotation were almost as high as annual averages of DOC leachate collected in a four-year study of conventional maize in Wisconsin using equilibrium lysimeters for no-till (108.7 kg C ha–1 y–1 [96.8 lb ac–1 yr–1]) and chisel-plowed treatments (125.5 kg C ha–1 y–1 [111.8 lb ac–1 yr–1]) (Brye et al. 2001).

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    Although it is unclear how much leached DOC actually would impact the adjacent waterways, the relative differences in esti-

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    * No data was collected for oats during the summer because no irrigation was applied.

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    † No data was collected for the tailwater pond due to lysimeter contamination caused by flooding.

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    Figure 4

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    (a, b, c, and d) Cumulative mean NO3 –-N leaching by season measured by anion exchange resin bags (columns) buried at 75 cm and loads calculated from data from lysimeters (dots) at 60 cm of depth. Tailwater pond lysimeters in Rainfed Y1 were excluded from the analysis because of flooding, indicated by ND. (e, f, g, and h) Soil NO3 –-N concentrations taken incrementally every 15 cm to a depth of 75 cm (29.5 in) at the start (white column) and at the end of the season (dark columns). Error bars represent standard errors. Letters (a, b, and c) indicate significant difference (p < 0.05) among anion exchange resin bag data and calculated loads (x and y) using a spatial covariance analysis of variance followed by Tukey's Honestrly Significant Post Hoc Test (see table 5 for statistical analysis of lysimeter concentrations). F = fallow and M = mustard cover crop.

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    mated load indicate an important potential negative tradeoff of using cover crops and the need to manage both their decomposition and irrigation runoff carefully. Transport of DOC away from the field may be largely determined by how much irrigation water is lost as DP (Brye et al. 2001). During Irrigated Y1, it was estimated that only 16% of the water was lost as DP. In Irrigated Y2, 15% of the water was lost as DP in the tomato field following the winter fallow, and 27% was lost in the tomato field following the mustard cover crop. Deep percolation and associated DOC could be reduced if cover crops are managed carefully to prevent residue from blocking furrows, which causes ponding and increased infiltration, either by mowing/ incorporating earlier to increase decomposition (weather permitting) or by improved irrigation efficiency.

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    Nitrous Oxide-Nitrogen and Carbon Dioxide-Carbon Soil Emissions. During the irrigation seasons, mean N_2O-N emissions (µg m<sup>-2</sup> h<sup>-1</sup>) were similar across the fields, ditches, and pond, but in the rainfed seasons, differences were significant (figure 5). The N_2O-N emission rates in the ditches during the Rainfed Y1 season were 4–, 7– and 9–fold higher than oat, mustard, or fallow fields, respectively. In the Rainfed Y2 season, the tailwater pond had the highest N_2O-N emission rates, followed closely by the ditches, and both were higher than the fallow field but were not different from the oat field.

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    Soil \\rm CO_2–C emission rates were higher in the tomato fields following a fallow, compared to ponds and ditches. Rates (mg m<sup>-2</sup> h<sup>-1</sup>) from the tomato field were 2.5 times higher than in the nonirrigated oat field during Irrigated Y1 (figure 5). In Irrigated Y2, the tomato field following the winter fallow had 3.5–fold higher \\rm CO_2–C emissions than the tailwater pond but was not different from the other sites. Mean \\rm CO_2–C emissions were not different in Rainfed Y1. In Rainfed Y2, emissions were almost 2-fold higher in the fallow and oat fields, compared to the tailwater pond, but were no different than the ditches.

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    Mean seasonal N<sub>2</sub>O-N emissions were always <5 g ha<sup>-1</sup> d<sup>-1</sup> (0.004 lb ac<sup>-1</sup> day<sup>-1</sup>), much lower than many conventionally managed systems, such as a cornfield fertilized with 290 kg N ha<sup>-1</sup> (259 lb N ac<sup>-1</sup>), where emissions were 52 g N<sub>2</sub>O-N ha<sup>-1</sup> d<sup>-1</sup> (0.05 lb N<sub>2</sub>O-N ac<sup>-1</sup> day<sup>-1</sup>) (McSwiney and Robertson 2005). Our results were more similar to means for unfertilized corn in

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    Table 6Tomato yields and weed biomass for 2005 and 2006. Mean fresh weights (fw) and standard errors for tomatoes in each field have been classified by quality (n = 6 in 2005 and n = 3 in 2006). Total aboveground biomass is reported as dry weight (dw).

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    20052006
    Harvested material (Mg ha-1)FallowMustard
    Weed biomass (dw)0.2 \\pm 0.02.2 ± 0.92.1 ± 0.6
    Total aboveground tomato biomass (dw)3.5 \\pm 0.515.9 ± 2.911.4 ± 3.5
    Split red tomatoes (fw)1.8 \\pm 0.56.5 ± 3.9b6.8 ± 2.1a
    Pink tomatoes (fw)3.0 \\pm 0.510.8 \\pm 4.5b13.2 ± 3.1a
    Green tomatoes (fw)3.1 \\pm 0.714.5 ± 5.022.4 ± 3.6
    Sunburn tomatoes (fw)6.3 \\pm 0.720.4 \\pm 4.119.3 ± 3.4
    Moldy or rotten tomatoes (fw)10.6 ± 1.127.3 \\pm 5.036.0 \\pm 6.4
    Blossom end rot tomatoes (fw)1.7 \\pm 0.41.9 \\pm 0.54.4 ± 1.2
    Insect damaged tomatoes (fw)1.7 \\pm 0.65.0 \\pm 2.411.4 ± 2.7
    Undamaged tomatoes (fw)15.7 ± 3.967.1 ± 5.555.0 ± 10.9
    Total fruit biomass (fw)46.9 \\pm 6.9153.6 ± 19.3168.5 ± 15.3
    ", + "rows": [ + { + "cells": [ + { + "text": "", + "is_header": true, + "structural_notes": null + }, + { + "text": "2005", + "is_header": true, + "structural_notes": null + }, + { + "text": "2006", + "is_header": true, + "structural_notes": null + }, + { + "text": "", + "is_header": true, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "Harvested material (Mg ha-1)", + "is_header": true, + "structural_notes": null + }, + { + "text": "", + "is_header": true, + "structural_notes": null + }, + { + "text": "Fallow", + "is_header": true, + "structural_notes": null + }, + { + "text": "Mustard", + "is_header": true, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "Weed biomass (dw)", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.2 \\pm 0.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "2.2 ± 0.9", + "is_header": false, + "structural_notes": null + }, + { + "text": "2.1 ± 0.6", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "Total aboveground tomato biomass (dw)", + "is_header": false, + "structural_notes": null + }, + { + "text": "3.5 \\pm 0.5", + "is_header": false, + "structural_notes": null + }, + { + "text": "15.9 ± 2.9", + "is_header": false, + "structural_notes": null + }, + { + "text": "11.4 ± 3.5", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "Split red tomatoes (fw)", + "is_header": false, + "structural_notes": null + }, + { + "text": "1.8 \\pm 0.5", + "is_header": false, + "structural_notes": null + }, + { + "text": "6.5 ± 3.9b", + "is_header": false, + "structural_notes": null + }, + { + "text": "6.8 ± 2.1a", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "Pink tomatoes (fw)", + "is_header": false, + "structural_notes": null + }, + { + "text": "3.0 \\pm 0.5", + "is_header": false, + "structural_notes": null + }, + { + "text": "10.8 \\pm 4.5b", + "is_header": false, + "structural_notes": null + }, + { + "text": "13.2 ± 3.1a", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "Green tomatoes (fw)", + "is_header": false, + "structural_notes": null + }, + { + "text": "3.1 \\pm 0.7", + "is_header": false, + "structural_notes": null + }, + { + "text": "14.5 ± 5.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "22.4 ± 3.6", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "Sunburn tomatoes (fw)", + "is_header": false, + "structural_notes": null + }, + { + "text": "6.3 \\pm 0.7", + "is_header": false, + "structural_notes": null + }, + { + "text": "20.4 \\pm 4.1", + "is_header": false, + "structural_notes": null + }, + { + "text": "19.3 ± 3.4", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "Moldy or rotten tomatoes (fw)", + "is_header": false, + "structural_notes": null + }, + { + "text": "10.6 ± 1.1", + "is_header": false, + "structural_notes": null + }, + { + "text": "27.3 \\pm 5.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "36.0 \\pm 6.4", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "Blossom end rot tomatoes (fw)", + "is_header": false, + "structural_notes": null + }, + { + "text": "1.7 \\pm 0.4", + "is_header": false, + "structural_notes": null + }, + { + "text": "1.9 \\pm 0.5", + "is_header": false, + "structural_notes": null + }, + { + "text": "4.4 ± 1.2", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "Insect damaged tomatoes (fw)", + "is_header": false, + "structural_notes": null + }, + { + "text": "1.7 \\pm 0.6", + "is_header": false, + "structural_notes": null + }, + { + "text": "5.0 \\pm 2.4", + "is_header": false, + "structural_notes": null + }, + { + "text": "11.4 ± 2.7", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "Undamaged tomatoes (fw)", + "is_header": false, + "structural_notes": null + }, + { + "text": "15.7 ± 3.9", + "is_header": false, + "structural_notes": null + }, + { + "text": "67.1 ± 5.5", + "is_header": false, + "structural_notes": null + }, + { + "text": "55.0 ± 10.9", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "Total fruit biomass (fw)", + "is_header": false, + "structural_notes": null + }, + { + "text": "46.9 \\pm 6.9", + "is_header": false, + "structural_notes": null + }, + { + "text": "153.6 ± 19.3", + "is_header": false, + "structural_notes": null + }, + { + "text": "168.5 ± 15.3", + "is_header": false, + "structural_notes": null + } + ] + } + ], + "cells": [], + "footnote_ids": [] + }, + { + "kind": "paragraph", + "id": "doc:f15706354bfe24f9", + "text": "

    Note: Letters indicate significant differences between the fallow and mustard rotations within each season using a covariance analysis of variance followed by Tukey's Honestly Significant Post Hoc Test.

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    the same study (5 g N_2O-N ha<sup>-1</sup> d<sup>-1</sup> [0.004 lb N_2O-N ac<sup>-1</sup> day<sup>-1</sup>]), and for a long term organic production trial in the Midwest United States (3.5 g N_2O-N ha<sup>-1</sup> d<sup>-1</sup> [0.003 lb N_2O-N ac<sup>-1</sup> day<sup>-1</sup>]) (Robertson et al. 2000). Other organic farming systems, however, have shown much higher mean emissions (10 to 70 g N_2O-N ha<sup>-1</sup> d<sup>-1</sup>) [0.009 to 0.06 lb N_2O-N ac<sup>-1</sup> day<sup>-1</sup>]) (Baggs et al. 2000).

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    Monthly sampling may have underestimated actual emissions as large spikes can occur on short time frames. For example, a large spike was observed (16 g N<sub>2</sub>O-N ha-1 h-1 [0.01 lb N<sub>2</sub>O-N ac-1 hr-1]) in the cover cropped field two days after irrigation. Irrigated vegetable production systems have high temporal and spatial variation in moisture and inorganic N between fields, ditches, and tailwater ponds (Sanchez-Martin et al. 2008). Thus these calculations of annual fluxes are considered estimates. These estimates indicate that while there are likely no increases in CO2-C emissions due to BMP implementation, there are significant increases in N<sub>2</sub>O-N emissions from tailwater ponds. How much this increases the overall global warming potential of the farm is contingent upon the relative size of the pond and its associated drying and wetting soils as well as the frequency of inundation.

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    Tomato Yields. In the first year of the experiment, late spring rains followed by high summer temperatures (figure 2) resulted in a fungal disease outbreak (Sclerotium rolfsii [Southern Blight]). The disease reduced plant density dramatically over the season (data not shown). By harvest, nearly one-quarter of the

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    tomato crop showed disease damage (table 6), and yields were very low. No control measures are available for this disease, even under conventional production (AVRDC—The World Vegetable Center 2010).

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    In the second year, there was little disease, and yields were much higher (no statistical comparison was made between years). At harvest, neither total crop yields, crop aboveground biomass, nor weed biomass differed between the prior winter fallow and mustard cover crop treatments (table 6). There were, however, more split red tomatoes and pink tomatoes following the mustard cover crop treatment. Only one-third of the tomatoes were undamaged, yet the grower harvested several of the other categories as is typical for machine-harvested processing tomatoes.

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    Tomato Nutrient Uptake and Yields. Tomato N uptake is another factor that contributes to the temporal and spatial variability of N dynamics. Larger N2O emissions and NO3-N leaching observed during Irrigated Y1 compared to Irrigated Y2 indicate that there was greater soil N availability in the first year with the diseased tomatos. One explanation may be that the residue from the tomatoes that grew nearly to maturity and died in the outbreak of Southern Blight underwent decomposition and N mineralization during the crop-growing season, increasing soil inorganic N concentrations. If this was the case, potentially 7.9 Mg (8.6 tn) of plant material from the biomass of these dead plants (estimated by the difference between the harvested live biomass of the diseased field of the first year and the healthy

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    Letters indicate significant difference (p < 0.05) using a spatial covariance analysis of variance followed by Tukey's Honestly Significant Post Hoc Test.

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z/kd/E3/XGz/9BkrWrJ8Of8jv4m/642f/AKDJQM66iiikBh3v/I66N/15Xn/oUFblYd7/AMjro3/Xlef+hQVuUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAYetf8AIe8N/wDX5L/6TS1uVh61/wAh7w3/ANfkv/pNLW5QAV578ajcj4cXPkbtvnxedj+5n+WcV6FUF5Z2+oWc1ndwpNbzKUkjcZDA9qAPPvhvJ4F07QLS50y70+G/e3Vbp5Zws27gsGDHIGfwriviyfDP9saXceG5LVtckud0psXBzyMFtvG7d+NbOrfALTZJ5J9P1yWyt+WMc8IlCD/e3Lx9a2fBPwm8P6Fdxat9v/te4jOYZcKIkYdwoJyfqTigZ6RFu8pN/wB/aN31p9FFAgooooAKKKKACiiigAooooA5nwT/AMeGrf8AYZvv/R7V01cz4J/48NW/7DN9/wCj2rpqACiiigAooooAKKKKACq1/YWuqWE1jewLPbTLskjbowqzWfq2h6drkUceo2/nJGxZBvZcH/gJFAGVB4D0CJonkhubl4HV4XubyWUxbTlQhLfKMgcDr3zXS15z4U8EeHZpte8zT8+Tq0qR/v5BhQqHH3vUmvRqACiiigAooooAKKKKACiiigAooooAKKKKACuZ8Pf8jX4u/wCv2D/0mirpq5nw9/yNfi7/AK/YP/SaKgDpqKKKACiiigArz341G5Hw4ufI3bfPi87H9zP8s4r0KoLyzt9Qs5rO7hSa3mUpJG4yGB7UAeffDeTwLp2gWlzpl3p8N+9uq3TyzhZt3BYMGOQM/hXFfFk+Gf7Y0u48NyWra5Jc7pTYuDnkYLbeN278a2dW+AWmyTyT6frktlb8sY54RKEH+9uXj61s+CfhN4f0K7i1b7f/AGvcRnMMuFESMO4UE5P1JxQM9Ii3eUm/7+0bvrT6KKBBRRRQAUUUUAFFFFABRRRQBxtr/wAj54j/AOuVp/6C9bVYtr/yPniP/rlaf+gvW1TEFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXO+E+mt/8AYWuP5iuirnfCfTW/+wtcfzFAHRUUUUAFFFFABRRRQAViW/8AyU+D/sEv/wCjRW3WJb/8lPg/7BL/APo0UAdnRRRSGFFFFAHhvxKXT5PjBoyeJGZdFNuuSSQvVs5I7bsZ9q9MguPBMOgHTYrvRRpJU5h8+MxEE5OQTjrzUvi3wZpPjLT1ttSjYPGSYZ4jh4yfQ9x7GvLrj4C2NrMrXHi1YYXbCiS2VWJ9ATJgn8KBh8Ohp8fxl1ePw0xOi/Z2+4SU/h6Z7bs49q9yrnPCHgrSfBlg9vpqO0kuDNcSnLyEdPoPYV0dAgooooAKKKKACiiigAooooAz9d/5F7U/+vSX/wBANQ+F/wDkUtF/68IP/Ra1Nrv/ACL2p/8AXpL/AOgGofC//IpaL/14Qf8AotaANamTM6QSNGm9wpKr6nHSn0UAeJ3WrQap4IvrnUfE182tSxTKdGtJFjEJGfkMQGdoHJZu2a37m/t9O1fwJqF2StpFpsnmz7crEGjjUMx7DJAz713mo6no+iFbjUryzsjMdqyTusZfHbJ61QHjjwnJ8v8AwkelHPGDdJz+tAGPo91b678S7zV9LlWfT7fTltHuYzmOSUvuwrdGwPT1ruKakaRIEjRUUdFUYAp1AHgDf8hTWP8AsJ3f/o5qdTW/5Cmsf9hO7/8ARzU6qET6V/yNnh7/ALCC/wDoD17tXhOlf8jZ4e/7CC/+gPXu1JjPGPGf/JQ9V/64W/8A6C1cxe/8hXTP9+T/ANANdP4z/wCSh6r/ANcLf/0Fq5i9/wCQrpn+/J/6AaAF13/kBXv/AFxavoxfuj6V8567/wAgK9/64tX0Yv3R9KGB5j8Vf+Q74b/65Xf/ALRrg5dF02eRpJLKFnc7mYryTXefFX/kO+G/+uV3/wC0a5GhAVrbT7OzZmtraOIsMEouM1678N/+Se6N/wBcT/6E1eV16p8N/wDknujf9cT/AOhNQwOpooopAFFFFABRRRQBzPw//wCRKsf9+b/0c9dNXM/D/wD5Eqx/35v/AEc9dNQAUUUUAFee/Go3I+HFz5G7b58XnY/uZ/lnFehVBeWdvqFnNZ3cKTW8ylJI3GQwPagDz74byeBdO0C0udMu9Phv3t1W6eWcLNu4LBgxyBn8K4r4snwz/bGl3HhuS1bXJLndKbFwc8jBbbxu3fjWzq3wC02SeSfT9clsrfljHPCJQg/3ty8fWtnwT8JvD+hXcWrfb/7XuIzmGXCiJGHcKCcn6k4oGekRbvKTf9/aN31p9FFAgooooAKKKKACiiigAooooA4rw9/yEfEv/YXk/wDRcVbtYXh7/kI+Jf8AsLyf+i4q3aYgooooAbJIkMTSyOqRoCzMxwAB1JNYdr4z8P3t3DbQX+ZJm2xFoZFSQ+iuVCn8DU3iq9j07wtqV3LaLdxxwMWgcZV+2D7etcL4ibUItF0V77XrSXz7u2eGytbVVQDePutktgDvQM9SooooEFFFFAHO6T/yO3iP/rnaf+gvXRVzuk/8jt4j/wCudp/6C9dFQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVk+HP+R38Tf9cbP/0GStasnw5/yO/ib/rjZ/8AoMlAzrqKKKQGHe/8jro3/Xlef+hQVuVh3v8AyOujf9eV5/6FBW5QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBh61/wAh7w3/ANfkv/pNLW5WHrX/ACHvDf8A1+S/+k0tblABRRRQBmeJP+RX1b/rzm/9ANcl8GP+Sa2X/XWX/wBDNdze2kd/Y3FnKWEc8bROVOCAwwcfnWJpmn6N8PfCZhN3JHptrukaW5YMw3HPYDPJ4AFAHRUVytn8RfDF/ol7q1rqBlt7IBrhRGwdATgHaRkj3rp4ZUnhjmQ5R1DKfYjNAD6KKKACiiigAooooAKKKKAOZ8E/8eGrf9hm+/8AR7V01cz4J/48NW/7DN9/6PaumoAKKKKACiiigAooooAKKwvFniUeFtLhvmsZbwSXCQeXE6qRuzz83HbH41y//C1h/wBC1qH/AH/h/wDi6AN/wf8A67xH/wBhmb/0COumrx/w58SDZSayX8P3kn2jUpZgEni+UFUGDlhzx24561vW/wAU0n1CxtG8P3sP2q6itxI80WFLsFzwSe9AHoVFFFABRRRQAUUUUAFFFc7d+O/DVjdS28+pANC+yV0hkeONvRnVSoP1NAHRUVy3iPxQNE1jQVa4gj068MxnlcZ+VU3LtPuT+Nauj+ItK17zhpt2JXhIEsbI0bpnplWAIB9cUAalFFFABXM+Hv8Aka/F3/X7B/6TRV01cz4e/wCRr8Xf9fsH/pNFQB01FFFABRRRQAUUUUAZniT/AJFfVv8Arzm/9ANcl8GP+Sa2X/XWX/0M13N7aR39jcWcpYRzxtE5U4IDDBx+dYmmafo3w98JmE3ckem2u6RpblgzDcc9gM8ngAUAdFRXK2fxF8MX+iXurWuoGW3sgGuFEbB0BOAdpGSPeunhlSeGOZDlHUMp9iM0APooooAKKKKACiiigAooooA421/5HzxH/wBcrT/0F62qxbX/AJHzxH/1ytP/AEF62qYgooooAKKKKACiiigAooooAKKKKACiiigAooooAK53wn01v/sLXH8xXRVzvhPprf8A2Frj+YoA6KiiigAooooAKKKKACsS3/5KfB/2CX/9GitusS3/AOSnwf8AYJf/ANGigDs6KKKQwooooAK8t+Nf/Hj4d/7Ca/yr1KsHxP4S0/xZFZx6hJcILScTx+QwXLD1yDxQBvdqKwPEfjTQPCbQJrF8IHn5RFRnYj1woOBU8PinSLm+060t7oTPqMLzWzRglXVevPb8aANiiiigAooooAKKKKACiiigDP13/kXtT/69Jf8A0A1D4X/5FLRf+vCD/wBFrU2u/wDIvan/ANekv/oBqHwv/wAilov/AF4Qf+i1oA1qKKKAGSRRygCSNHA6bhmuW8H2dt5viL/R4f8AkMzfwD+6ldZXM+D/APW+Iv8AsMzf+gpQB01FFFAHgDf8hTWP+wnd/wDo5qdTW/5Cmsf9hO7/APRzU6qET6V/yNnh7/sIL/6A9e7V4TpX/I2eHv8AsIL/AOgPXu1JjPF/GhI+IOrFRk/Z7fAzjPytXITjVpry0m+wQDyGY4+09cqR/d967Dxn/wAlD1X/AK4W/wD6C1ZNAGTqj3Enh6+NzCkL+W2FSTeMY65wK+kl+6PpXznrv/ICvf8Ari1fRi/dH0oYHmPxV/5Dvhv/AK5Xf/tGuRrrvir/AMh3w3/1yu//AGjXI0IAr1T4b/8AJPdG/wCuJ/8AQmryuvVPhv8A8k90b/rif/QmoYHU0UUUgCiiigAooooA5n4f/wDIlWP+/N/6Oeumrmfh/wD8iVY/783/AKOeumoAKKKKACiiigDM8Sf8ivq3/XnN/wCgGuS+DH/JNbL/AK6y/wDoZrub20jv7G4s5SwjnjaJypwQGGDj86xNM0/Rvh74TMJu5I9Ntd0jS3LBmG457AZ5PAAoA6KiuVs/iL4Yv9EvdWtdQMtvZANcKI2DoCcA7SMke9dPDKk8McyHKOoZT7EZoAfRRRQAUUUUAFFFFABRRRQBxXh7/kI+Jf8AsLyf+i4q3awvD3/IR8S/9heT/wBFxVu0xBRRRQBBeT2kFuxvZYY4G+QmZgFOe3PFc9aQeBbV5o7UaAjEqZVVos5zlcjtyMiuiubS2vYvKureKePOdkqBhn1wa5TSND0h/FniNG0uyKI9vtU26YXMWTjjigDsFZXRXRgysMgg5BFLSIixoqIoVVGAoGAB6UtABRXMfEONZfAOrI+ceWp4JHR1PavNv7Ntv+m//gRJ/wDFUAenaT/yO3iP/rnaf+gvXRV8/wBrp1t/bOoDEowsRyJnBPB6nPNLren26aLdsPNyIyRmZyPyJoGe/wBFFFAgooooAKKKKACiiigAooooAKKKKACsnw5/yO/ib/rjZ/8AoMla1ZPhz/kd/E3/AFxs/wD0GSgZ11FFFIDDvf8AkddG/wCvK8/9CgrcrDvf+R10b/ryvP8A0KCtygAooooAKKKKACiiigAooooAKKKKACiiigDD1r/kPeG/+vyX/wBJpa3Kw9a/5D3hv/r8l/8ASaWtygAoori/inr954c8DXN1YOY7mV1gSQdU3dSPfANAHQ3/AIi0TSpfK1DWLC0k/uT3CI35E5rJ8T6fbeOvCVxZ6VqVpKxZJI5UkEke9SGAbbng4rzfw/8ABC21fR7bVNV1y5a4vI1nIhUHG4Z5Zs7jz1rL8ReFbz4RatpmtaPrEk0E03lvE42swHJVsHDAj8qBnZ+LPh5qmu6BDe2kdlZeImh8i8jtjshuEJ+7z6YBBPp9K9LsomgsLaF8b44lVseoAFSxv5kSOBjcAcU6gQUUUyWWOCF5ZXWONAWZ2OAoHUk0APorL0HxDp3iWwe+0yVpbdZWi3lcZK9SPaszWvH/AIf0LUHsLmeeW6jXdLHbQNKYh6ttGBQB09FUtN1aw1jTI9RsLqOe0kG5ZVOBx1znpj3rmj8UfCgvPI+2y+V5nl/a/s7+Ru9N+Mfj0oA7KikVgyhlIKkZBB4IpaAOZ8E/8eGrf9hm+/8AR7V01cz4J/48NW/7DN9/6PaumoAKKKKACiiigAooooA4b4q/8ivaf9hGD+ZrzuvRPir/AMivaf8AYRg/ma87poDO0r7+of8AX2/8lrQh/wCQ3of/AGFbT/0atZ+lff1D/r7f+S1oQ/8AIb0P/sK2n/o1aBHv1FFMmkWKGSRyQiKWYj0ApDKMuv6NBfCxm1ewjuycCB7lBJn02k5qnqniJdL8SaZpsqxJb3cM8slxI+0RiMA/THNec3UE158OdR1Cx0bRbLRZYpZY5Lx3lupOT85bHDE9Mk9q2by3ttU8S+AV1JUmV7GWQrJyHcRoRkd+efwoA9BstQstSg8+wvLe6hzjzIJQ659Mg4qzXEafbw6f8WL630+NIrefS0muoohhRIHwrEDgEjNdvQAjHapOCcDOBXky317qHgLU9UTVdP0bSnFwiadBZq7E5YbXZj99j2A7161XPX+keEdKum1jUbPR7SaVthurlY49zEHIy3GSAfc80AcYZrWMfDKa+ZBEIid8nQN5KhST25x+Nb+6Of4vo9myt5OlMt6yHIBLgxhvfqfpV19b8CS2qWsmqeHGt44zEkRuICqocZUDOAOBx7Vs6TpGlaRa+XpNla2sEnz4t0Ch/fjrQBfooooAK5nw9/yNfi7/AK/YP/SaKumrmfD3/I1+Lv8Ar9g/9JoqAOmooooAKKKKACiiuL+Kev3nhzwNc3Vg5juZXWBJB1Td1I98A0AdDf8AiLRNKl8rUNYsLST+5PcIjfkTmsnxPp9t468JXFnpWpWkrFkkjlSQSR71IYBtueDivN/D/wAELbV9HttU1XXLlri8jWciFQcbhnlmzuPPWsvxF4VvPhFq2ma1o+sSTQTTeW8TjazAclWwcMCPyoGdn4s+Hmqa7oEN7aR2Vl4iaHyLyO2OyG4Qn7vPpgEE+n0r0uyiaCwtoXxvjiVWx6gAVLG/mRI4GNwBxTqBBRRTJZY4IXlldY40BZnY4CgdSTQA+isvQfEOneJbB77TJWlt1laLeVxkr1I9qzNa8f8Ah/QtQewuZ55bqNd0sdtA0piHq20YFAHT0VS03VrDWNMj1Gwuo57SQbllU4HHXOemPeuaPxR8KC88j7bL5XmeX9r+zv5G7034x+PSgDsqKRWDKGUgqRkEHgiloA421/5HzxH/ANcrT/0F62qxbX/kfPEf/XK0/wDQXrapiCiikfcUbYQGxwSMjNAC0Vxt9deL7LVNLszqGjN9uleMMLKQbNsbPnHm8/dxXS6ZHqUduw1S4tZ5t+Va2haNQuBwQWbnOec0AXaKKKACiiigAooooAKKKKACoL67jsLC5vJgxit4mlcIMnaoJOB3PFT1meI/+RY1b/rym/8AQDQBhJ8StCeNXEOp4YAj/QZD/SsXw3490m0GrebBqI83UppU22bt8pxjOOh9jzXO2X/Hhbf9cl/kKq6T0vf+vuT+lAz1XRfGWl69qjafZpdrOsJnPn27RjaGC9/dhXQ15f4I/wCR9k/7Bb/+jUr1CgQUUUUAFFFFABWJb/8AJT4P+wS//o0Vt1iW/wDyU+D/ALBL/wDo0UAdnRRRSGFFFFACMyopZiFUDJJOAKxz4v8ADQuPIPiHSvNzjZ9sjzn0615n8TptT8TePdL8EWt79jtJ4xJK3OHJyeR/FgLwPWpf+GftI8nH9t33m4+95abc/T/69AHW634Zvb3xlp/iHT/7PurdoPsl3DdruBhLZLJ2zyaytL+H15oPxHtdRsJFOgRJMyQM/Nu8i4KqP7pIB9qwPhnJqXhb4g6l4JuL37ZZxxmSI84QjByB/DkHketez0AFFFFABRWT4g8SaX4YsBeapcCKNnCIoGWdj2A71pSTxQ2zXEsixwqu9nc4CrjOTQBJRXH2vxO8LXd9FbJeTIsz+XFcS27pDI3oHIxXQazrmneH9Oa/1O5W3t1IG4gksT0AA5J9hQBoUVzmheOND8Q3rWNpNNFeKu/7PcwtE7L6gMOR9K6OgDP13/kXtT/69Jf/AEA1D4X/AORS0X/rwg/9FrU2u/8AIvan/wBekv8A6Aah8L/8ilov/XhB/wCi1oA1qz9dmnt/D2pT2svk3EdrK8Um0NscISDg8HBxwa0KzfEP/Itar/15zf8AoBoA8htPGPjGazglbxANzxqx/wBCi6kfSqGi+JPFFu2p+RrYQyX0kkn+iRncxC5PI46DioNP/wCQba/9cU/kKr6V96//AOvt/wCQpgegeBfEniDUvGclhqmqfa7X+z3mWP7PHHhxIgzlRnox46V6dXkHw7/5KG//AGCZf/RsVev0gPAG/wCQprH/AGE7v/0c1Oprf8hTWP8AsJ3f/o5qdVCJ9K/5Gzw9/wBhBf8A0B692rwnSv8AkbPD3/YQX/0B692pMZ4x4z/5KHqv/XC3/wDQWrJrW8Z/8lD1X/rhb/8AoLVk0IRn67/yAr3/AK4tX0Yv3R9K+c9d/wCQFe/9cWr6MX7o+lDGeY/FX/kO+G/+uV3/AO0a5Guu+Kv/ACHfDf8A1yu//aNcjQgCvVPhv/yT3Rv+uJ/9CavK69U+G/8AyT3Rv+uJ/wDQmoYHU0UVl6hrcOn6zpemvE7Sai0io64wuxdxzSA1KKKKACiiigDmfh//AMiVY/783/o566auZ+H/APyJVj/vzf8Ao566agAooooAKKK4v4p6/eeHPA1zdWDmO5ldYEkHVN3Uj3wDQB0N/wCItE0qXytQ1iwtJP7k9wiN+ROayfE+n23jrwlcWelalaSsWSSOVJBJHvUhgG254OK838P/AAQttX0e21TVdcuWuLyNZyIVBxuGeWbO489ay/EXhW8+EWraZrWj6xJNBNN5bxONrMByVbBwwI/KgZ2fiz4eaprugQ3tpHZWXiJofIvI7Y7IbhCfu8+mAQT6fSvS7KJoLC2hfG+OJVbHqABUsb+ZEjgY3AHFOoEFFFMlljgheWV1jjQFmdjgKB1JNAD6Ky9B8Q6d4lsHvtMlaW3WVot5XGSvUj2rM1rx/wCH9C1B7C5nnluo13Sx20DSmIerbRgUAdPRVLTdWsNY0yPUbC6jntJBuWVTgcdc56Y965o/FHwoLzyPtsvleZ5f2v7O/kbvTfjH49KAOyopFYMoZSCpGQQeCKWgDivD3/IR8S/9heT/ANFxVu1heHv+Qj4l/wCwvJ/6LirdpiCszxDrUfh7Q7jVJIHnSEoDHGQGbc4XjP8AvVp1yvxH/wCRD1D/AHof/RyUAZ//AAslf+he1H/v5D/8XWNpnj7yfEmu3B0K9ZbhoCFWSPcu2PHOWx+RP4Vm1nWX/IX1P6x/+gUDO5m+J8cEe9vD+ogZA5ki7nH973rva8O1P/jxP/XSP/0Na9xoEc18QP8AkRNW/wCuQ/8AQhXn9egfED/kRNW/65D/ANCFef0AZ1p/yG9R/wB2L+Ro13/kBXn/AFyNFp/yG9R/3Yv5GjXf+QFef9cjQM92ooooEFQT3ttbTQQzzJHJcPsiVjy7YzgfgK4TV/EF5b3V+JvFtrY3ULN5NjbWyzgKPu+YdpYE9+mKZq2o3utWfgi/tvKhvLqfcCy5VGMRycd8cnFAz0WiuUsr7VtK8WQaLqd+NQgvbd5YJ2hWN0ZMblIXAIwciuroEFFFFABRTUkSQEo6sAcHBzg+lOoAKKKKACsnw5/yO/ib/rjZ/wDoMla1ZPhz/kd/E3/XGz/9BkoGddRRRSAw73/kddG/68rz/wBCgrcrDvf+R10b/ryvP/QoK3KACiiigAooooAKKKKACiiigAooooAKKKKAMPWv+Q94b/6/Jf8A0mlrcrD1r/kPeG/+vyX/ANJpa3KACsfxR4dtfFPh+50i7JRJgNsi9UYchh9DWxXH/E3xHdeF/BNzfWJ23UjrBE+M7C38X1ABoA80Xwb8V/DP+g6JqElzZIf3flXSBQPZZCNv0FaOh/DDxTr+tW2p+OdSaSG3YMts03ms2DnHHyqPXHWtr4V+D9a0ud/Emr6kLmTUrUHy2ZncAkMCzHvjt71zPiu01/4aeMk8SW2p+fp2pXrGW3GVByclWXJB46N7dqBnuwGBgdKKajiSNXHRgCKdQIKyda8PWmvtbJfyTNawuXa1V9scx4x5g/iAx06VrVia/qmuac0A0fw4dXDg+YRexweX6ff659vSgDA+GOIfDeq7FACapdYUDAGG6UfCmNZfCUmpyANd6hdzTXEh6sd5AB+gFZ3gw+MdBjksbrwa3kXV/JcSXH9pQ/ulkbJ+UElsD86nsofEfgWe90/TtBfWdJnnae0aG4WNoCxyUYN2z3oA5jV7mXQbH4jaXYkx2wlgkjC8CPzsB8enWvT/AOwrBvBY0XyE+xmz8rZjjG3r9c859awNM8E3F94d15deZE1LXmMk4iO4QYH7tQe+3iqi3/jxNE/4R7/hHVa+EX2Yar9qTyNuMeZj72cduufyoA1/hfeTXvw80t52LPGrQ7j3CMVH6AVuavrH9k+T/wAS3Ub3zd3/AB5w+Zsxj73IxnPH0NJ4c0WLw74estJhbettGFL4+83Un8SSa1KAOU+H9x9q0fUpvJlh36veN5cq7WXMzHBHYjOD7iurrmfBP/Hhq3/YZvv/AEe1dNQAUUUUAFFct8R7iW18A6rPDK8UiIhDxsQw+dehFRReMLy1utOTVdDlsrC/dYbe6a4VzvI+USIB8mfqfegDrqKKKAOG+Kv/ACK9p/2EYP5mvNLq1W6VVaWaPac5ikKE/XFel/FX/kV7T/sIwfzNed00BmR6HBEXKXV8N7bj/pDDJrSh/wCQ3of/AGFbT/0atLSQ/wDIb0P/ALCtp/6NWgR79QQCCCMg9qKKQzlx8PfDg8yM2s7Wrlj9ka6lMClupWPdtB5PQcdsVfvPCmi39vaw3FnuW0i8m3YSuGiXj7rA5B+Uc9eOtWtV0TTdcgSHUrRLmONt6qxIwcYzwa4jwz4H8NT33iNJdJidYNVaOMFm+VfJibHX1Yn8aAOy0bw9p2hCc2UchluGDTTzytLLIR03OxJOK1KAAAAOgooAKjmghuECTxRyqDkK6hhn8akooA5DQNNsW8V+LA1lbkLdwBQYl4H2aI8cepNdcAAAAAAOgFc34e/5Gzxd/wBflv8A+ksVdLQAUUUUAFcz4e/5Gvxd/wBfsH/pNFXTVzPh7/ka/F3/AF+wf+k0VAHTUUUUAFFFFABWP4o8O2vinw/c6RdkokwG2ReqMOQw+hrYrj/ib4juvC/gm5vrE7bqR1gifGdhb+L6gA0AeaL4N+K/hn/QdE1CS5skP7vyrpAoHsshG36CtHQ/hh4p1/WrbU/HOpNJDbsGW2abzWbBzjj5VHrjrW18K/B+taXO/iTV9SFzJqVqD5bMzuASGBZj3x2965nxXaa/8NPGSeJLbU/P07Ur1jLbjKg5OSrLkg8dG9u1Az3YDAwOlFNRxJGrjowBFOoEFZOteHrTX2tkv5JmtYXLtaq+2OY8Y8wfxAY6dK1qxNf1TXNOaAaP4cOrhwfMIvY4PL9Pv9c+3pQBgfDHEPhvVdigBNUusKBgDDdKPhTGsvhKTU5AGu9Qu5priQ9WO8gA/QCs7wYfGOgxyWN14NbyLq/kuJLj+0of3SyNk/KCS2B+dT2UPiPwLPe6fp2gvrOkzztPaNDcLG0BY5KMG7Z70Acxq9zLoNj8RtLsSY7YSwSRheBH52A+PTrXp/8AYVg3gsaL5CfYzZ+Vsxxjb1+uec+tYGmeCbi+8O68uvMialrzGScRHcIMD92oPfbxVRb/AMeJon/CPf8ACOq18Ivsw1X7UnkbcY8zH3s47dc/lQBr/C+8mvfh5pbzsWeNWh3HuEYqP0Arf1bVf7Kijk/s++vN7bdtnD5hX3IyMCo/DmixeHfD1lpMLb1towpfH3m6k/iSTWpQBwWh3/8AaXjDxDc/ZLq1zHajyrqPY4wr84ya6WsW1/5HzxH/ANcrT/0F62qYgoorlNY8d2uj6zPpj6XqFxLCiOzwiPaQw4+84PY/lQBa13/kaPC3/X3P/wCk8ldDXl+q+P7e417QZxo2pqLaeVyrLHl8wuuFw+M/NnnHArVm+KNlbwPNJoerKiKWYlYuB/38oGd3RTY38yNXAI3AHB6inUCCiiigAooooAKKKKACszxH/wAixq3/AF5Tf+gGtOszxH/yLGrf9eU3/oBoA8msv+PC2/65L/IVV0npe/8AX3J/SrVl/wAeFt/1yX+QqrpPS9/6+5P6UwOm8Ef8j7J/2C3/APRqV6hXl/gj/kfZP+wW/wD6NSvUKQBRRRQAUUUUAFYlv/yU+D/sEv8A+jRW3WJb/wDJT4P+wS//AKNFAHZ0UUUhhRRRQB598Rvh3N4rlttV0m7FprFoNqOWKhwDkfMOVIPQ1w39jfGr/j2+13Hlfd837ZD+ec7v61s/E/U9d1rxlp3gnRbs2q3EYklYOU35yfmI52gL0711dn4FuYPhhJ4Sk1FPOeN0+0rGSBl93TP4daBlP4dfDmbwrPc6tq94LzWLpdruGLBATk/MeWJPU16FXj3wy1LW9A8aah4H1i7N2kEZkgcsW2YweCecEHp2r2GgQUUUUAeafEzw5ZQeGtY1qRpbm+leJUed9wgTzF+SMdFHr3NaHxPlceCbe0ViqXt3b20pH9xm5/lVLx03i/X9Lv8ARLPwezQPKvl3n9pQjequGB2EgjOOhNXbyy13xp4dvdI1fw+dEZUR7W4N7HPmVTkcJyOn60AbHi7R7O58B6lp5hRYIrNjEoGAhRcqR6YwK4jRryTxF4j8Bx6h+8SLSnvNrchpR8oY+4xmtO+u/HGvaO/h2Xw4LGedPIudSa6RoQh4ZkUckkdu2a0Nc8KXlhHoOo+G4klvdETyVt5H2C4hK4Zd3QHjINAEPxMQWZ8Pa1CAt5aapFGrjqUfIZfoa72vP2ttf8a61pj6rojaPpOnTC5ZJp1kkuJR90AL0UdeetegUAcn4i8SGKw1Sy/sTWXxDLH56WuYj8pG7dn7vvWt4X/5FLRf+vCD/wBFrU2u/wDIvan/ANekv/oBqHwv/wAilov/AF4Qf+i1oA1qzfEP/Itar/15zf8AoBrSrN8Q/wDItar/ANec3/oBoA8EgultdLsy0U8m6JRiKMuR8o64qlp+oeS135llfL5lwzr/AKM3IIHPT2rU0/8A5Btr/wBcU/kKs0xG98O/+Shv/wBgmX/0bFXr9eQfDv8A5KG//YJl/wDRsVev0mM8Ab/kKax/2E7v/wBHNTqa3/IU1j/sJ3f/AKOanVQifSv+Rs8Pf9hBf/QHr3avCdK/5Gzw9/2EF/8AQHr3akxnjHjP/koeq/8AXC3/APQWrJrW8Z/8lD1X/rhb/wDoLVk0IRn67/yAr3/ri1fRi/dH0r5z13/kBXv/AFxavoxfuj6UMZ5j8Vf+Q74b/wCuV3/7Rrka674q/wDId8N/9crv/wBo1yNCAK9U+G//ACT3Rv8Arif/AEJq8rr1T4b/APJPdG/64n/0JqGB1NcL43sItT8XeEbOZ5FhkluA4jcqWXyxlcjnB6HHau6rF13wzZ6/NaTzz3MFxZlmt5beQK0bNj5uh5GPpycg0gOf02xg8NfElNJ0pTDp19p7XElqrEpHIjgblB6ZBwa7qsPRvDUel39xqVzfXWo6jOgja5udoKoOQqqoAUZ56cmtygAooooA5n4f/wDIlWP+/N/6Oeumrmfh/wD8iVY/783/AKOeumoAKKKKACsfxR4dtfFPh+50i7JRJgNsi9UYchh9DWxXH/E3xHdeF/BNzfWJ23UjrBE+M7C38X1ABoA80Xwb8V/DP+g6JqElzZIf3flXSBQPZZCNv0FaOh/DDxTr+tW2p+OdSaSG3YMts03ms2DnHHyqPXHWtr4V+D9a0ud/Emr6kLmTUrUHy2ZncAkMCzHvjt71zPiu01/4aeMk8SW2p+fp2pXrGW3GVByclWXJB46N7dqBnuwGBgdKKajiSNXHRgCKdQIKyda8PWmvtbJfyTNawuXa1V9scx4x5g/iAx06VrVia/qmuac0A0fw4dXDg+YRexweX6ff659vSgDA+GOIfDeq7FACapdYUDAGG6UfCmNZfCUmpyANd6hdzTXEh6sd5AB+gFZ3gw+MdBjksbrwa3kXV/JcSXH9pQ/ulkbJ+UElsD86nsofEfgWe90/TtBfWdJnnae0aG4WNoCxyUYN2z3oA5jV7mXQbH4jaXYkx2wlgkjC8CPzsB8enWvT/wCwrBvBY0XyE+xmz8rZjjG3r9c859awNM8E3F94d15deZE1LXmMk4iO4QYH7tQe+3iqi3/jxNE/4R7/AIR1WvhF9mGq/ak8jbjHmY+9nHbrn8qANf4X3k178PNLedizxq0O49wjFR+gFa2qeIDpl39n/sfVrv5Q3mWltvTntnI5qXw5osXh3w9ZaTC29baMKXx95upP4kk1qUAcL4Vm+0z6/P5UkXmao7+XKu10zFEcMOxHeuirC8Pf8hHxL/2F5P8A0XFW7TEFcr8R/wDkQ9Q/3of/AEcldVXK/Ef/AJEPUP8Aeh/9HJQB59cxzSxbYZzA+c7gobj0wazo9KvorieYaq26bbn9wvYYrXopgUtRBGnAM25g8eTjGfnWvcq8O1P/AI8T/wBdI/8A0Na9xpAc18QP+RE1b/rkP/QhXn9egfED/kRNW/65D/0IV51cW6XMXlu0ijOcxuVP5igCnaf8hvUf92L+Ro13/kBXn/XI01dCtFkeQSXQZ8biLl8nHTvS60gj8P3SAkhYcAk5P50DPd6KKKBHD6foHiPSrK+0ezTTvs9zNLINRaVvMAck/NHt+ZhnGd2Kf/wierR6H4at7e5tEvNHO9i25kkIUgDoDg55Pb3rq9QjvZbRl0+4ht7gkbZJojIoHf5Qw/nXLW03i2fX9Q03+19MH2WGGXf/AGexDeZv4x5nGNnqetAy7YaVqt74kj1vWY7W3NtA0NtbW8pkwWI3OzFR1xgDFdNUVusyW0S3MiSThAJHRdqs2OSBk4Htk1LQIKKKKAPOfDk/iJLXXDpEFgYodTuWP2ouWmbdnau0jb9Tnk9K7XQdXi17Q7XU4kMazpkoTyjA4I/Ag1y+mjxB4dh1O3XQpLz7ZdzT2zwzRgJuY4EmSMDocjPXHWuh8LaO+heG7PT5XDzRqTIy9C7Esce2TQM2KKKKBBWT4c/5HfxN/wBcbP8A9BkrWrJ8Of8AI7+Jv+uNn/6DJQM66iiikBh3v/I66N/15Xn/AKFBW5WHe/8AI66N/wBeV5/6FBW5QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBh61/yHvDf/AF+S/wDpNLW5WHrX/Ie8N/8AX5L/AOk0tblABWB4z8Mx+LfDF1pLyCJ5MPFIRkK45BPt2/Gt+igD56ivfi34OiXSorS8ubaH5YiloLlQvbDAE49jVzTfC3jv4ha3ZXPi4T22mWr79k0YiJ9VVAAcnHUiveaKAEACgADAHApaKKACiiigAooooAKKKKACiiigDmfBP/Hhq3/YZvv/AEe1dNXM+Cf+PDVv+wzff+j2rpqACiiigDmvH9ldaj4I1G0soHnuZQixxoMljvWsLWL6XxaukaLa6ZqEE8V5DPetcWzxpbrGckbyNrEngbSc12GrawukJEzWOoXfmEjFnbNKVx646daxbHx9aag8qwaJr/7qdoHJ09sBgcHJBOPx5oA6yiiigDhvir/yK9p/2EYP5mvO69E+Kv8AyK9p/wBhGD+ZrzumgCkh/wCQ3of/AGFbT/0atLSQ/wDIb0P/ALCtp/6NWgR79XPeM/ENz4Z0Jb+0tYrmQ3EUOyWQoMO2M5APtXQ1xPxT/wCRPj/6/wC2/wDRgpDML/hZ+vf9ALT/APwNf/43WPofxB1qzu9adNHsZGudQMzhrpl2Hy41wPkORhRzx16VkyzxQKGmlSME4BdgP51l6bfWiz6iTdQANdEgmQcjYtOwHocfxP1o3dpFLoliiT3MUBZbt2I3uFzjYPWvUq+fj/x/aZ/2EbX/ANHJX0DSA5nx/ql/o3g27vtMuBb3aSQqkhQPjdKing8HgmvPf+Ex8X/9BqD/AMAU/wAa7f4o/wDIgXv/AF3tv/R8deZ00Aab4o8UQ61rU0WrxLJPNEZSbNCGYRKAQO3AA/CtGbxp4vjMP/E6hw08SHFkg4aRVP6Gudsf+Qpqn/XVP/Ra1auult/192//AKNSgD6EooopAFcz4e/5Gvxd/wBfsH/pNFXTVzPh7/ka/F3/AF+wf+k0VAHTUUUUAFFFFABWB4z8Mx+LfDF1pLyCJ5MPFIRkK45BPt2/Gt+igD56ivfi34OiXSorS8ubaH5YiloLlQvbDAE49jVzTfC3jv4ha3ZXPi4T22mWr79k0YiJ9VVAAcnHUiveaKAEACgADAHApaKKACiiigAooooAKKKKACiiigDjbX/kfPEf/XK0/wDQXrarFtf+R88R/wDXK0/9BetqmIK8q8Vf8j3qP/Xtb/8As9eq15V4rIHjrUiTgC1t8n/vugDnr7/kKaX/ANdX/wDRbU7W/wDkB33/AFwb+VUr3VNPfUdNdb23KpK5YiQYUeWw5/GrGp3MF1oF+9vNHKgicFkYMAcdOKBnusH/AB7x/wC4P5VJUcH/AB7x/wC4P5VJQIKKKKACiiigAorH1a51+3kZtNstOmtlj3Fri5eN885GAhGOneqXh7VvEOr2thfXOn6bFY3USylo7p2kAZcj5SmM9O9AHS1meI/+RY1b/rym/wDQDWnWZ4j/AORY1b/rym/9ANAHk1l/x4W3/XJf5Cquk9L3/r7k/pVqy/48Lb/rkv8AIVV0npe/9fcn9KYHTeCP+R9k/wCwW/8A6NSvUK8v8Ef8j7J/2C3/APRqV6hSAKKKKACiiigArEt/+Snwf9gl/wD0aK26xLf/AJKfB/2CX/8ARooA7OiiikMKKKKAPMvib4G1fVtRs/EnhqUpq1mu0orhWcAkgqTxkZPB6iuL/wCEu+MKn7L/AGbfmTp5n9lj887dtfQNFAHmHwy8D6xpuqXnifxNIW1a7UqsbMGZQcEliOMnAGB0Fen0UUAFFFFABRRRQAUUUUAFFFFAGfrv/Ivan/16S/8AoBqHwv8A8ilov/XhB/6LWptd/wCRe1P/AK9Jf/QDUPhf/kUtF/68IP8A0WtAGtWb4h/5FrVf+vOb/wBANaVZviH/AJFrVf8Arzm/9ANAHhen/wDINtf+uKfyFWaraf8A8g21/wCuKfyFWaoRvfDv/kob/wDYJl/9GxV6/XkHw7/5KG//AGCZf/RsVev1LGeAN/yFNY/7Cd3/AOjmp1Nb/kKax/2E7v8A9HNTqoRPpX/I2eHv+wgv/oD17tXhOlf8jZ4e/wCwgv8A6A9e7UmM8Y8Z/wDJQ9V/64W//oLVk1reM/8Akoeq/wDXC3/9BasmhCM/Xf8AkBXv/XFq+jF+6PpXznrv/ICvf+uLV9GL90fShjPMfir/AMh3w3/1yu//AGjXI113xV/5Dvhv/rld/wDtGuRoQBXqnw3/AOSe6N/1xP8A6E1eV16p8N/+Se6N/wBcT/6E1DA6mue8ReGtM1OG6vrlbv7QluwBivZol4BI+VHC/pWf8S7y9sfC8U1hez2cxvYEMkBAYqzYI5B45/SvNrvVvEEllOj+JdUZWjYEboxkY9kzSA9I8D+GtNh0DQdXUXhvXsYZWZ76Z1LNENx2Fyvc8YwO1dlXz9o+q69HolgkPiLUooltowkaGPCDaMAZToK9H+GF/qF/p2rnUL+4vHiv/LR52BIXyozgYAA5JoA7qiiigDmfh/8A8iVY/wC/N/6Oeumrmfh//wAiVY/783/o566agAooooAKwPGfhmPxb4YutJeQRPJh4pCMhXHIJ9u341v0UAfPUV78W/B0S6VFaXlzbQ/LEUtBcqF7YYAnHsauab4W8d/ELW7K58XCe20y1ffsmjERPqqoADk46kV7zRQAgAUAAYA4FLRRQAUUUUAFFFFABRRRQAUUUUAcV4e/5CPiX/sLyf8AouKt2sLw9/yEfEv/AGF5P/RcVbtMQVyvxH/5EPUP96H/ANHJXVVyvxH/AORD1D/eh/8ARyUAcJRRRTAp6n/x4n/rpH/6Gte414dqf/Hif+ukf/oa17jSA5r4gf8AIiat/wBch/6EK8/r0D4gf8iJq3/XIf8AoQrz+gArO13/AJAV5/1yNaNZ2u/8gK8/65GmB7tVPVtTt9G0q51G73+Rbpvfy13Nj2FXK53x5/yIet/9er0gKf8AwsfQf+eepf8AgBL/AIVi2PjzR4/F+sXbR6h5U1vaomLOQnKmXORjIHzDr15rCrOtv+Q9f/8AXGH/ANnoGeiy/EzQIYXldNRCoCTmxkH6kYrr43EkauucMARkYrw3Wv8AkB33/XB/5V7hb/8AHtF/uD+VAiSq99cTWtnJNb2cl3KuNsEbKrNyBwWIHHXk9qZqd/HpWlXmoSozx2sLzMqdSFBJAz34rkV+JtqyhhoWrYIyOIv/AI5QBeXxbqTapJp48Kah9oSFZ2T7RBkKzMoP38dVPfPHSultJpZ7WOWa3e2kZctC7KWQ+hKkj8jXmsfj+BfGdzff2NqZV9Pih8vEW4FZJDn7+MHd654NasvxQtIYXlfQtWCIpZiRFwBz/foGd3RUVrOLq0huAjIJY1cK2MrkZwcd6loEFZPhz/kd/E3/AFxs/wD0GStasnw5/wAjv4m/642f/oMlAzrqKKKQGHe/8jro3/Xlef8AoUFblYd7/wAjro3/AF5Xn/oUFblABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAGHrX/Ie8N/8AX5L/AOk0tblYetf8h7w3/wBfkv8A6TS1uUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHM+Cf+PDVv8AsM33/o9q6auZ8E/8eGrf9hm+/wDR7V01ABUNzd21mivdXEUCswRWlcKC3oM9+DU1ee/F+KObw9pKSorodVjyrDIP7uSgDtv7W07/AKCFr/3+X/Gua8G6pp4j1wG/tR/xObo8zL/eHvXk/wDZWnf8+Fr/AN+V/wAKo6XptjIt3vsrZtt1IozEpwM9OlOwH0WNV05pEjGoWpeRgqKJlyxPYDPJq5Xz9pWn2UXinQGjs7dG/tKLlYlB7n09QK+gaQHDfFX/AJFe0/7CMH8zXndeifFX/kV7T/sIwfzNed00AUkP/Ib0P/sK2n/o1aWkh/5Deh/9hW0/9GrQI9+rifin/wAifH/1/wBt/wCjBXbVxPxT/wCRPj/6/wC2/wDRgpDPNZYYpgBLGkgHIDqDUI0+yXOLO3GTk4iXn9Ks0VQiM/8AH9pn/YRtf/RyV9A18/H/AI/tM/7CNr/6OSvoGkxnHfFH/kQL3/rvbf8Ao+OvLbm5S1jDukrAnGI4y5/IV6l8Uf8AkQL3/rvbf+j468zoQGFaalHFfX8jW94ElkRkP2Z+QEUenqK1bg7ltSM83VueRj/lqlWKguult/192/8A6NSgD6EooopAFcz4e/5Gvxd/1+wf+k0VdNXM+Hv+Rr8Xf9fsH/pNFQB01FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAcba/8j54j/65Wn/oL1tVi2v/ACPniP8A65Wn/oL1tUxBXlXir/ke9R/69rf/ANnr1WvKvFX/ACPeo/8AXtb/APs9AGX5EJIPlJx0+UVT1lVTQr4KoA8l+gx2rQqhrf8AyA77/rg38qYHuEH/AB7x/wC4P5VJUcH/AB7x/wC4P5VJSAKK5fXvEWu6JBc3Y8P209pE6qrjUdruGYKDt8vjkjjNaumXesXErrqWlW9nGFyrRXnnFj6Y2LigDTooooAhvP8Ajyn/AOubfyrK8G/8iToX/XhB/wCgCtW8/wCPKf8A65t/Ksrwb/yJOhf9eEH/AKAKANuszxH/AMixq3/XlN/6Aa06zPEf/Isat/15Tf8AoBoA8msv+PC2/wCuS/yFVdJ6Xv8A19yf0p0cd1Jp1p9muEhIjXcXi354HuKgttN1G2Mm3UoiJJDIwNt3PXHzUDOr8Ef8j7J/2C3/APRqV6hXl/gj/kfZP+wW/wD6NSvUKBBRVa/hup7N47K6W1uDjbM0QkC88/KSM8cVyUEfiqTxReaYfE0PlwWsMwP9mpyXZx/e/wBj179u4B21FRWqTRWsSXEwnmVQHlCbA59cdqloAKxLf/kp8H/YJf8A9GitusS3/wCSnwf9gl//AEaKAOzooopDCiiigAooooAKKKKACiiigAooooAKKKKACiiigDP13/kXtT/69Jf/AEA1D4X/AORS0X/rwg/9FrU2u/8AIvan/wBekv8A6Aah8L/8ilov/XhB/wCi1oA1qzfEP/Itar/15zf+gGtKs3xD/wAi1qv/AF5zf+gGgDwvT/8AkG2v/XFP5CrNVtP/AOQba/8AXFP5CrNUI3vh3/yUN/8AsEy/+jYq9fryD4d/8lDf/sEy/wDo2KvX6ljPAG/5Cmsf9hO7/wDRzU6mt/yFNY/7Cd3/AOjmp1UIn0r/AJGzw9/2EF/9Aevdq8J0r/kbPD3/AGEF/wDQHr3akxnjHjP/AJKHqv8A1wt//QWrJrW8Z/8AJQ9V/wCuFv8A+gtWTQhGfrv/ACAr3/ri1fRi/dH0r5z13/kBXv8A1xavoxfuj6UMZ5j8Vf8AkO+G/wDrld/+0a5Guu+Kv/Id8N/9crv/ANo1yNCAK9U+G/8AyT3Rv+uJ/wDQmryuvVPhv/yT3Rv+uJ/9CahgUvip/wAijD/2ELb/ANDFeV3ltezlhBepDGy7dpg3H65yK9U+Kn/Iow/9hC2/9DFecUIDOsbK9tFgie+SSCJAmwQbSQBgc7q9N+E//IO1z/sJf+0Yq4Ou8+E//IO1z/sJf+0YqGB6DRRRSA5n4f8A/IlWP+/N/wCjnrpq5n4f/wDIlWP+/N/6OeumoAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDivD3/IR8S/9heT/ANFxVu1heHv+Qj4l/wCwvJ/6LirdpiCuV+I//Ih6h/vQ/wDo5K6quV+I/wDyIeof70P/AKOSgDhKKKKYFPU/+PE/9dI//Q1r3GvDtT/48T/10j/9DWvcaQHNfED/AJETVv8ArkP/AEIV5/XoHxA/5ETVv+uQ/wDQhXn9ABWdrv8AyArz/rka0aztd/5AV5/1yNMD3aud8ef8iHrf/Xq9dFXO+PP+RD1v/r1ekB5vcW8d1CYpQxQ/3XKn8wQapDQdPDlxHMGbGT9okyf/AB6tKimBnatGsXh+7jTO1bdgMkk4x6mvcrf/AI9ov9wfyrw/Wv8AkB33/XB/5V7hb/8AHtF/uD+VIDJ8Yf8AIla7/wBg+f8A9FtXlnmmGxSQRSSkIvyRgFj06ZIr1Pxh/wAiVrv/AGD5/wD0W1eYQf8AHvH/ALg/lQBjreTjWJLn+zL7Y1ukY+Rc5DMf72O9XdRkMug3jmN4y1tIdr9R8p61fqnq3/IGvv8Ar3k/9BNMZ7Ppv/ILtP8Argn/AKCKtVV03/kF2n/XBP8A0EVapCCsnw5/yO/ib/rjZ/8AoMla1ZPhz/kd/E3/AFxs/wD0GSgZ11FFFIDDvf8AkddG/wCvK8/9CgrcrDvf+R10b/ryvP8A0KCtygAooooAKKKKACiiigAooooAKKKKACiiigDD1r/kPeG/+vyX/wBJpa3Kw9a/5D3hv/r8l/8ASaWtygAooooAKKKKACiiigAooooAKKKKACiiigAooooA5nwT/wAeGrf9hm+/9HtXTVzPgn/jw1b/ALDN9/6PaumoAK4D4tf8gHSP+wrF/wCi5K7+uA+LX/IB0j/sKxf+i5KAPObq2e4ChLqeDb3i28/XINU4NGNuWMepXoDuZGG5OSev8NalFUIl07/kZ9A/7CUX9a94rwfTv+Rn0D/sJRf1r3ikxnDfFX/kV7T/ALCMH8zXndeifFX/AJFe0/7CMH8zXndCAKSH/kN6H/2FbT/0atLSQ/8AIb0P/sK2n/o1aBHv1cT8U/8AkT4/+v8Atv8A0YK7auJ+Kf8AyJ8f/X/bf+jBSGecUUUVQiM/8f2mf9hG1/8ARyV9A18/H/j+0z/sI2v/AKOSvoGkxnHfFH/kQL3/AK723/o+OvM69M+KP/IgXv8A13tv/R8deZ0IAqC66W3/AF92/wD6NSp6guult/192/8A6NSmI+hKKKKkYVzPh7/ka/F3/X7B/wCk0VdNXM+Hv+Rr8Xf9fsH/AKTRUAdNRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHG2v/ACPniP8A65Wn/oL1tVi2v/I+eI/+uVp/6C9bVMQV5V4q/wCR71H/AK9rf/2evVa8q8Vf8j3qP/Xtb/8As9AGfVDW/wDkB33/AFwb+VX6oa3/AMgO+/64N/KmB7hB/wAe8f8AuD+VQXuq6dprRrf39ramXPlieZU34xnGTzjI/Op4P+PeP/cH8q89+JcUcuteHhJGrgJdcMM9o6QGn4z8QaLP4Vuo4dXsJHMkJCpcoTxKhPAPpW//AMJNoP8A0G9N/wDAuP8AxrxjW7S2GkykW8QO5OiD++Kv/Y7X/n2h/wC+BQM9mtLy2v7ZLmzuIbi3fO2WFw6tg4OCODyCK5T4mRJN4SCSLuU3luMfWQD+RNWvh2ip4D0tUUKu2TgDA/1jVB8R/wDkVV/6/bb/ANGrQI82m0XTjBJ/oq/dPc+n1qtpOkWEmkWbtbKWaFWY5PJIya2Jv9RJ/un+VVdH/wCQJY/9cE/9BFAHafC+3itoNbSFAii9UAD/AK5If6mur8R/8ixq3/XlN/6Aa5j4bf6vXP8Ar9X/ANFJXT+I/wDkWNW/68pv/QDQB5NZf8eFt/1yX+QqeoLL/jwtv+uS/wAhU9MDV8Ef8j7J/wBgt/8A0aleoV5f4I/5H2T/ALBb/wDo1K9QpAeTjxZ4pe4uwmp2ypHdTRIGswSFWRlHIYdhWbB4h8SL4nvLhdTtvPe1iRm+xjBUM+Bjdxgluc9/aodsznUFglWKT7fcYdk3Afvm7ZFVF03Ulu3uRqUPmOiof9F4wCSP4vc0DOs07xR4lk8QaTbXOo20kFxdCORUtApK7GPXcfT/AD0r0+vHNKDr4k8PCVw7i9G5guAT5cnbtXsdAgrEt/8Akp8H/YJf/wBGitusS3/5KfB/2CX/APRooA7OiiikMKKKKACiiigAooooAKKKKACiiigAooooAKKKKAM/Xf8AkXtT/wCvSX/0A1D4X/5FLRf+vCD/ANFrU2u/8i9qf/XpL/6Aah8L/wDIpaL/ANeEH/otaANas3xD/wAi1qv/AF5zf+gGtKs3xD/yLWq/9ec3/oBoA8L0/wD5Btr/ANcU/kKs1W0//kG2v/XFP5CrNUI3vh3/AMlDf/sEy/8Ao2KvX68g+Hf/ACUN/wDsEy/+jYq9fqWM8Ab/AJCmsf8AYTu//RzU6mt/yFNY/wCwnd/+jmp1UIn0r/kbPD3/AGEF/wDQHr3avCdK/wCRs8Pf9hBf/QHr3akxnjHjP/koeq/9cLf/ANBasmtbxn/yUPVf+uFv/wCgtWTQhGfrv/ICvf8Ari1fRi/dH0r5z13/AJAV7/1xavoxfuj6UMZ5j8Vf+Q74b/65Xf8A7Rrka674q/8AId8N/wDXK7/9o1yNCAK9U+G//JPdG/64n/0Jq8rr1T4b/wDJPdG/64n/ANCahgUvip/yKMP/AGELb/0MV5xXo/xU/wCRRh/7CFt/6GK84oQBXefCf/kHa5/2Ev8A2jFXB13nwn/5B2uf9hL/ANoxUMD0GiiikBzPw/8A+RKsf9+b/wBHPXTVzPw//wCRKsf9+b/0c9dNQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHFeHv+Qj4l/7C8n/AKLirdrC8Pf8hHxL/wBheT/0XFW7TEFcr8R/+RD1D/eh/wDRyV1Vcr8R/wDkQ9Q/3of/AEclAHCUUUUwKep/8eJ/66R/+hrXuNeHan/x4n/rpH/6Gte40gOa+IH/ACImrf8AXIf+hCvP69A+IH/Iiat/1yH/AKEK8/oAKztd/wCQFef9cjWjWdrv/ICvP+uRpge7Vzvjz/kQ9b/69Xroq53x5/yIet/9er0gPPKKKKYFDWv+QHff9cH/AJV7hb/8e0X+4P5V4frX/IDvv+uD/wAq9wt/+PaL/cH8qQGT4w/5ErXf+wfP/wCi2rzCD/j3j/3B/KvT/GH/ACJWu/8AYPn/APRbV5hB/wAe8f8AuD+VAElU9W/5A19/17yf+gmrlU9W/wCQNff9e8n/AKCaYHs+m/8AILtP+uCf+girVVdN/wCQXaf9cE/9BFWqQBWT4c/5HfxN/wBcbP8A9BkrWrJ8Of8AI7+Jv+uNn/6DJQM66iiikBh3v/I66N/15Xn/AKFBW5WHe/8AI66N/wBeV5/6FBW5QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBh61/yHvDf/AF+S/wDpNLW5WHrX/Ie8N/8AX5L/AOk0tblABRRRQAUUUUAFFFFABRRRQAUUUUAFFYvi7XD4c8K6hqqKGkgi/dqehcnC/qRXHnwHqB8P/wBsf8JDq3/CSeT9o877SfL343bNnTZ2x/8AqoA9KorD8Ia6fEnhTT9VdQss0f71R0Dg4b9Qa3KAOZ8E/wDHhq3/AGGb7/0e1dNXM+Cf+PDVv+wzff8Ao9q6agArgPi1/wAgHSP+wrF/6Lkrv64D4tf8gHSP+wrF/wCi5KAOBoooqhEunf8AIz6B/wBhKL+te8V4Pp3/ACM+gf8AYSi/rXvFJjOG+Kv/ACK9p/2EYP5mvO69E+Kv/Ir2n/YRg/ma87oQBSQ/8hvQ/wDsK2n/AKNWlpIf+Q3of/YVtP8A0atAj36uJ+Kf/Inx/wDX/bf+jBXbVxPxT/5E+P8A6/7b/wBGCkM84oooqhEZ/wCP7TP+wja/+jkr6Br5+P8Ax/aZ/wBhG1/9HJX0DSYzjvij/wAiBe/9d7b/ANHx15nXpnxR/wCRAvf+u9t/6PjrzOhAFQXXS2/6+7f/ANGpU9QXXS2/6+7f/wBGpTEfQlFFFSMK5nw9/wAjX4u/6/YP/SaKumrmfD3/ACNfi7/r9g/9JoqAOmooooAKKKKACiiigAooooAKKKKACiiigAooooAKKxfF2uHw54V1DVUUNJBF+7U9C5OF/UiuPPgPUD4f/tj/AISHVv8AhJPJ+0ed9pPl78btmzps7Y//AFUAelUVh+ENdPiTwpp+quoWWaP96o6BwcN+oNblAHG2v/I+eI/+uVp/6C9bVYtr/wAj54j/AOuVp/6C9bVMQV5V4q/5HvUf+va3/wDZ69VryrxV/wAj3qP/AF7W/wD7PQBn1Q1v/kB33/XBv5Vfqhrf/IDvv+uDfypge4Qf8e8f+4P5VwHxH/5Dfh7/AHLr+Udd/B/x7x/7g/lXAfEf/kN+Hv8Acuv5R0gOJv8ATLy9jkjGpGON2BC+Qp24OR/KrVrBdROxnvDOCOAYwuPyq1RTA7/4ff8AIi6Z/uv/AOjGqv8AEf8A5FVf+v22/wDRq1Y+H3/Ii6Z/uv8A+jGqv8R/+RVX/r9tv/Rq0hnnd5cXSFo4bCScFfvrIgGfTkg1X0qS8htbW0m0+WMRxqjSGRCBgegOa1aKYjq/ht/q9c/6/V/9FJXT+I/+RY1b/rym/wDQDXMfDb/V65/1+r/6KSun8R/8ixq3/XlN/wCgGkB5NZf8eFt/1yX+QqeoLL/jwtv+uS/yFT0wNXwR/wAj7J/2C3/9GpXqFeX+CP8AkfZP+wW//o1K9QpAeJwf6+//AOv65/8ARrVPUEH+vv8A/r+uf/RrVPTAk07/AJGjQP8Ar+/9pyV7DXj2nf8AI0aB/wBf3/tOSvYaQBWJb/8AJT4P+wS//o0Vt1iW/wDyU+D/ALBL/wDo0UAdnRRRSGFFFFABRRRQAUUUUAFFFFABRRRQAUUVxPjG8vdR8Q6R4TsLyayF8rz3dxA22RYU/hU9iTxmgDtqK81vtPl+HetaRe2Go38+k3tytneW13OZQrP92RSehz1r0qgDP13/AJF7U/8Ar0l/9ANQ+F/+RS0X/rwg/wDRa1Nrv/Ivan/16S/+gGofC/8AyKWi/wDXhB/6LWgDWrN8Q/8AItar/wBec3/oBrSrN8Q/8i1qv/XnN/6AaAPC9P8A+Qba/wDXFP5CrNVtP/5Btr/1xT+QqzVCN74d/wDJQ3/7BMv/AKNir1+vIPh3/wAlDf8A7BMv/o2KvX6ljPAG/wCQprH/AGE7v/0c1Oprf8hTWP8AsJ3f/o5qdVCJ9K/5Gzw9/wBhBf8A0B692rwnSv8AkbPD3/YQX/0B692pMZ4x4z/5KHqv/XC3/wDQWrJrW8Z/8lD1X/rhb/8AoLVk0IRn67/yAr3/AK4tX0Yv3R9K+c9d/wCQFe/9cWr6MX7o+lDGeY/FX/kO+G/+uV3/AO0a5Guu+Kv/ACHfDf8A1yu//aNcjQgCvVPhv/yT3Rv+uJ/9CavK69U+G/8AyT3Rv+uJ/wDQmoYFL4qf8ijD/wBhC2/9DFecV6P8VP8AkUYf+whbf+hivOKEAV3nwn/5B2uf9hL/ANoxVwdd58J/+Qdrn/YS/wDaMVDA9BooopAcz8P/APkSrH/fm/8ARz101cz8P/8AkSrH/fm/9HPXTUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFYvi7XD4c8K6hqqKGkgi/dqehcnC/qRXHnwHqB8P/wBsf8JDq3/CSeT9o877SfL343bNnTZ2x/8AqoA9KorD8Ia6fEnhTT9VdQss0f71R0Dg4b9Qa3KAOK8Pf8hHxL/2F5P/AEXFW7WF4e/5CPiX/sLyf+i4q3aYgrlfiP8A8iHqH+9D/wCjkrqq5X4j/wDIh6h/vQ/+jkoA4SiiimBT1P8A48T/ANdI/wD0Na9xrw7U/wDjxP8A10j/APQ1r3GkBzXxA/5ETVv+uQ/9CFef16B8QP8AkRNW/wCuQ/8AQhXn9ABWdrv/ACArz/rka0aztd/5AV5/1yNMD3aud8ef8iHrf/Xq9dFXO+PP+RD1v/r1ekB55RRRTAoa1/yA77/rg/8AKvcLf/j2i/3B/KvD9a/5Ad9/1wf+Ve4W/wDx7Rf7g/lSAyfGH/Ila7/2D5//AEW1eYQf8e8f+4P5V6f4w/5ErXf+wfP/AOi2rzCD/j3j/wBwfyoAkqnq3/IGvv8Ar3k/9BNXKp6t/wAga+/695P/AEE0wPZ9N/5Bdp/1wT/0EVaqrpv/ACC7T/rgn/oIq1SAKyfDn/I7+Jv+uNn/AOgyVrVk+HP+R38Tf9cbP/0GSgZ11FFFIDDvf+R10b/ryvP/AEKCtysO9/5HXRv+vK8/9CgrcoAKKKKACiiigAooooAKKKKACiiigAooooAw9a/5D3hv/r8l/wDSaWtysPWv+Q94b/6/Jf8A0mlrcoAKKKKACiiigAooooAKKKKACiiigDmPiFpU+teBdUs7VC9x5YkjQdWKENj8cVUTx/oP/CEjVTqFuHFtg25kHm+Ztxs29c54rsqxz4U8PnVP7TOi2Bvd27z/ACF3bvXp19+tAGd8OtKn0bwLplrdIUuChldD1UuxbH4ZrW1fw/pWu+T/AGnZrceTu8vczDbnGehHoK06KAOXj+HPhKLds0aIbmLH94/JPf71O/4V94V/6BEf/fx//iq6aigDmf8AhX3hX/oER/8Afx//AIqmv8OvCUgAfRYWAORl3PP511FVdRvG0/T57tbW4umiXcILdd0j+yjuaAMD/hW/hD/oBwf99P8A41i+FPh14fk8OW76poSi8Ly7xNvVseY23Iz/AHcVM3xOCXqWTeEPEv2p0LrD9kXcVHfG/p71Zs/iIlzrdnpU/hrXbKe7bEbXVuqL7nO7nHfGaALf/Ct/CGQf7DgyDkHc/H60/wD4V94V/wCgRH/38f8A+KrpqKAOXf4d+E5Bh9GhYA5wXc/+zUn/AArfwh/0A4P++n/xrf1G8bT9Pnu1tbi6aJdwgt13SP7KO5rjG+JwS9Sybwh4l+1OhdYfsi7io7439PegCHwr8OvD8mgq+p6EoujcXAPnb1bYJnCcZ6bAuPbBrZ/4Vv4PyD/YcGQcj5n4P51Vs/iIlzrdnpU/hrXbKe7bEbXVuqL7nO7nHfGa7SgDmf8AhX3hX/oER/8Afx//AIqkb4eeE3A3aNEcHIzI/X/vqunooA5n/hX3hX/oER/9/H/+KrF8M+ANEezvm1DRiJf7RuhH5rSKTEJWEeOem0DB7iumk8RxDxK+jR20snkW/wBourgEBIAfug9yTg9K5vSviDqF9c2V3c6CbfQdQuPs1pe+eGcsSQpZMcBiKANdvh54TcYbRoiM55kf/wCKpf8AhX3hX/oER/8Afx//AIqumooA5h/h34TkXa+jRMvoZHP/ALNTf+Fb+EP+gHB/30/+NdSTgE1wl78TBp7otz4S8SJ5knlR5tV/eN2C/NyaAK/hz4deH3j1M6hoShhqM6w+bvH7oN8mOeRjoa2T8NvB5/5gVv8A99P/AI1m3XxOFjGsl54R8SW8bMEDy2qKuT0BJfAruon8yJH2su5QdrdR7GgDm/8AhX3hX/oER/8Afx//AIqj/hX3hX/oER/9/H/+KrpqKAOZ/wCFfeFf+gRH/wB/H/8Aiq1dI0LTNCili0y0S2SV98gUk7mxjJyfQVo0UAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHMfELSp9a8C6pZ2qF7jyxJGg6sUIbH44qonj/AEH/AIQkaqdQtw4tsG3Mg83zNuNm3rnPFdlWOfCnh86p/aZ0WwN7u3ef5C7t3r06+/WgDO+HWlT6N4F0y1ukKXBQyuh6qXYtj8M1s6toem65FHFqVqtwkbbkBYjB/AitCigDmP8AhXfhMMW/saLcep8x+f8Ax6l/4V74U/6A8X/fx/8A4qumooA5n/hXvhT/AKA8X/fx/wD4qkHw78Jhiw0WHJ6ne/P610rtsRm2lsDOB1NcLdfE0WckUdx4R8SxvM+yJWtFzI3oo380ARX/AMP9D/4S3Rkh0XNiYLk3BUuUDjy9m454PL49ea2m+HfhNlKto0JB4ILvz+tZVx8TVszF9r8JeJLdZXEavNaoilj0GS+M13anKg4IyOh7UAc1/wAK88Kf9AeL/v4//wAVTW+HPhFyC2iQMR0y7nH611Fcxr/jFtBu5IW8O61eRRxiRrm1gVogMZPzFh070AYfiT4deH0trD+z9CUu2o2yzeVvJ8kyDfnngbc5Patr/hW/hD/oBwf99P8A41mQ/Ew3Not3D4O8USW7LuWRbNSGHqPm5FdJ4Z8Qw+J9HXU7e1ubaJ3KqlwFDHHf5SR7fhQBnr8O/CSKFXRYVA6AO4/rQ3w68JOMNosLD0Luf611FZmuau+i2IuY9MvtRJcJ5NlGHcZ74JHFAHM698OfDSeHdTex0KM3a2kpgEZct5mw7cDPJziptO+HHhU6XaG40KLzjCnmbmcHdtGc89c1Vi+JwnuprWLwh4lkngx5qLaKSmeRu+fitLQfHMeu65JpJ0PVrC4ji81/tsKxgL243E8+uMUAPX4c+Ekzt0SFc8nDuM/rSn4eeEyCDo0RB6gyP/8AFV09FAHK/wDCt/B4/wCYFb/99P8A41jeFvh14fk0PdqehKLr7TcD99vVtgmcJxnps249sV1eq+IE03VtN0uO1muru+c4SMgCONcbpGJ7DI+tczffEHUIdQ1Kaz0E3Oh6XN5F5eeeFcMMbiqY5C55oA1V+HHhFW3LokAbGMh3/wAaf/wr3wp/0B4v+/j/APxVdJHIssayIco4DKfUGnUAcr/wrfwf/wBAK355+8/+NY2mfDrw+3iTXFudCX7GrQfZd+8Ljy/n2nPPzda3Nf8AGLaDdyQt4d1q8ijjEjXNrArRAYyfmLDp3rKh+JhubRbuHwd4okt2Xcsi2akMPUfNyKANP/hW/hAEEaHBkcg7n4/Wn/8ACvfCn/QHi/7+P/8AFVe8M+IYfE+jrqdva3NtE7lVS4Chjjv8pI9vwrYoA5n/AIV74U/6A8X/AH8f/wCKq7pXhTQ9Du3utN06O3ndPLaQFiSuc45J71s0UAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFcF4rlTQfiD4f8RXZ2ac0MlhPMfuxFuULHsCe9d7UVza297bPbXUEc8Egw8cqhlYe4PWgDgPG+oWfiS+0Hw9pd1Dd3Et/HdTeQ4cRRR5JZiOntXolZuleHtH0PzP7L0y1szJ98wxBS31PWtKgDAu/BPhy+u5bq50yOSeVi7uXcbie/BqBfh54UVQq6PEABgASPx/49XTUUAcz/AMK+8K/9AiP/AL+P/wDFUH4e+FCCDo8RB6gyP/8AFV01U9Vv20zTZrxLO5vGjAIgtU3SPzjgZGaAMH/hW/g8DA0K3/76f/Gsbwx8OvD8mlTNqWhKJ/tlyF83ep8sTOE4z027ceoqb/hZw+3fYv8AhEPEputnmeSLRSwXpkjfwPerenfEFL7XrXR5fDuuWNxcAlGu4FjXA6n72SPpmgC0vw58Io25dEgVsYyHcHH507/hX3hX/oER/wDfx/8A4qumooA5b/hW/hDJP9h2+Scn5n/xrF074deH28T62lxoS/YlFv8AZS28Jyp37TnnnGau6r8Rl0eWUXXhbxCIo5fKE4tl2SHOBtJbnPb1qG6+Jv2G2NzdeEPE0EC/ekktFVV+pLcfjQBpj4b+EAQRocGRyDufj9af/wAK+8K/9AiP/v4//wAVW9Y3QvrC3uhFJEJo1kEcmNy5GcHBIzVigDlm+HHhFmLNokBY9SXfP86xbz4deHx4v0pItCX+z2tbkzld+zeDFs3HPBwXx+PpXS6/4kl0KaGNNB1fUhIpYvYwB1THZskYNYFp8ThfwGa08IeJp4gSu+O0VlJHBwQ3NAGmfht4PIwdCtyP95/8ak/4V94V/wCgRH/38f8A+Kqbwt4rh8VQXUsOn31n9mk8p1u0VW3dxgE4I7g4610FAHLv8OvCUhBfRYWI6Zdzj9axPEfw68PpFpv9n6EpZtRgWfyt5/clvnzzwMdTXodcvN42tYYNeuzZzmx0fKPc5AWaUdUUdeDgZ9TQAn/Ct/CH/QDg/wC+n/xpy/DzwmihV0aJVHQCRwP/AEKq3h7xdqt9qlvYa7oY0x72A3FmyTiUOowSrccMAQa7CgDmH+HfhNxh9GhYdcF3P9ayvEXw68Nx+GdWfT9CQ3q2cxtxEXZvM2HbtGeTnGBXZajeNp+nz3a2txdNEu4QW67pH9lHc1xjfE4JepZN4Q8S/anQusP2RdxUd8b+nvQBcsvhx4UNhbmbQ4fN8pd+5nznAznmrKfDrwlGCE0WFcnJw7jP61Ts/iIlzrdnpU/hrXbKe7bEbXVuqL7nO7nHfGa7SgDmf+FfeFf+gRH/AN/H/wDiqP8AhX3hX/oER/8Afx//AIqumooAq6dp1ppNhFY2ECwWsIIjjXoozn+ZNWqKKACiiigAooooAKKKKACiiigAooooAKKKKAOY+IWlT614F1SztUL3HliSNB1YoQ2PxxVRPH+g/wDCEjVTqFuHFtg25kHm+Ztxs29c54rsqxz4U8PnVP7TOi2Bvd27z/IXdu9enX360AZ3w60qfRvAumWt0hS4KGV0PVS7FsfhmtDVPCmh6zd/atQsEnn2hN5dhwOg4PvWzRQBzT/D/wAKyOXbR4ixABO9xnAAHf0ApP8AhXvhT/oDxf8Afx//AIqumooA5n/hXvhT/oDxf9/H/wDiqRvh34TYYbRYSPQu/wDjXT1FdTm2tJpxDJMY0LiOIZZ8DOAPU0Ac9/wr3wp/0B4v+/j/APxVYvh/4f6G9xrRvtFwo1F1t/MLrmLYmNvPIzu5+tPm+JwguYbaXwh4lWefPlRG0Xc+OuBvycVKPiSq39pZ3PhbxDavdyiKJri2RFLH3L/jjrQBot8O/CbDDaLCR6F3/wAaX/hXvhT/AKA8X/fx/wD4qumooA5hvh34SdSraLCQeoLuf603/hW/hD/oBwf99P8A411NcTqvxGXR5ZRdeFvEIijl8oTi2XZIc4G0luc9vWgClofw68PvqOui90JREt8FtfM3geV5UZ+Xnkbi/Prmtk/DbweRg6Fbke7P/jWbdfE37DbG5uvCHiaCBfvSSWiqq/UluPxrtLG6F9YW90IpIhNGsgjkxuXIzg4JGaAMH/hXvhT/AKA8X/fx/wD4qkb4d+E2Uq2iwkHqC7/4109FAHM/8K98Kf8AQHi/7+P/APFViaT8P9DbxDr4utFxarNCLQuXClfJUttOeRuLfjmup8Q6+mg21uwtZbu6uplgt7aLAaRzz1PAAAJJrC1rxpqtvrt3puh6D/aX9nQrNfO1wI9gYZCrxy2OaALzfDvwmylW0aEg8EF35/Wl/wCFeeFP+gPF/wB/H/8Aiq29L1GDV9KtdRts+TcxLKmeoBGcGrdAHMH4d+E2Uq2jREHggu/P60f8K88KD/mDRf8Afx//AIqunrmNf8YtoN3JC3h3WryKOMSNc2sCtEBjJ+YsOnegDG034f6G3ibXBcaLi0XyBali4UjZ8+055561sn4d+E2Ug6NEQeCC78/rWVD8TDc2i3cPg7xRJbsu5ZFs1IYeo+bkV0nhnxDD4n0ddTt7W5toncqqXAUMcd/lJHt+FAFAfDzwoBgaNFj/AK6P/wDFUv8Awr3wp/0B4v8Av4//AMVXTUUAcz/wr3wp/wBAeL/v4/8A8VWlo/hzSNAM50uxjtjcFTKVJJfbnGck9MmtSigAooooAw73/kddG/68rz/0KCtysO9/5HXRv+vK8/8AQoK3KACiiigAooooAKKKKACiiigAooooAKKKKAMPWv8AkPeG/wDr8l/9Jpa3Kw9a/wCQ94b/AOvyX/0mlrcoAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooqrqMV5Pp08VhdLa3bLiKZo/MCH1296AOR1SXVfDHjC91qPRrrVtOv4Ikf7EA88DJkYCH7ynOeO9VLjUtU8b6jpcFp4d1LTLO0vI7ua81KIQsNn8KLkkk9M0mqr4q0O3E+qfEPS7SMnCmbTUXcfQDdz+FW9GHiOXUrBpvHGn3kEyC4Fslgkbzw+q/NkD3xQB3NFFFABXD6pLqvhjxhe61Ho11q2nX8ESP8AYgHngZMjAQ/eU5zx3rrtRivJ9OnisLpbW7ZcRTNH5gQ+u3vXC6qvirQ7cT6p8Q9LtIycKZtNRdx9AN3P4UALcalqnjfUdLgtPDupaZZ2l5HdzXmpRCFhs/hRckknpmvQ64bRh4jl1Kwabxxp95BMguBbJYJG88PqvzZA98V3NABRRRQBwfifQvEEOpanfaHd6ZFb6rbLBdm+Zl8kqCodSODweh71J/wjN1Ja+GdFtJrZtF04RXM1wHy8zJ90KB2J5zmsrxNa6drXjDWLLxA+9LXTll0y1kkKxuSrb3AyNzAgD2qBGt7bSPAF3aTFdaYW9ukaOcyW7L+8DL/dA5z2NAHqlFFFABXJeLrXVINU0jXtMsTqP9nmVZrNWCuyuANyZ4LDHT3rra5XxVfaudW0jRdJvU09r8ymS9aISFAig7VVuNxz39KAMTV/FWpeJ9KutF03wbrcc93GYWl1K3EEMQPBYsSc464rvNPtmstNtbV5DI8MKRs5/iIAGa5b/hE/E3/Q+X//AIBQf4V2EaskSK7l2CgFiMbj60AOooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigArkPFUGq2Ov6Z4i07T21OO0ilgntI2AkCvg70z1IxjFda4YowVtrEcHGcGuEvrHxlpts91e+PdNtbdOsk2moqj8S1AFTWvEep+MNJuNC0zwjrVvJdr5UlxqduIIoQTy2cncR2Ar0S3iMNtFEzlyiBSx74GM153YzeJNQt7a5tviLpcsFxKYoW/s1AJHHVVy3J4r0dQQoDHJxyfWgBaxvFmlXGt+FtR021kVJ54sRlvukgg4PscY/Gtmua1rS/FV1qBl0nxJb2FptAEMlgspB7ncSKAMiLx9qNpAlpeeB/EIvkUIVtrYSQEj0kBxj8K1vBWnahZafe3OpwJa3GoXkl39lRtwgDYwuRwTxk+5rmbe88QXd9PZW3xK0ia4gjaSVI9ORtijqSQ2Biu28OC+/sdHv9Xg1WSQl0uoIhGjIcYAAJB780Aa1FFZmuWurXliI9G1KPT7neCZpLcTAr3G0ke3NAHIQalqvgjUNUgu/D2panY3d5Jdw3mmxCZ/n52umQQR0zVnS5tU8UeMLLWpNFu9I0+wgljU3qhJ7hnxxs7KMZ571narceI9Elji1L4j6VbSyY2Ryaam9vou7Nb/h+DX49Zmj1LxXZamkC4ltIbNYnRmAKkkEkcfnQB1dFFFAHLeJ9I1htVstd0GSy+22kUkLxXpYRvG+D1XkEEA1g2/hjVB4EOiWV5YXNzqtzI2pXiyZSMOcvsH8RwMdqu+NEtL/xToWla3MY9EuElZkMhjSeYY2I5GOMEkDPJrmDBpll8Mr51kFtcWOqXLaZ5TlWEokIQKM856Y54oA9dghW3t44U+7GoQfQDFSVFbNI9pC0y7ZWRS49DjmpaAMbxZpVxrfhbUdNtZFSeeLEZb7pIIOD7HGPxrnIvH2o2kCWl54H8Qi+RQhW2thJASPSQHGPwrpPFWq3GieF9Q1K1iWSeCLcit90HOMn2Gcn6VhQ+GfFVxBHNL48ut7qGPk2MITn046UAX/BWnahZafe3OpwJa3GoXkl39lRtwgDYwuRwTxk+5rpqztFsL3TrDyL/VJdSm3lvPljVDj0wvFaNABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUVT1WC+udNmi028Szu2A8ud4hIE5/unrxQByF7car4T8WapqY0S81bTdSETb7BRJNAyLt2lMgle/FRfb9U8aa5pDQeH9R0uw0+6F1LdalGIpGwCNiJknnPJ9Kh1Y+KNChWXVfiJpdmjfd87TUUt9BuyfwrR0VPEbavafavGlhfQtGLg2kdikbyxHowO7IGe+KAO1ooooA5fxnYanNHpepaVbLeT6Zdi4Noz7fOXaVIBPG4ZyKyLvxxqOp2c2n2PgjXzeTIY8XtsIoFJGMs5OMfzq5qOmeMYZLm6HjSxtLJWZwJdOTESZ4BYt2HesWzvdfv7WW6tfiVpElvFKsLyjTkCBz0GS2OaAO58N6bNo/hrTtNuJRLNbW6Ru46EgdvatSoLKO4isoI7udZ7hUAklVNodsckDtn0qegCtqFs17pt1apIY2mheMOP4SQRmuD0nxVqnhfSbbRdU8Ha3LPaRiFJtMtxPDKFGAwIIxn0NdPr+n+I72eFtE12DTY1UiRJLMTbznrkkYrk/tHiP+2U0gfEjSm1FiQLZdNRnBAyQQG44HegDd8IW2pz6rrGv6jYHTRqJiEVmzAuqoCNz44DHPT2rrawPCo1NrOabUPEFrrKu+IpbaBY1TGQwO0nJz+WK36ACvMtb8J63i90cajpcHhzUL77VLNO7LOmWDNGB905I4r02vFdftdO1Sy8T6hqj+dr9nqAihilkObeESKE2JnGCDnOO9AHog0jULzxvBqNx5EWm6ZA0VoiPueR3Ubmb+6ABgCumriHa3tviraLpsxM13ZyNqcSPuUBQBGzD+Fs8fSu3oAK4fVJdV8MeML3Wo9GutW06/giR/sQDzwMmRgIfvKc54713FcXqEuv654vvdI0/Wf7HtbGCKQtHbpJJOXzz83AUYxx3oAoXGpap431HS4LTw7qWmWdpeR3c15qUQhYbP4UXJJJ6Zr0OuVsPDXiC1v4J7jxne3UMbhnge0iUSD0JAyPwrqqACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKiukmktJktpRDOyERyFdwRscHHfB7UAch4gGq6J4ti8R2mlTatZvZ/ZZ4LbBmhw24Min7wPQgelZmq6xqnjm3i0fT/AAxq1hE08ck17qcIgEKowbKjJLNxjip9St/F2j2putS+IGmWkAON82mxqCfQZbk+1JpjeJLuTT5l8f6bcQXTFokXTkUzqp+YL82aAPQaKKKACuX8Z2GpzR6XqWlWy3k+mXYuDaM+3zl2lSATxuGciuorjNR0zxjDJc3Q8aWNpZKzOBLpyYiTPALFuw70AU7vxxqOp2c2n2PgjXzeTIY8XtsIoFJGMs5OMfzrq/DemzaP4a07TbiUSzW1ukbuOhIHb2rhrO91+/tZbq1+JWkSW8UqwvKNOQIHPQZLY5r0ayjuIrKCO7nWe4VAJJVTaHbHJA7Z9KAJ6KKKAOe8V6NqGpR2F5pMtumo6dcefCtzny5MqVZWxyMg9a5/StJ1210fXr2K40u68Q6rcASCKY+TbjG0DPUkAk471e+Ici/ZtItLy4kttIur5Yr+VHKfJtJCsw6KWABNc7ZaV4ctNM8a2MpitNJhuI3iMcpXYwhVlKnOSc8j1NAHomg6UuiaBY6Wr+YLWFY9+PvEDk/nWjWN4TuLy78I6TcX+77VJao0hYYJOOp9z1rZoAKxvFmlXGt+FtR021kVJ54sRlvukgg4PscY/GtmsfxVqtxonhfUNStYlkngi3IrfdBzjJ9hnJ+lAHNxePtRtIEtLzwP4hF8ihCttbCSAkekgOMfhWt4K07ULLT7251OBLW41C8ku/sqNuEAbGFyOCeMn3NUIfDPiq4gjml8eXW91DHybGEJz6cdK6TRbC906w8i/wBUl1KbeW8+WNUOPTC8UAaNFFFABRRRQAUUUUAYd7/yOujf9eV5/wChQVuVh3v/ACOujf8AXlef+hQVuUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAYetf8h7w3/wBfkv8A6TS1uVh61/yHvDf/AF+S/wDpNLW5QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRVLVr6TTdKuLuG2+0yxrlIfMCbznpuPAoA898TTaTY+M9Xl8SLEBPpoXSprlcxqQrb1UnhX3YPrVGz1HS9T0zwNZaVNDda9bNbszW5DNbwhf3ocjoMcYPeuu8Q6lqkhSzPgZtZtHjV28yeHYGPVdr9SPWqnh+91GyvYra3+HI0i2mcLLPDPAAg/vEKMnFAHdUUUUAFeYeJptJsfGery+JFiAn00LpU1yuY1IVt6qTwr7sH1r0LVr6TTdKuLuG2+0yxrlIfMCbznpuPArnfEOpapIUsz4GbWbR41dvMnh2Bj1Xa/Uj1oA5Gz1HS9T0zwNZaVNDda9bNbszW5DNbwhf3ocjoMcYPevXa4Xw/e6jZXsVtb/DkaRbTOFlnhngAQf3iFGTiu6oAKKKq6jcXFpp089pZteTouUt1cIZD6ZPAoA47xxfeG768TRdR8O32vXkaCUxWUBZ4FPQlwylc47Gs7wiuiaPq0Uel/D3XNOknYRtd3EG4Rg9yzOSB64pI9T8X2via51e18C3Oy8jRLmF76HkpkKytnjg4II59q6HT/EviS61CCC68FXVpBI4V52vYmEY9SByaAOsooooAK5rxpoNnrWmxPqGs3GlW1o/mmaGVI8HsSzA4I7EY610tcJ4+FkmseHrjXU36BHNJ9o3qWiWUqPLMg/u5z14oAx9MtNG1a7FnYfFLWp7g8LGL5NzfTK8/hXqMSGOJELs5VQNzdT7mvNvGOoeA7nw3PFZy6TcagyYsU08o04m/g27ORzivQdMFyNKsxef8fXkp53+/tGf1zQBaooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigArg/Gz2Ft4p0G81+NX0JFlUtKu6KO4ONjOOnTIBPQ13Mr+XE74ztUnGcVy19rusTaLZyx+DpL/wC1q3n2v2qLEQB4yTwwI54oA4Eat4fuPh/qWlwz2tzqc2pXB022tyGlEhlPluoHKjvnpivZbYSraQic5lCKHP8AtY5/WvPtPu9R0y486w+FKWcrcGSCe2RsfUCvRVJKgkYJHI9KAFrA8bQ3s/gvVotPEjXLQHasf3mH8QHuVyK36xrnWL6O81GC20h7kWkCyRMlwg85z/yzwfun3NAHnul6/wCCrXxZp09jdadZ6aujyRSo+2PDb1+VweS2M8Hk811Xw5CnR7+a1ieHS5r+WTT0ZSuITjkA9FJ3ED3rGnnvbq7+13HwkhluScmaSa2ZyfXJGa7jQr6+1DThNf6S+lzByot3lWQ7RjByvH4e1AGlRRWfquoT6eLX7PZG7ee4WIqJVQop6tz1x6DmgDyPxBcaRZWfim21cQxeIpL9ZIpLlQGkg8xdnlsf4QoPA9DXW22oaZrPxNsrnw/JFc+TZyrqV1bENGwOPLUsOGbIJHtU3iC+1K9vZbW4+HI1e2hciKaaeBg4/vAMMjNXfC9/qIuBYN4JOhWQUt5iTRFN3ptT1oA6yiimuSqMVXcQMhfX2oA5jxrqehwWcOmavpc+rveE+TY20HmyPt6sBkYxnrkVxej2/hzRdQS8sPhh4ijuFbKSSQGTYfUbpCB9a0NS1LxdPrtjrFp4GuUubVHhKyX0JWWJsZHXIOQCDz9K2F8WeLCQG8AXYz1/4mEPFAHaKdyg4IyM4PalpFJKgkYJHI9KWgCrqVl/aOm3Nn58kAmjKGWLG5c9xkEV5Ylj4Z0iUaZH8UdUtzGdoiW+TYntwuF+nau+8bRXs3gvVo9PEhuWgO0RfeI/iA99ua5/S9T+GaaBEkc2gx2wjAaG4EYk6chlb5iaAOr0DTjpmlrCdVu9UDsZFubqQOxBxgAgAY/xrUrjfhtt/sO9a1SVNJa+lbTlkBGIOMYB5253YrsqACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooqhrN/Npmly3VvaG7mXASASrGXJIGAx470AebeKZtJsvEXik+IViW6ubADSZrlcpsEZDKhPAbf8Aic1LbajperXngiDRpobrWLVYjcS2xDGG3EeHV2HTJIGD3ro/EmoanLObFvAh1qzCqweSeHYWI5G1/TpUPhy91G1vYrOH4ejRrSVv3s0U8AVeOpVOTQB21FFFAHF/EYRLaaRPqETy6LDfK+oIFLDZg7SwHVQ2Ca5W217wckPjGGe50+WxuZk+zWsRUmX9yoHloOc7u4HBrubjX9WbSpbi28MyXji6eAQLdR/PGM/vMnjB6Y61zdtcX1nd/a7X4SxQXOc+bFNbK4P1AzQB2HhKK8g8I6TFqG4XaWqCUP8AeBx0PvWzUFnNNcWUE1xbtbTOgZ4WYMYyRyuRwcVPQBV1Nbh9KvEtDtuWhcRH0facfrivINJ1XwvYT+D1SW0sL20mlGpi4IjlSTymDGQtgnLd/evVrnU7mHWI7GGwM8bW7zNMJlG1h0XaeefXpXF313qOp3AuL/4Tx3Uw6STz2zt+ZFAF/wADy2t3r/iO+0dAuiTSx+SyLtjkmCnzGQenTkdSK7isTw3f6he2siX3h59FWHasURmRww9tnAxW3QAV5j4sufCniDV57efwhqmvXFm3kzXVhAQEYfwGQMpJHpziu71zUL/TbET6dpMmqTFwpgjmWMgeuW4//XXC6Nqfi/Rbq+WHwLcvZXVw1yI2voQ8btywznkE89iPegDT8Dvpllcvp+l+CtV0SN1LvcXUGA2OgLlixPPFd1XN6Jr2u6jqHkaj4UuNMg2FvtEl3HIM9hheea6SgArhvGmhaW2oxa5qXiy90Qxp5URhuEi+oGRuOe49uldzXnWpSaJZ/Ey5n8V/Z1iktYhpkt6B5C4z5gBb5Q2cde1AEnhix0/UdQiudN+IWrap9ncO9s12jBh6Mu0HFeg15nrV14avPEmgr4Xaxm1pb1C0mnbTtg/5aeYycbcdjXplABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFV765azsLi5WPzGijZwm8LuIHTJ4GfWgDhvFEul2Xj+zu/EqR/2Y1iY7OW4XdDHPuy2c8BiuME+lcva6lpF98P9A0uwmgufEK3atZxwENLAfOJLHH3V25znqK7zWNY1eWxtYx4KfVIrmASTwtcxbYmP8BDcN9RxWZo95qOnXaLZfDBdOWVgsk0E9umFJ5J2jJx1xQB6BRRRQAVxfxGES2mkT6hE8uiw3yvqCBSw2YO0sB1UNgmu0rmrjX9WbSpbi28MyXji6e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    Tradeoff analysis for BMP alternatives based on the extrapolation of plot and field observations of environmental and production indicators to the entire farm area in tomato production for one year. Values for various management options are illustrated as a per hectare average annual rate with standard errors for marketable tomato yields (Yields), total suspended solids (TSS), nitrate (NO_3^-N), dissolved reactive phosphorus (DRP), dissolved organic carbon (DOC), soil nitrous oxide (N_2O) emissions given in carbon dioxide equivalents (CO_2 eq), and carbon dioxide emissions (CO_2). Estimates were calculated for tomato production for a winter fallow only, winter cover crops only (Cover crops), winter cover crops and tailwater ponds (cover crop + tailwater pond), or winter cover crops, tailwater ponds and a tailwater return system (cover crops + tailwater pond + Return).

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    Management optionsYield
    (Mg ha-1 y-1)    		TSS
    (Mg ha-1 y-1)    		NO3N
    (kg ha-1 y-1)    		DRP
    (kg ha-1 y-1)    		DOC
    (kg ha-1 y-1)    		N20
    (CO2 eq kg ha-1 y-1)    		CO2
    (Mg ha-1 y-1)
    Fallow rotation109.9 ± 20.422.0 ± 7.619.2 ± 8.91.4 ± 0.646.9 ± 18.0138.6 ± 45.776.0 ± 27.7
    Cover crop105.6 ± 22.28.1 ± 1.919.4 ± 4.41.3 \\pm 0.276.0 ± 13.4166.4 ± 57.838.2 ± 12.5
    Cover crop + tailwater pond105.4 ± 22.10.3 \\pm 0.122.2 ± 5.01.3 \\pm 0.280.1 ± 14.2166.5 ± 57.938.1 ± 12.5
    Cover crop + tailwater pond + return105.4 ± 22.10.0 ± 0.016.1 ± 3.80.6 ± 0.167.8 ± 11.6166.5 ± 57.938.1 ± 12.5
    ", + "rows": [ + { + "cells": [ + { + "text": "", + "is_header": true, + "structural_notes": null + }, + { + "text": "", + "is_header": true, + "structural_notes": null + }, + { + "text": "", + "is_header": true, + "structural_notes": null + }, + { + "text": "", + "is_header": true, + "structural_notes": null + }, + { + "text": "", + "is_header": true, + "structural_notes": null + }, + { + "text": "", + "is_header": true, + "structural_notes": null + }, + { + "text": "", + "is_header": true, + "structural_notes": null + }, + { + "text": "", + "is_header": true, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "Management options", + "is_header": true, + "structural_notes": null + }, + { + "text": "Yield
    (Mg ha-1 y-1)", + "is_header": true, + "structural_notes": null + }, + { + "text": "TSS
    (Mg ha-1 y-1)", + "is_header": true, + "structural_notes": null + }, + { + "text": "NO3N
    (kg ha-1 y-1)", + "is_header": true, + "structural_notes": null + }, + { + "text": "DRP
    (kg ha-1 y-1)", + "is_header": true, + "structural_notes": null + }, + { + "text": "DOC
    (kg ha-1 y-1)", + "is_header": true, + "structural_notes": null + }, + { + "text": "N20
    (CO2 eq kg ha-1 y-1)", + "is_header": true, + "structural_notes": null + }, + { + "text": "CO2
    (Mg ha-1 y-1)", + "is_header": true, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "Fallow rotation", + "is_header": false, + "structural_notes": null + }, + { + "text": "109.9 ± 20.4", + "is_header": false, + "structural_notes": null + }, + { + "text": "22.0 ± 7.6", + "is_header": false, + "structural_notes": null + }, + { + "text": "19.2 ± 8.9", + "is_header": false, + "structural_notes": null + }, + { + "text": "1.4 ± 0.6", + "is_header": false, + "structural_notes": null + }, + { + "text": "46.9 ± 18.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "138.6 ± 45.7", + "is_header": false, + "structural_notes": null + }, + { + "text": "76.0 ± 27.7", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "Cover crop", + "is_header": false, + "structural_notes": null + }, + { + "text": "105.6 ± 22.2", + "is_header": false, + "structural_notes": null + }, + { + "text": "8.1 ± 1.9", + "is_header": false, + "structural_notes": null + }, + { + "text": "19.4 ± 4.4", + "is_header": false, + "structural_notes": null + }, + { + "text": "1.3 \\pm 0.2", + "is_header": false, + "structural_notes": null + }, + { + "text": "76.0 ± 13.4", + "is_header": false, + "structural_notes": null + }, + { + "text": "166.4 ± 57.8", + "is_header": false, + "structural_notes": null + }, + { + "text": "38.2 ± 12.5", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "Cover crop + tailwater pond", + "is_header": false, + "structural_notes": null + }, + { + "text": "105.4 ± 22.1", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.3 \\pm 0.1", + "is_header": false, + "structural_notes": null + }, + { + "text": "22.2 ± 5.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "1.3 \\pm 0.2", + "is_header": false, + "structural_notes": null + }, + { + "text": "80.1 ± 14.2", + "is_header": false, + "structural_notes": null + }, + { + "text": "166.5 ± 57.9", + "is_header": false, + "structural_notes": null + }, + { + "text": "38.1 ± 12.5", + "is_header": false, + "structural_notes": null + } + ] + }, + { + "cells": [ + { + "text": "Cover crop + tailwater pond + return", + "is_header": false, + "structural_notes": null + }, + { + "text": "105.4 ± 22.1", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.0 ± 0.0", + "is_header": false, + "structural_notes": null + }, + { + "text": "16.1 ± 3.8", + "is_header": false, + "structural_notes": null + }, + { + "text": "0.6 ± 0.1", + "is_header": false, + "structural_notes": null + }, + { + "text": "67.8 ± 11.6", + "is_header": false, + "structural_notes": null + }, + { + "text": "166.5 ± 57.9", + "is_header": false, + "structural_notes": null + }, + { + "text": "38.1 ± 12.5", + "is_header": false, + "structural_notes": null + } + ] + } + ], + "cells": [], + "footnote_ids": [] + }, + { + "kind": "paragraph", + "id": "doc:87e9568f66cc74c3", + "text": "

    field the second year) at 1.43% N, could have contributed >112 kg N ha<sup>-1</sup> (100 lb N ac<sup>-1</sup>) for N mineralization. This would also mean that > 3.4 \\text{ Mg C ha}^{-1} (> 1.5 \\text{ tn C ac}^{-1}) of plant material would have been decomposing, yet CO<sub>2</sub>-C emissions were substantially lower than the year with the healthy crop. An alternative explanation for the higher N<sub>2</sub>O-N emissions could be lower N uptake given the missing plants. Lower N uptake implies less C fixation, root growth, and in turn less root C turnover, respiration in the soil, and thus lower CO<sub>2</sub>-C emissions. It should be noted that the soil NO<sub>3</sub>-N pool at the end of both production seasons was similar, suggesting that tomatoes in the second year assimilated the available N, while much of it may have been lost to denitrification and leaching in the first year due at least in part to higher prevalence of disease.

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    Management Tradeoffs. When nutrient losses from the plot and field were extrapolated to the entire production area of the farm, a broader set of tradeoffs associated with these BMPs emerged (table 7). These calculations showed that using winter cover crops across the farm, without the other BMPs, decreased the farm's TSS and DRP losses and its soil CO, emissions but substantially increased DOC losses and soil N2O emissions. Note that the increased N<sub>2</sub>O emissions (CO2 equivalents) are minor compared to the reduction in CO2 emissions. With an addition of a tailwater pond, the farm's estimated TSS losses would be further reduced, but NO, -N and DOC losses would likely increase. Although the tailwater pond at times had higher localized rates of soil N<sub>2</sub>O emissions, when these values are extrapolated to the full production area of the farm, the increase is minor (<1%) given the small area of the ponds (0.6 ha [1.5 ac]) compared to the rest of the acreage. The tailwater return

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    system increased the reduction of TSS by only 1% but greatly reduced NO<sub>3</sub><sup>-</sup>-N, DRP, and DOC losses, with essentially no effect on soil N<sub>2</sub>O and CO<sub>2</sub> emissions.

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    Thus winter cover crops alone may be an effective BMP for reducing some environmental impacts, but the farm benefits more from the addition of a tailwater pond with a tailwater return system. This exercise in scaling up from plot- to farm-level data suggests that further data collection (especially to capture extreme events), replication of these management options on multiple farms to allow for statistical analysis, and modeling over longer periods of time would help farmers make decisions about which BMPs to implement. Heavy rainfall and flooding is an example of an extreme event that influences the design of BMPs. During Rainfall Y1, runoff from rainfall exceeded the capacity of both tailwater ponds, causing overflow and discharging an unknown amount of sediment and other nutrients into the adjacent slough. Larger tailwater ponds would have greater capacity to detain discharge but would reduce the area available for crop production. To detain all the stormwater during the peak period of rainfall during this event could have required as much as a 15-fold increase in pond size. Using both cover crops and a tailwater pond as BMPs may be a useful combination for such a situation, as cover crops will likely stabilize the soil, minimizing sediment loss when the capacity of the tailwater pond is exceeded.

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    The long-term management effort and financial costs must also be considered in the context of the relative environmental quality benefits of these BMPs. The tailwater pond requires annual dredging and redistribution of sediment, and without the addition of a continually operating pump to return irrigation water to the field, the relatively short

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    detention times of small-scale tailwater systems appear to concentrate certain pollutants. Cover crop operations add additional costs in terms of seed, labor, machinery, fuel, and/or electricity. This additional mechanization also contributes to increased greenhouse gas emissions. Furthermore, organic farmers using cover crops risk problems with nutrient availability should management operations be delayed (e.g., late spring rains). Alternative nutrient inputs for organic vegetable production systems may be effective but expensive (Smukler et al. 2008). Adoption of alternative irrigation systems, such as drip lines, could substantially increase water-use efficiency and reduce nutrient losses from surface runoff, leaching, and gaseous emissions (Sanchez-Martin et al. 2008; Vazquez et al. 2005), but purchasing, maintaining, and installing drip lines is expensive (Rominger personal communication 2008).

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    Summary and Conclusions

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    This study showed that tailwater ponds with a return system are a key BMP for reducing environmental impacts in California organic vegetable production and that cover crops can provide additional or even complimentary benefits, if managed carefully. Scaling plot-level data to an entire farm can help illustrate the benefits or tradeoffs of various BMPs and enable farmers to make more informed management decisions. A more complete life cycle analysis of BMPs would further enable farmers to choose more environmentally appropriate methodologies. These BMPs require additional investment, labor, and inputs for which farmers are rarely compensated in California, and an accurate cost-benefit analysis is needed. It is clear, however, that these BMPs generate multiple environmental benefits across the organic

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    farmscape and deserve more attention by both farmers and policymakers.

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    Acknowledgements

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    We greatly appreciate the cooperation from Bruce Rominger and Rominger Brothers Farms of Yolo County, California throughout this project. Funding was provided by the USDA Cooperative State Research, Education, and Extension Service Organic Agriculture Research and Education Initiative Award 04-51106-02242, the Milton D. and Mary Miller Plant Sciences Award, the Graduate Group of Ecology Block Grants and Jastro-Shields Awards, and the Achievement Rewards for College Scientists Foundation Scholarship.

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    References

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    Canopy Architecture and Morphology of Switchgrass Populations Differing in Forage Yield

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    Daren D. Redfearn,* Kenneth J. Moore, Kenneth P. Vogel, Steven S. Waller, and Robert B. Mitchell

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    ABSTRACT

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    vitro dry matter disappearance (IVDMD), but the effects on canopy Phenotypic selection has been used to improve forage yield and in architecture and morphology are not understood. Our objectives were to determine if canopy architecture and morphology can explain genotype \\times environment (G \\times E) yield differences in switchgrass (Panicum virgatum L.) and to evaluate canopy architecture and morphology as selection criteria for increasing yield. This study was conducted in 1993 near Mead, NE, and near Ames, IA. The experimental design was a randomized complete block experiment with a split-plot arrangement of four replicates at each location. Whole plots were tiller population and subplots were sward maturity. Tiller populations were harvested on 9 June, 19 July, and 27 August at Ames and on 10 June, Tillers were separated into primary yield components and dried at 55°C to determine total forage yield and dry matter contribution of morphological components. Genotype × environment interactions occurred for total forage yield and tiller density. Previous phenotypic selection for increased forage yield and IVDMD apparently altered morphological changes within the canopy of selected switchgrass popumeasurements showed that leaf area index (LAI) has some potential as a selection criterion for increasing total forage yield. However, lations. The most apparent changes were development of additional collared leaves and internodes in some populations across locations. Although canopy architecture may not be a useful selection criterion because of variability associated with individual canopy traits, indirect selection for individual canopy traits may be most effective for modi-27 July, and 26 August at Mead and were classified morphologically. fying sward growth habits.

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    D.D. Redfearn, LSU Agric. Ctr., S.E. Res. Stn., P.O. Drawer 567, Franklinton, LA 70438; K.J. Moore, Dep. of Agronomy, Iowa State Univ., Ames, IA 50011; K.P. Vogel, USDA-ARS and Dep. of Agronomy, and S.S. Waller, Dep. of Agronomy, Univ. of Nebraska, Lincoln, NE 68583; and R.B. Mitchell, Dep. of Range, Wildlife, and Fisheries Management, Texas Tech Univ., Lubbock, TX 79409. The research reported in this article is a portion from the dissertation submitted by the senior author in partial fulfillment of the requirements for the Ph.D. degree from the Univ. of Nebraska. Research funded in part by the U.S. Dep. of Energy Biomass Fuels Program, Oak Ridge Natl. Lab. Contract no. DE-4105-900R21954. Joint contribution from the Nebraska Agric. Exp. Sh. and Iowa Agric. and Home Econ. Exp. Sh. Published as Journal Series no. 11442, Nebraska Agric. Exp. Sh. Received 6 Mar 1996. *Corresponding author (dredfearn@agetr.lsu.edu).

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    Published in Agron. J. 89:262-269 (1997).

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    with a wide range of adaptation throughout North aged, switchgrass can provide a reliable source of forage during the summer, when cool-season grasses are low in grown in Kentucky. Persistence was not affected by 58%, but a single harvest during the active growing WITCHGRASS is a native, perennial warm-season grass America (Stubbendieck et al., 1992). If properly manswitchgrass harvested at early head emergence produced 4.7 Mg ha-1 (Griffin and Jung, 1981). Similarly, Henry over a 3-yr period for nonfertilized Blackwell switchgrass stage of morphological development at first harvest, but regrowth potential decreased with later first-harvest date (Anderson and Matches, 1983). Harvest frequency had a large influence on stand persistence. Harvesting two or three times yearly decreased switchgrass stands by period decreased stands by only 39% (Newell and Keim, et al. (1976) reported yields of more than 6.0 Mg ha<sup>-1</sup> productivity. In Pennsylvania, nonfertilized 'Blackwell 1947)

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    mental status of tiller populations within the sward. Traits tion of the leaf tip), leaf blade length and width, and internode length. Changes in plant morphology that occur during primary growth can be important determinants Newell and Eberhart, 1961; Talbert et al., 1983). Tan of a seven-parent diallel cross in smooth bromegrass vested, is determined by size, architecture, and developof canopy architecture include plant and tiller densities, LAI, leaf mean tilt angle (MTA, or the angle of inclina-Because yield is controlled by many genes, heritability et al. (1977) concluded that leaf area per tiller and tiller density were two fundamental factors that affected yield (Bromus inermis Leyss.). Yield of perennial ryegrass Quantity of forage, either grazed or mechanically harof potential productivity in perennial forage grasses. of yield in switchgrass is low (Godshalk et al., 1986;

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    Abbreviations: DM, dry matter; DW, dry weight; G \\times E, genotype \\times environment; IVDMD, in vitro dry matter disappearance; LAI, leaf area index; MSC, mean stage by count; MSW, mean stage by weight; MTA, mean tilt angle; L \\times P, location \\times population.

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    (Lolium perenne L.) was positively correlated with leaf length and LAI (Rhodes, 1969, 1975), and perennial ryegrass varieties with erect foliage were more productive than varieties with less erect canopies (Rhodes, 1968, 1971). Erect leaf angles may allow more light to illuminate a greater leaf area and thus possibly increase forage productivity. Maize (Zea mays L.) had greater yield from canopies with erect leaves than canopies without erect leaves (Pendleton et al., 1968). Light entering a canopy of erect leaves was spread over a larger photosynthetic area than in prostrate varieties, resulting in greater photosynthetic efficiency.

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    An improved understanding of the associated effects of individual morphological components and morphological developmental of tiller populations within a sward on yield may improve forage management and use. For example, increased yields have resulted from selection of plants with larger tillers and leaf blades in reed canarygrass (Phalaris arundinacea L.) and tall fescue (Festuca arundinacea Schreb.) (Carlson et al., 1983; Nelson and Sleper, 1983). High yielding forage species are favored by accumulation of a large number of reproductive tillers. For North American tallgrass prairie species, including switchgrass, the relationship of yield to tiller number per unit area has not been determined.

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    Investigation of canopy architecture within a grass sward may help explain how plants utilize their aerial environment. In forage crops, aboveground vegetation × environment interactions are largely controlled by canopy architecture (Welles and Norman, 1991) as it relates to the distribution, areas and shapes of leaves, stems, and inflorescences. Leaf area index, leaf MTA, foliage density, and estimates of foliage arrangement often have been used to describe canopy structure. Brougham (1958) suggested that inter- and intraspecies variation in LAI would become evident as the growing season progressed.

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    There has been minimal detailed research on switchgrass to describe development of switchgrass canopy structure. Measurements of sward developmental morphology in relation to tiller and canopy architecture for improved switchgrass varieties are limited. Elementary information on the growth and development of the canopy structure is required to use switchgrass successfully as a forage crop.

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    Our objectives were to determine if traits of canopy architecture can explain observed G \\times E differences in switchgrass populations known to differ in yield and to evaluate canopy architecture as a potential selection criterion for increasing yield in a switchgrass breeding

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    established to evaluate genotype and G \\times E interactions in switchgrass (Hopkins et al., 1995). This study was structured as an in-depth investigation of the genotype and G \\times E study previously reported by Hopkins et al. (1995). More specifically, six subset populations were used to represent the extent of variation that existed for yield and also populations that showed significant G \\times E responses. The plots were established in the spring of 1990 as a randomized complete block design with four replicates of 20 switchgrass populations at each location. Plot size at Ames was 3.7 by 0.9 m; plot size at Mead was 4.5 by 1.5 m. Each plot was subdivided into three subplots, to be harvested randomly for the three different sward maturities.

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    This study used three commercially available switchgrass cultivars (Trailblazer, Pathfinder, and Cave-in-Rock) and three experimental populations selected either for high or low IVDMD or dry matter (DM) yield. These experimental populations had been developed by the USDA-ARS forage grass breeding project located at Lincoln, NE. Descriptions of genetic backgrounds of the switchgrass populations follow the nomenclature previously detailed by Vogel et al. (1991) and Hopkins et al. (1993). Trailblazer was developed by one cycle of selection for high IVDMD in the Ey × FF population that originated from selections made in Nebraska and Kansas; it is similar in maturity and adaptation to Pathfinder but with increased IVDMD (Vogel et al., 1991). Pathfinder is a synthetic cultivar developed from selections made in Nebraska and Kansas (Newell, 1968). Cave-in-Rock was developed from selections made in southern Illinois and developed by the Natural Resources Conservation Service Plant Material Center in Ellsberry, MO. The three experimental populations were Ey × FF Low IVDMD Cycle 1, Ey × FF High IVDMD Cycle 3, and Pathfinder High Yield-DMD Cycle 2. The Ey × FF Low IVDMD Cycle 1 population was selected from the same base population as Trailblazer and is identical to the population described by Vogel et al. (1991) for one cycle of decreased IVDMD. The Ey \\times FF High IVDMD Cycle 3 population was selected from the same base population as Trailblazer using three cycles of recurrent restricted phenotypic selection for increased IVDMD (Hopkins et al., 1993). Pathfinder High Yield-DMD Cycle 2 was selected from Pathfinder using two cycles of recurrent restricted phenotypic selection for both increased yield and IVDMD.

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    Previous year's growth was removed in the fall after a killing frost. All plots at both locations received 122 kg N ha<sup>-1</sup> in the form of NH<sub>4</sub>NO<sub>3</sub> approximately 1 wk following initiation of spring growth. After initiation of spring growth, stand counts were estimated according to methods described by Hopkins et al. (1995). Harvest date and growth stage of subplots were 9 June (vegetative), 19 July (elongating), and 27 August (reproductive) at Ames and 10 June (vegetative), 27 July (elongating), and 26 August (reproductive) at Mead. These dates were chosen to approximate tiller populations selected from the mid-vegetative, mid-elongating, and mid-reproductive stages of morphological development based on the relationship

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    program.

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    MATERIALS AND METHODS

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    This study was conducted during 1993 at the University of Nebraska Agricultural Research and Development Center near Mead, NE, on a Sharpsburg silty clay loam (fine, montmorillonitic, mesic Typic Argiudoll) and at the Iowa State University Agronomy and Agricultural Engineering Research Center near Ames, IA, on a Webster silty clay loam (fine-loamy, mixed, mesic Typic Endoaquolls). The experimental plots used in the study were switchgrass (Panicum virgatum L.) yield trials

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    of switchgrass morphological development to day of the year (Mitchell et al., 1992). During this study, all populations at Mead except Cave-in-Rock were infected by a leaf rust (presumably caused by a Puccinia species).

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    Prior to harvest at each sward maturity, LAI and leaf MTA were determined for each plot, using a LI-COR LAI-2000 leaf area analyzer (LI-COR, Lincoln, NE) for an indirect measure of canopy architecture as described by Welles and Norman

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    &lt;sup>1</sup> Mention of a trade name does not constitute a guarantee of the product by the USDA or the Univ. of Nebraska and does not imply its approval to the exclusion of other suitable products.

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    (1991). Five above-canopy and 20 below-canopy readings were taken. Leaf area index and leaf MTA measurements were taken prior to 1000 h (daylight time) to reduce the influence of sunlight reflections on canopy estimations.

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    Plant materials were harvested by hand-clipping all tillers to ground level from a randomly placed 0.1-m<sup>2</sup> quadrat. Harvested tillers were classified morphologically using the growth staging system for perennial forage grasses described by Moore et al. (1991) to estimate sward maturity on a mean stage by count (MSC) basis.

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    Further discussion of primary and secondary growth stage descriptions follow the definitions given by Moore et al. (1991). All measurements were made on only one leaf blade and internode per tiller. Measurements of leaf blade length and width were taken from freshly harvested leaves of 10 randomly chosen tillers within the V2 secondary stage from vegetative swards. Measurements of leaf blade length and width, and internode length were taken from 10 randomly selected tillers within the E2 and R4 secondary stages from the elongating and reproductive sward maturities, respectively. These individual stages were chosen to represent the estimated MSC within each sward maturity. Leaf blade length and width were taken from the first fully expanded, collared leaf extending from the top of the whorl (i.e., the most recently collared leaf). Leaf blade length was measured from the leaf collar to the leaf tip. Leaf blade width was measured at the widest portion of the leaf blade. Stem internode length was measured from the midpoint of the first palpable node to the midpoint of the second palpable node.

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    Tillers separated by growth stage were dried in a forced-draft oven at 55°C and weighed to estimate sward maturity on a mean stage by weight (MSW) basis (Moore et al., 1991) and to determine total yield and dry matter (DM) partitioning into leaf blade, leaf sheath, and stem components. Inflorescences were included with the stem fraction. Morphological components of secondary growth stages (Moore et al., 1991) were pooled according to primary growth stage (vegetative, elongating, reproductive, or seed ripening) to determine DM contribution from individual yield components within each primary growth stage. Total yield estimates were calculated for each

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    Table 1. Monthly minimum, maximum, and average temperature and total monthly precipitation at Ames, IA, and Mead, NE, during May, June, July, and August 1993.

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    1emperature
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    _ °c _mm
    Ames, IAMay3.828.513.9122
    June5.531.819.8188
    July11.537.222.3351
    August12.833.317.5267
    Mead, NEMay2.733.615.869
    0.00 to 10.00 0.00 0.00 0.00 0.00 0.00 0.00 0.June6.935.622.2193
    July13.835.022.3173
    August10.934.523.6155
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    population by summing the dry weight of the individual morphological components.

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    The experiment was designed as a randomized complete block with a split-plot arrangement. Whole plots were tiller population and subplots were sward maturities. Locations and blocks nested within location were assumed to be random factors with populations and sward maturities considered fixed factors (McIntosh, 1983). Data were analyzed using the General Linear Model (GLM) procedures of SAS (1985). Linear contrasts were used to compare specific sets of populations. All tests of significance were made at the 0.05 level of probability unless otherwise noted. Numerous higher order interactions were significant for most traits. However, only a small portion of the overall variation was partitioned into these interactions. They were considered biologically insignificant and not deemed. important in the interpretation of the results. Stepwise regression (SAS, 1985) was used to predict yield, LAI, and leaf MTA. Terms included in the stepwise regression equations were significant at the 0.15 level of probability.

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    RESULTS AND DISCUSSION Weather Conditions

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    Average monthly temperature was lower at Ames than

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    Table 2. Total dry matter (DM) yield of sward and leaf blade, leaf sheath, and stem yield (DM basis) for six switchgrass populations at three sward maturities at Ames, IA, and Mead, NE, during 1993.

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    Population†MaturityAmesMeadAmesMeadAmesMeadAmesMead
    kg DlM m-2 —
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    Elongating
    Reproductive    		0.19
    0.90
    1.16    		0.30
    1.26
    1.09    		0.13
    0.38
    0.34    		0.18
    0.41
    0.30    		0.06
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    0.22    		0.11
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    0.60    		0.01
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    Reproductive    		0.23
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    1.23    		0.29
    1.32
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    0.61    		0.02
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    LSD (0.05)0.190.170.100.38
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    † LDMDC1, Low IVDMD Cycle 1; HDMDC3, High IVDMD Cycle 3; HYDMDC2, High Yield-DMD Cycle 2.

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    Table 3. Yield prediction equations using leaf blade (LB), leaf sheath (LS), and stem dry weight per tiller, tiller density (T), and mean stage by count (MSC), and mean stage by weight (MSW) for six switchgrass populations at Ames, IA, and Mead, NE, during 1993.

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    Population†Total dry matter yield
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    † LDMDC1, Low IVDMD Cycle 1; HDMDC3, High IVDMD Cycle 3; HYDMDC2, High Yield-DMD Cycle 2.

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    at Mead, except during July, when the average temperature was identical. Wetter than normal conditions occurred during 1993 with more than 100 mm of rainfall during each month at both locations, except for May at Mead, when 69 mm of rain fell (Table 1).

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    Total Dry Matter Yield

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    Significant location, population, and harvest effects and location \\times population (L \\times P) interactions occurred for yield (P < 0.05). Maximum yields occurred in reproductive swards, except for Trailblazer, which had a small but significant yield decrease for reproductive swards at Mead (Table 2). Yield in reproductive swards at Ames ranged from 0.87 kg m<sup>-2</sup> (for Ey \\times FF High IVDMD Cycle 3) to 2.16 kg m<sup>-2</sup> (for Cave-in-Rock). A similar range was observed for yield in reproductive swards at Mead: 1.09 kg m<sup>-2</sup> for Trailblazer to 2.41 kg m<sup>-2</sup> for Pathfinder High Yield-DMD Cycle 2. The most notable difference was the significant shift in yield ranking within location for Cave-in-Rock and Pathfinder High Yield-DMD Cycle 2. These reversed rankings are consistent with the G \\times E interactions previously reported by Hopkins et al. (1995) for these same two populations.

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    Yield Prediction

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    Leaf blade, leaf sheath, and stem dry weight per tiller, tillers per unit area, mean stage by count, and mean stage by weight were used to predict dry matter yield

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    (Table 3). The primary yield components should theoretically account for all variation in the yield prediction equations. It was not surprising that MSC and MSW were significant factors for predicting yield in Cave-in-Rock, Ey × FF Low IVDMD Cycle 1, and Pathfinder High Yield-DMD Cycle 2, because DM accumulation should be directly related to plant growth and development. Leaf blade and/or leaf sheath dry weight (DW) per tiller were significant factors for predicting yield in Trailblazer, Pathfinder, and Ey × FF High IVDMD Cycle 3 (Table 2).

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    Pathfinder and Pathfinder High Yield-DMD Cycle 2 had similar yields at Ames, but at Mead the yield of Pathfinder High Yield-DMD Cycle 2 was greater than that of Pathfinder. This was due mainly to differences in contribution of tiller size to total DM yield (Table 4). Selection of genotypes for high tiller dry weight increased DM yields of vegetative (Nelson et al., 1985) and reproductive (Sleper and Drolsom, 1974) swards of tall fescue and smooth bromegrass. However, as plant densities increase, tiller size usually decreases (Nelson and Moser, 1994). Trailblazer and Ey × FF Low IVDMD Cycle 1 had greater yield than Ey × FF High IVDMD Cycle 3 at Ames, due to major contributions of total leaf blade DW per tiller (Tables 2 and 4) and tiller numbers (Fig. 1). At Mead, conversely, Ey × FF Low IVDMD Cycle 1 and Ey \\times FF High IVDMD Cycle 3 both had greater total yields than Trailblazer, because total stem dry weight made a major contribution (Table

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    Table 4. Average tiller weight at the vegetative, elongating, reproductive, and seed ripening growth stages for six switchgrass populations grown at Ames, IA, and Mead, NE, during 1993.

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    † LDMDC1, Low IVDMD Cycle 1; HDMDC3, High IVDMD Cycle 3; HYDMDC2, High Yield-DMD Cycle 2.

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    Fig. 1. Tiller density of six switchgrass populations at Ames, IA, and Mead, NE, averaged across three sward maturities.

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    2). Additionally, leaf sheath dry weight contributed to the total yield of Ey × FF High IVDMD Cycle 3.

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    On a per-tiller basis, contribution by leaf blade and sheath weight varied by location and population with no significant L \\times P interactions (P < 0.05). This suggests that dry weight contributions from individual morphological components were expressed consistently across environments. Generally, leaf blade and leaf sheath fractions accounted for virtually all of the DM accumulation in vegetative swards at both locations (Table 2). In elongating and reproductive swards at Ames, leaf blade and sheath DW per tiller remained constant, whereas stem DW per tiller generally increased. At Mead, leaf blade and sheath DW per tiller generally decreased, whereas stem DW per tiller tended to increase as elongating swards progressed to reproductive sward maturities (Table 2). In elongating swards, the inflorescences of the early reproductive tillers contributed toward total stem weight and may have inflated differences in stem weights in elongating and reproductive swards. The rust infection that occurred in most populations at Mead may have accelerated leaf loss in the infected populations, particularly Trailblazer, which had decreased yield as elongating swards progressed to a reproductive sward maturity.

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    Mean Stage by Count and Mean Stage by Weight

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    Since yield is related to plant developmental morphology, quantification of switchgrass sward development could be a useful tool in yield prediction. If plant maturity within these switchgrass populations had been affected

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    (Kalu and Fick, 1981; Moore et al., 1991). Cave-in-Rock had a greater MSC and MSW than the other populations at both locations (Table 5).

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    A strong association between growing degree days and morphological development was observed by Sanderson and Wolf (1995), who found vegetative growth to be temperature sensitive and reproductive growth to be photoperiod sensitive. Sims et al. (1971) found that the middle phytomers were responsible for approximately 75% of the total forage yield in switchgrass. They concluded that the contribution to total dry matter of the last phytomers was primarily by the addition of stem. Similarly, greater than 75% of the total forage yield had occurred by an elongating sward maturity growth stage with additional yield increases occurring only due to stem accumulation (Table 3).

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    The difference in yield ranking for Pathfinder High Yield-DMD Cycle 2 and Cave-in-Rock at Mead resulted from increased dry weight contribution from leaf blade, leaf sheath, and stems. This was probably caused by changes in the morphology of Pathfinder High Yield-DMD Cycle 2. At Ames, the maximum number of collared leaves prior to elongation (V_{max}) was 3, and the maximum number of palpable nodes prior to seedhead emergence (E_{\\text{max}}) was 8; at Mead, V_{\\text{max}} and E_{\\text{max}} increased to 4 and 9, respectively. For Cave-in-Rock, however, the values were identical for both Ames and Mead: V_{\\text{max}} = 4 and E_{\\text{max}} = 7. It is not unreasonable to think that this small change in tiller developmental morphology resulted in significant increases in dry weight contributions of the morphological components and thus greatly affected yield. This change in developmental morphology becomes increasingly important, considering that tiller densities were not different between Cave-in-Rock and Pathfinder High Yield-DMD Cycle 2 (Fig. 1).

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    Plant and Tiller Density

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    Plant density varied by location and population. Average plant density for all populations generally was greater at Ames (23.0 plants m<sup>-2</sup>) than at Mead (18.0 plants m<sup>-2</sup>). Averaged across locations, Cave-in-Rock had 22.5 plants m<sup>-2</sup>. Pathfinder had 22.0 plants m<sup>-2</sup>, compared with 20.5 plants m<sup>-2</sup> for Pathfinder High Yield-DMD Cycle 2. Trailblazer and Ey × FF Low IVDMD Cycle 1 had similar plant densities (21.0 and 20.0 plants m<sup>-2</sup>, respectively), compared with 16.5 plants m<sup>-2</sup> for Ey × FF High IVDMD Cycle 3. Reduced yields would be expected to occur as a result of low plant densities. The reasons for differences in plant density among

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    by recurrent restricted phenotypic selection for increased yield or IVDMD, these differences would be manifested in MSC and MSW estimates that quantify sward maturity at the time of harvest (Moore and Moser, 1995). The relationship of MSC to MSW was linear and highly correlated. The equation relating these variables was y = 0.084 + 1.123x, r^2 = 0.99, where y = MSW and x = MSC. However, research by others has shown that MSC tended to underestimate MSW, particularly in reproductive swards, due to greater DM accumulation of stem tissue in tillers of advanced sward maturities

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    switchgrass populations selected from the same base populations for increased IVDMD have not been determined. However, in space-planted nurseries, there has been substantial winter-kill of Ey × FF High IVDMD Cycle 3 (D.R. Buxton, personal communication, 1995; K.P. Vogel, unpublished data, 1995).

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    Significant location and population effects and L \\times P interactions occurred for tiller density (P < 0.05). Averaged across sward maturities, tiller density was greater at Ames than Mead. At Ames, Ey \\times FF Low IVDMD Cycle 1 had a greater tiller density than the other

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    Table 5. Sward maturity estimated as mean stage by count (MSC) and mean stage by weight (MSW) for six switchgrass populations grown at Ames, IA, and Mead, NE, and harvested at three sward maturities during 1993.

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    Population†M.SCMSW
    MaturityAmesMeadAmesMead
    TrailblazerVegetative1.52 (0.19)‡1.45 (0.25)1.591.57
    Elongating2.30 (0.27)2.72 (0.40)2.492.94
    Reproductive2.97 (0.47)3.14 (0.50)3.313.48
    PathfinderVegetative1.51 (0.22)1.45 (0.30)1.591.61
    Elongating2.35 (0.24)2.71 (0.43)2.502.94
    Reproductive2.92 (0.38)3.25 (0.55)3.213.56
    Cave-in-RockVegetative1.45 (0.20)1.88 (0.35)1.532.09
    Elongating2.58 (0.37)2.87 (0.39)2.803.03
    Reproductive3.41 (0.55)3.48 (0.58)3.703.77
    Ey × FF LDMDC1Vegetative1.51 (0.18)1.49 (0.26)1.561.62
    Elongating2.28 (0.23)2.62 (0.44)2.422.93
    Reproductive2.78 (0.47)3.23 (0.54)3.133.59
    Ey × FF HDMDC3Vegetative1.49 (0.19)1.41 (0.18)1.551.48
    Elongating2.32 (0.26)2.67 (0.47)2.452.89
    Reproductive3.12 (0.53)3.34 (0.50)3.463.57
    Pathfinder HYDMDC2Vegetative1.51 (0.19)1.43 (0.21)1.571.53
    Elongating2.29 (0.20)2.67 (0.39)2.432.91
    Reproductive2.86 (0.39)3.18 (0.63)3.273.58
    LSD (0.05)0.170.17
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    † LDMDC1, Low IVDMD Cycle 1; HDMDC3, High IVDMD Cycle 3; HYDMDC2, High Yield-DMD Cycle 2.

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    populations, whereas Cave-in-Rock had a significantly lower tiller density than the other populations (Fig. 1), but greater forage yield (Table 2). Tiller density of Ey × FF High IVDMD Cycle 3 did not differ across locations. Thus, tiller density may potentially affect developmental morphology. Cave-in-Rock had the greatest MSC and MSW at both locations, with a lower tiller density suggesting a more rapid growth rate.

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    Morphological Components

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    Each switchgrass population was represented by several genotypes (Vogel and Pedersen, 1993). Thus, the genetic representation within each population would be expected to produce a variation in morphological charac-

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    teristics and also differences in morphological development. Significant differences among populations were apparent for leaf blade length and width, internode length, LAI, and leaf MTA. Significant location effects and L \\times P interactions also occurred for leaf blade width and internode length (P < 0.05).

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    Generally, leaf blades of vegetative and elongating tillers were narrower than leaf blades of reproductive tillers (Table 6). Once the leaf collar has emerged, leaf growth has been completed; however, leaves emerging early in the growing season usually were narrower than those emerging later in the growing season (Nelson and Larson, 1984). More energy may have been required in rapidly growing vegetative and elongating tillers to

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    Table 6. Leaf blade width, leaf blade length, and stem internode length of six switchgrass populations grown at Ames, IA, and Mead, NE, harvested at three sward maturities during 1993.

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    Population†Leaf blade widthLeaf blade lengthStem internode length
    MaturityAmesMeadAmesMeadAmesMead
    mım ———cm
    TrailblazerVegetative
    Elongating
    Reproductive    		5.0
    4.9
    6.4    		6.3
    6.2
    7.2    		22.3
    44.3
    44.2    		25.1
    43.4
    45.6    		0
    14.7
    18.9    		0
    9.8
    15.6
    PathfinderVegetative
    Elongating
    Reproductive    		5.2
    4.6
    6.2    		5.8
    4.6
    6.8    		23.2
    41.2
    41.1    		23.7
    27.4
    45.3    		0
    17.4
    19.5    		9.2
    19.2
    Cave-in-RockVegetative6.87.422.419.000
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    ‡ Values in parentheses are the standard deviation.

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    Ey \\times FF HDMDC3Vegetative
    Elongating    		4.6
    5.6    		6.6
    7.3    		19.4
    43.6    		22.7
    37.3    		0
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    Path finder HVDMDC2Reproductive7.37.043.840.217.716.9
    Pathfinder HYDMDC2Vegetative
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    Reproductive    		5.1
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    5.8    		6.4
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    7.0    		23.2
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    38.4    		21.0
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    43.4    		15.811.4
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    † LDMDC1, Low IVDMD Cycle 1; HDMDC3, High IVDMD Cycle 3; HYDMDC2, High Yield-DMD Cycle 2.

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    Table 7. Leaf area index (LAI) and leaf mean tilt angle (MTA) of six switchgrass populations grown at Ames, IA, and Mead, NE, harvested at three sward maturities during 1993 and the LAI and MTA of the six populations averaged across locations and maturities.

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    FactorMaturityLAIMTA
    Location, across populations9000 00
    Ames, IAVegetative2.857.6
    Elongating5.546.2
    Reproductive4.946.0
    Mead, NEVegetative3.454.9
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    LSD (0.05)0.32.0
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    Trailblazer84 (S.G. 6010) 4010 (S.G. 7-122), •14.049.9
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    Cave-in-Rock4.746.5
    Ey × FF LDMDC14.250.2
    Ey × FF HDMDC34.151.2
    Pathfinder HYDMDC24.849.5
    LSD (0.05)0.32.0
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    \\dagger Mean tilt angle (i.e., the inclination angle of the leaf tip) of an erect canopy is 90°.

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    support cellular division in meristematic regions rather than leaf expansion (Brown, 1984).

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    Differences in leaf blade length of the populations were not associated with increased yield or IVDMD. Shorter leaf blade lengths of reproductive tillers may have been exaggerated, due to leaf tip necrosis in combination with the leaf rust infection. As internode elongation progressed, canopy height was increased. This occurred simultaneously with increases in leaf blade length. Both Trailblazer and Ey × FF High IVDMD Cycle 3 had shorter internode lengths than Ey × FF Low IVDMD Cycle 1 at Mead, although internode length did not differ at Ames.

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    Leaf Area Index and Mean Tilt Angle

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    Averaged across populations, location and sward maturity differences were observed for LAI and leaf MTA (Table 7). Leaf area index and leaf MTA were predicted with a multiple regression using leaf blade length and width, internode length, leaf blade, leaf sheath, and stem DW per tiller, and MSC and MSW.

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    Leaf area index increased with increasing leaf blade length and leaf blade DW per tiller. Conversely, LAI decreased as leaf blade width increased. Although leaf blade length and width were significant factors in the regression, leaf blade DW per tiller was by far the

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    canopy measurements, LAI had the strongest association with yield (r = 0.67). Leaf blade width was not well correlated with yield or canopy architecture. Although leaf blade length was not correlated highly with yield, it was well correlated with LAI and leaf MTA, which were in turn well correlated with yield. For the switchgrass populations in this study, increased LAI compensated for less erect canopies. The decreased leaf area in old canopies probably resulted from senescence of leaves located toward the bottom of the canopy.

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    CONCLUSIONS

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    Yield was affected primarily by tiller growth and development and the associated morphological modifications occurring in the leaf blades, leaf sheaths, and stems in these divergent switchgrass populations. These same traits of canopy architecture explained observed G \\times E interactions for yield. Low correlations of yield were associated with all individual canopy traits except possibly LAI (which showed some promise for use as a phenotypic selection criterion). Thus, manipulation of canopy architecture may not be useful in a switchgrass breeding program.

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    Although direct cause and effect relationships have not been determined, several anomalies in switchgrass developmental morphology were observed. The number of collared leaves prior to elongation and the number of internodes was not identical for all populations within a location, and it also varied for some of the populations across locations. Internode length varied considerably among the populations. Most noteworthy was the difference of internode length among the Ey × FF population through several cycles of recurrent restricted phenotypic selection for improved IVDMD.

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    Selection for increased yield and IVDMD caused significant changes in canopy architecture, but the relationship between individual canopy traits and forage yield was variable. For switchgrass strains used as hay or biomass crops, canopies should contain tillers with higher DW. This may result in fewer tillers per unit area, but possibly greater forage yields. If the switchgrass canopy is to be managed for grazing, leaf yield would be more important than total forage yield. If individual canopy traits can be identified as consistent indicators of forage palatability to grazing animals, nutrient density, or nutrient yield, then selection for individual canopy traits may be effective for modifying sward growth habits.

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    REFERENCES

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    ‡ LDMĎC1, Low IVDMD Cycle 1; HDMDC3, High IVDMD Cycle 3; HYDMDC2, High Yield-DMD Cycle 2.

    ", + "attached_object_id": "doc:f148869703fe84bd" + }, + { + "kind": "paragraph", + "id": "doc:678be923d87543be", + "text": "", + "provenance": { + "marker_block_ids": [ + "/page/13/Text/0" + ], + "page_number": 13, + "bbox": { + "x0": 22.5, + "y0": 14.25, + "x1": 367.5, + "y1": 24.36328125 + }, + "contributing_bboxes": null, + "polygon": { + "points": [ + [ + 22.5, + 14.25 + ], + [ + 367.5, + 14.25 + ], + [ + 367.5, + 24.36328125 + ], + [ + 22.5, + 24.36328125 + ] + ] + }, + "reading_order_index": 153, + "section_path": [ + "doc:6037d1e90dd832b1", + "doc:dcc9d741c76519da", + "doc:6eb6a2aa693c6da6" + ] + } + }, + { + "kind": "paragraph", + "id": "doc:eede156f14382ae4", + "text": "

    most important in affecting LAI and leaf MTA. The relationship between leaf blade length and width was low (r=0.22) and nonsignificant. Thus, within these switchgrass populations, it should be possible to select for longer leaf blade length without affecting leaf blade width.

    ", + "provenance": { + "marker_block_ids": [ + "/page/13/Text/1" + ], + "page_number": 13, + "bbox": { + "x0": 57.0, + "y0": 93.97265625, + "x1": 293.25, + "y1": 156.75 + }, + "contributing_bboxes": null, + "polygon": { + "points": [ + [ + 57.0, + 93.97265625 + ], + [ + 293.25, + 93.97265625 + ], + [ + 293.25, + 156.75 + ], + [ + 57.0, + 156.75 + ] + ] + }, + "reading_order_index": 154, + "section_path": [ + "doc:6037d1e90dd832b1", + "doc:dcc9d741c76519da", + "doc:6eb6a2aa693c6da6" + ] + } + }, + { + "kind": "paragraph", + "id": "doc:d7c312fd07f86261", + "text": "

    Averaged across locations and sward maturities, leaf MTA decreased as leaf blade length increased and leaf blade DW per tiller increased. This suggests that leaf MTA decreased as a result of the simultaneous increase in leaf weight due to increased length. Of the individual

    ", + "provenance": { + "marker_block_ids": [ + "/page/13/Text/2" + ], + "page_number": 13, + "bbox": { + "x0": 56.25, + "y0": 158.25, + "x1": 293.25, + "y1": 210.76171875 + }, + "contributing_bboxes": null, + "polygon": { + "points": [ + [ + 56.25, + 158.25 + ], + [ + 293.25, + 158.25 + ], + [ + 293.25, + 210.76171875 + ], + [ + 56.25, + 210.76171875 + ] + ] + }, + "reading_order_index": 155, + "section_path": [ + "doc:6037d1e90dd832b1", + "doc:dcc9d741c76519da", + "doc:6eb6a2aa693c6da6" + ] + } + }, + { + "kind": "paragraph", + "id": "doc:b3cdb3cb390d5aa7", + "text": "

    Anderson, B., and A.G. Matches. 1983. Forage yield, quality, and persistence of switchgrass and caucasian bluestem. Agron. J. 75: 119-124.

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    Brougham, R.W. 1958. Interception of light by the foliage of pure and mixed stands of pasture plants. Aust. J. Agric. Res. 9:39-52.

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    Brown, R.H. 1984. Growth of the green plant. p. 153-174. In M.B. Tesar (ed.) Physiological basis of crop growth and development. ASA and CSSA, Madison, WI.

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    Carlson, I.T., D.K. Christensen, and R.B. Pearce. 1983. Selection for specific leaf weight in reed canarygrass and its effect on the plant. p. 207-209. In J.A. Smith and V.W. Hays (ed.) Proc. Int. Grassl. Congr., 14th, Lexington, KY. 15-24 June 1981. Westview Press, Boulder, CO.

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    in vitro dry matter disappearance in switchgrass regrowth. Crop Jodshalk, E.B., J.C. Burns, and D.H. Timothy. 1986. Selection for

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    Griffin, J.L., and G.A. Jung. 1981. Yield and forage quality of Panicum virgatum. p. 491-494. In J.A. Smith and V.W. Hays (ed.) Proc. Int. Grassl. Congr., 14th, Lexington, KY. 15-24 June 1981. Westview Press, Boulder, CO.

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    Henry, D.S., H.W. Everett, and J.K. Evans. 1976. Clipping effect on stand, yield, and quality of three warm-season grasses. p. 701-704. In J. Luchok et al. (ed.) Hill lands. Proc. Int. Symp., Morgantown, WV. 3-9 Oct. 1976. West Virginia Univ. Office of Publications, Morgantown.

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    Hopkins, A.A., K.P. Vogel, and K.J. Moore. 1993. Predicted and realized gains from selection for in vitro dry matter digestibility

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    and forage yield in switchgrass. Crop Sci. 33:253-258. Hopkins, A.A., K.P. Vogel, K.J. Moore, K.D. Johnson, and I.T. Carlson. 1995. Genotype effects and genotype by environment interactions for traits of elite switchgrass populations. Crop Sci. 35:125-132.

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    Kalu, B.A., and G.W. Fick. 1981. Quantifying morphological devel-

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    opment for studies of herbage quality. Crop Sci. 21:267-271. McIntosh, M.S. 1983. Analysis of combined experiments. Agron. J. 75:153-155.

    ", + "provenance": { + "marker_block_ids": [ + "/page/14/Text/10" + ], + "page_number": 14, + "bbox": { + "x0": 274.5, + "y0": 416.25, + "x1": 303.0, + "y1": 671.34375 + }, + "contributing_bboxes": null, + "polygon": { + "points": [ + [ + 275.25, + 416.25 + ], + [ + 303.0, + 416.25 + ], + [ + 303.0, + 671.34375 + ], + [ + 274.5, + 671.34375 + ] + ] + }, + "reading_order_index": 166, + "section_path": [ + "doc:6037d1e90dd832b1", + "doc:dcc9d741c76519da", + "doc:6eb6a2aa693c6da6" + ] + } + }, + { + "kind": "paragraph", + "id": "doc:16fb23173fafd901", + "text": "

    Forage quality of switchgrass and big bluestern in relation to Mitchell, R.B., K.J. Moore, L.E. Moser, and K.P. Vogel. 1992. morphological development. p. 183. In Agronomy abstracts. ASA, Madison, WI.

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    Moore, K.J., and L.E. Moser. 1995. Quantifying developmental

    ", + "provenance": { + "marker_block_ids": [ + "/page/14/Text/12" + ], + "page_number": 14, + "bbox": { + "x0": 341.25, + "y0": 417.0, + "x1": 360.38671875, + "y1": 662.25 + }, + "contributing_bboxes": null, + "polygon": { + "points": [ + [ + 342.0, + 417.0 + ], + [ + 360.38671875, + 417.75 + ], + [ + 360.38671875, + 662.25 + ], + [ + 341.25, + 662.25 + ] + ] + }, + "reading_order_index": 168, + "section_path": [ + "doc:6037d1e90dd832b1", + "doc:dcc9d741c76519da", + "doc:6eb6a2aa693c6da6" + ] + } + }, + { + "kind": "paragraph", + "id": "doc:511628e837fb0fb2", + "text": "

    morphology of perennial grasses. Crop Sci. 35:37-43. Moore, K.J., L.E. Moser, K.P. Vogel, S.S. Waller, B.E. Johnson, and J.F. Pedersen. 1991. Describing and quantifying growth stages of perennial forage grasses. Agron. J. 83:1073-1077

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    In M.B. Tesar (ed.) Physiological basis of crop growth and develop-Nelson, C.J., and K.L. Larson. 1984. Seedling growth. p. 93-129 ment. ASA and CSSA, Madison, WI.

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It defines every +object in the canonical Document layer — its purpose, fields, types, relationships, +identifier rule, provenance rule, validation rules, and serialization requirements — +so that implementing the Pydantic v2 Document Object later requires translation, not +design. No parsing, normalization, or business logic is described here; only the shape +of the data those future components will populate. + +--- + +## 0. Relationship to the Raw Marker Model + +The Raw Marker Model is a lossless, uninterpreted mirror of Marker's JSON output: a +single `MarkerBlock` envelope type, `extra="allow"`, no discriminated union, no +semantic interpretation. The Document Object described here is the next layer down +the pipeline: it is produced *from* the Raw Marker Model by the Normalizer (not yet +implemented) and is the first place where **structural** interpretation occurs — +deciding what counts as a Table, a Section, a Footnote's likely attachment — while +still containing zero scientific meaning. + +Every Document-layer object therefore exists in addition to, not instead of, the Raw +Marker Model. The Raw Marker Model remains the permanent ground truth on disk; +the Document Object is a derived, queryable, typed structural view over it. This +specification assumes the Raw Marker Model is available as an immutable input and +focuses entirely on what the Normalizer must produce from it. + +--- + +## 1. Architectural Invariants + +These rules are not per-object — they govern the entire Document layer and every +object defined below conforms to them without restating them per-section. + +**1.1 Immutability.** Every Document-layer model is frozen after construction +(Pydantic v2 `model_config = ConfigDict(frozen=True)`). No object is mutated after +the Normalizer finishes building it. Corrections, re-interpretation, or review +happen in later layers (IR, Scientist Review) and never write back into the +Document Object. + +**1.2 Structural-only content.** No Document-layer object may contain a field whose +purpose is to record scientific meaning. Concretely: no `treatment`, `species`, +`observation`, `management_event`, `variable`, or `trait` field exists anywhere in +this schema, even as an optional placeholder. If a future need arises to record such +a concept, it belongs in the IR, which is built on top of — never inside — the +Document Object. + +**1.3 Deterministic identifiers.** No identifier in this schema is a UUID4 or any +other non-deterministic value. Every identifier is a pure function of stable inputs, +so that re-running the same PDF through the same Marker version and the same +Normalizer version always yields byte-identical identifiers. The exact construction +rule is given in Section 2. + +**1.4 Maximum available provenance.** Every object that originates from one or more +Marker blocks retains a `StructuralProvenance` value (Section 3.3) referencing the +originating Marker block id(s), page number, bounding box, polygon, and reading-order +position. An object is never permitted to "lose" its Marker origin even when the +Normalizer reshapes or merges multiple Marker blocks into one Document object (e.g. +turning a `SectionHeader` + `Text` pair into one normalized `Caption`). + +**1.5 Deterministic, lossless serialization.** Every model in this schema must +support `model_dump()` / `model_dump_json()` and round-trip back through +`model_validate()` without information loss, exactly as already verified for the +Raw Marker Model. Field ordering in dumped JSON is determined by declaration order +in the Pydantic model (not insertion order at runtime) to keep serialized output +byte-stable across runs. + +**1.6 Independence from downstream layers.** Nothing in this schema imports from, +references, or anticipates retrieval, LLM extraction, validation, the IR, or BETYdb +export. The Document Object's public surface is consumed by those layers, but this +schema has zero knowledge of them. + +--- + +## 2. Identifier Strategy + +**Rule.** Every Document-layer object's `id` is computed as: + +``` +id = "doc:" + sha256( document_id + "|" + canonical_path )[:16] +``` + +where `document_id` is the parent Document's own id (Section 4), and +`canonical_path` is a deterministic structural path string specific to each object +type, defined per-object below (generally derived from the originating Marker +block's own path-like id, e.g. `/page/7/Table/2`, when one exists 1:1; or, for objects +synthesized from multiple Marker blocks or with no direct Marker counterpart — such +as a parsed `TableRow` — a path built from the parent object's id plus an ordinal +position among deterministically-ordered siblings, e.g. `.../Table/2/row/3`). + +**Why a hash rather than reusing Marker's path id directly.** Marker's own ids +(`/page/7/Table/2`) are positional/index-based — `Table/2` means "third +Table-typed block encountered in that page's traversal." If a future Marker version +changes internal traversal order, encounters a new block type, or reorders block +discovery, these indices could silently shift between runs on an unchanged PDF, +producing different "stable" ids for the same content. Hashing a path that is +itself still derived from Marker's structure, combined with the document id, +preserves determinism for a fixed Marker/adapter version while making the contract +explicit: **stability is guaranteed within one Marker version, not promised across +Marker upgrades.** The original Marker path is never discarded — it is preserved +verbatim inside every object's `StructuralProvenance.marker_block_id` — so a Marker +version bump that changes traversal order is detectable (ids change) and +diagnosable (provenance still shows the old vs. new Marker ids). + +**document_id construction.** `document_id = "betydoc:" + sha256(source_pdf_identifier)[:16]`, +where `source_pdf_identifier` is a stable external identifier for the source PDF +(DOI if known, else a content hash of the source PDF bytes). This deliberately +excludes Marker version and timestamp from the identity computation: the same PDF +must always resolve to the same `document_id` so that re-processing (e.g. after a +Normalizer bug fix) updates the same logical Document Object rather than minting an +unrelated one. Marker version and processing time are recorded as **metadata about +the materialization**, not folded into identity — see `ProcessingMetadata` (Section +6). + +**Properties guaranteed by this scheme:** +- Same PDF + same Marker version + same Normalizer version ⇒ identical ids + throughout the tree. +- Ids are opaque strings, safe to use as dictionary keys, filenames, or database + foreign keys. +- Every id is traceable backward to a concrete Marker block via + `StructuralProvenance`, satisfying invariant 1.4. + +--- + +## 3. Foundational Supporting Types + +These are not top-level entities; they are embedded value objects used throughout +the schema. + +### 3.1 BoundingBox + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `x0` | float | yes | Marker-observed | Left edge | +| `y0` | float | yes | Marker-observed | Top edge | +| `x1` | float | yes | Marker-observed | Right edge | +| `y1` | float | yes | Marker-observed | Bottom edge | + +Directly carried over from Marker's `bbox` (already typed as `MarkerBBox` in the Raw +Marker Model). Retained at the Document layer because footnote-to-table attachment, +evidence highlighting in the Scientist Review UI, and any future geometric +reconstruction (e.g. merged-cell heuristics) all require it. **Invariant:** `x1 >= +x0` and `y1 >= y0`; the Normalizer is responsible for not constructing a violating +instance, but the model also validates this on construction since the cost of +allowing silently-inverted boxes downstream is high (evidence UI would render +boxes wrong with no error signal). + +### 3.2 Polygon + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `points` | list of 4 `(float, float)` pairs | yes | Marker-observed | Carried over from Marker's `polygon` | + +Retained even though `BoundingBox` is derivable from it, because Marker provides +both independently and the polygon can in principle capture skew that an +axis-aligned bbox cannot. This is a direct empirical carry-over (already present and +typed in the Raw Marker Model) rather than a new design — Document-layer objects +simply forward it unchanged. No Document-layer object computes one from the other; +both come from Marker as-is. + +### 3.3 StructuralProvenance + +This is the single most important supporting type in the schema — it is what +satisfies invariant 1.4 for every object below. + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `marker_block_ids` | list of str | yes (min length 1) | Marker-observed | The originating Marker block id(s), e.g. `["/page/7/Table/2"]`. A list, not a single value, because some Document objects (e.g. a normalized `Caption` under Pattern B) are synthesized from more than one Marker block. | +| `page_number` | int | yes | Marker-observed | The PDF page this object originates from. For objects spanning conceptually across the synthesis of multiple Marker blocks, this is the page of the primary/first contributing block. | +| `bbox` | `BoundingBox` | no | Marker-observed | Present for any object with a single, well-defined originating region. Absent for objects synthesized from multiple non-adjacent blocks where a single bbox would be misleading (e.g. a Pattern-B caption combining a `SectionHeader` far above a trailing `Text` note) — in that case `contributing_bboxes` is populated instead. | +| `contributing_bboxes` | list of `BoundingBox` | no | Marker-observed | Used instead of (or in addition to) `bbox` when more than one Marker block contributes geometry, preserving each one rather than collapsing them into a single misleading box. | +| `polygon` | `Polygon` | no | Marker-observed | Mirrors `bbox`'s optionality logic. | +| `reading_order_index` | int | yes | Architectural requirement | The object's position in the document's global linear reading order (Section 3.4). Required on every provenance instance because every structural object has a place in reading order even if its bbox is ambiguous. | +| `section_path` | list of str | yes (may be empty) | Marker-observed (derived) | The chain of governing `SectionHeader` Marker-block ids from Marker's own `section_hierarchy` map, ordered outermost to innermost. Empty only for objects outside any section (e.g. a journal wrapper page's `Picture`). | + +**Why a list of Marker block ids rather than exactly one.** Empirically, not every +Document-layer concept maps 1:1 to a Marker block. The clearest case is `Table` +captions: under Pattern A (`TableGroup`), the caption is one `Caption` block; under +Pattern B (bare `Table`), the equivalent information is split across a +`SectionHeader` block and a `Text` block, sometimes with a trailing "Note:" `Text` +block. Forcing a single-id provenance field would require silently picking one +contributing block and losing the others. A list preserves all of them, satisfying +invariant 1.4 even when normalization merges several Marker blocks into one +Document concept. + +### 3.4 Reading Order + +Reading order is **not** a field on a supporting type — it is a global integer +sequence assigned by the Normalizer to every leaf and container object during +construction, equal to that object's position in a single depth-first traversal of +the final Document Object tree, in `children` array order. + +This decision is made explicitly here because it was a confirmed empirical finding, +not a default assumption: Marker's own block id local-index numbers (e.g. the +trailing `4` in `/page/7/Footnote/4`) are **not** monotonic with true reading order — +`Footnote/4` and `Footnote/5` physically appear, in the actual children array, after +`Table/8`, despite having lower index numbers. Reading order must therefore be +(re)computed by the Normalizer from final tree position, never inferred from Marker's +id numbering. `StructuralProvenance.reading_order_index` is this recomputed value, +not a copy of any number embedded in a Marker id string. + +### 3.5 Section Path + +`StructuralProvenance.section_path` is populated directly from Marker's own +`section_hierarchy` dict, which the empirical findings confirmed is already a +precomputed breadcrumb (e.g. a deeply nested `TableCell` carrying +`{'1': '/page/1/SectionHeader/1', '4': '/page/7/SectionHeader/0'}`). Two properties +of this dict are carried forward into the spec rather than assumed away: + +- **Depth keys are not contiguous small integers.** The observed keys were `'1'` + and `'4'`, not `'1'` and `'2'`, indicating these correspond to some absolute + nesting depth from Marker's internal traversal rather than a clean rank. The + Document schema therefore stores `section_path` as an **ordered list of + SectionHeader Marker-block ids** (sorted by the numeric value of their original + dict key) rather than preserving Marker's dict-with-gaps shape — this gives + downstream consumers (Retrieval layer, Section nesting) a clean, ordinary list + without forcing them to understand Marker's internal depth-key semantics. +- **The mapping is per-block, not per-Section-object.** Every Marker block — + including deeply nested ones like a `TableCell` — carries its own full path. The + Document Object's `Section` containment hierarchy (Section 9) is derived from this + same data, so `section_path` on any object and that object's ancestor `Section` + chain are guaranteed consistent by construction, not by a separate invariant check. + +--- + +## 4. Document + +**Purpose.** The root container for one processed paper. Holds the page sequence, +top-level metadata, processing metadata, and aggregate statistics. Exactly one +`Document` exists per source PDF per Normalizer run. + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `id` | str | yes | Architectural requirement | `document_id`, Section 2. | +| `source_pdf_identifier` | str | yes | Architectural requirement | The stable external identifier used to compute `id` (DOI or content hash). Stored explicitly so the id's derivation is independently checkable, not just trusted. | +| `metadata` | `Metadata` | yes | Marker-observed + Architectural | Section 5. | +| `processing_metadata` | `ProcessingMetadata` | yes | Architectural requirement | Section 6. | +| `statistics` | `Statistics` | yes | Architectural requirement | Section 7. | +| `pages` | list of `Page` | yes (min length 1) | Marker-observed | Ordered by page number ascending; this ordering is also the top level of global reading order. | + +**Invariants.** +- `pages` is non-empty and sorted ascending by `Page.page_number` with no + duplicate or skipped page numbers other than what Marker itself reported (a + Marker-side page omission is preserved, not silently re-numbered). +- `Document` is the only object in this schema with no `StructuralProvenance` of + its own (there is no single Marker block representing "the whole document" — the + Raw Marker Model's root node was empirically confirmed to have no `id`, `bbox`, + or `polygon` at all). Its provenance is implicitly "the entire Raw Marker Model + file," which `processing_metadata.source_marker_artifact_ref` captures (Section + 6) rather than a `StructuralProvenance` instance. + +--- + +## 5. Metadata + +**Purpose.** Bibliographic and identification facts about the paper, to the extent +they are structurally recoverable (not semantically extracted — see the boundary +note below). + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `title` | Optional[str] | no | Marker-observed | Taken verbatim from the first/top-level `SectionHeader` or title-styled block on the front matter page, if structurally identifiable. | +| `page_count` | int | yes | Marker-observed | Count of `Page` objects; redundant with `len(pages)` but kept as an explicit field since `Statistics` (Section 7) is meant to hold *derived counts*, while this one is a basic identifying fact worth surfacing without traversing the tree. | +| `has_front_matter_page` | bool | yes | Marker-observed (heuristic) | Whether page 0 (or any page) was structurally flagged as publisher wrapper content. See `Page.is_front_matter` (Section 8) for the per-page flag this aggregates. | + +**Boundary note.** `Metadata` deliberately does **not** include authors, journal +name, publication year, or DOI as structured fields, even though these are +intuitively "metadata." Per the empirical findings (3.8), front-matter and +citation-bearing content is **structurally indistinguishable** from other text at +the block-type level — recovering "the authors" or "the journal" requires reading +and interpreting text content, which is scientific/semantic extraction, not +structural parsing. That work belongs to the IR's `Citation` entity (already +specified in the project's broader IR design), built by the extraction layer. This +spec only exposes what is mechanically true of the page structure (title block +location, page count, front-matter flag) — adding speculative `author`/`doi`/`year` +fields here would violate invariant 1.2 and the "no speculative fields" instruction, +since populating them correctly is not a structural operation. + +--- + +## 6. ProcessingMetadata + +**Purpose.** Records *how* this particular Document Object was produced, separate +from *what* it identifies (Section 4's `id`/`source_pdf_identifier`). This is what +makes reproducibility checkable: two Document Objects with the same `id` but +different `ProcessingMetadata` indicate the same paper was processed by a different +Marker or Normalizer version, which is exactly the signal needed to detect drift +without conflating it with document identity. + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `marker_version` | str | yes | Marker-observed | Verbatim from Marker's own output metadata, if present; otherwise the version string of the Marker invocation recorded by the adapter. | +| `normalizer_version` | str | yes | Architectural requirement | Semantic version of the Normalizer code that produced this Document Object. Required so a future schema/logic change is always attributable. | +| `processed_at` | datetime (ISO 8601, UTC) | yes | Architectural requirement | Wall-clock time of this materialization. Explicitly **not** part of `id` computation (Section 2) — recorded for audit/debugging only. | +| `source_marker_artifact_ref` | str | yes | Architectural requirement | A path or content hash identifying the exact Raw Marker Model JSON file this Document Object was normalized from, satisfying the "Document has no own provenance" note in Section 4 by pointing at the file-level artifact instead of a block-level one. | + +**Why this is architectural rather than Marker-observed for most fields.** Only +`marker_version` comes from Marker itself; the rest exist purely because the +project's stated reproducibility requirement ("identical PDFs ... should always +produce identical Document Objects," and detectability of drift) demands a place to +record the inputs that determine reproducibility, even though Marker's own output +has no opinion on them. + +--- + +## 7. Statistics + +**Purpose.** Aggregate counts over the final Document Object tree, useful for +sanity-checking a Normalizer run (e.g. "did this paper produce zero tables when the +PDF clearly has six") without re-traversing the tree ad hoc. + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `page_count` | int | yes | Architectural requirement (derived) | `len(pages)`. | +| `section_count` | int | yes | Architectural requirement (derived) | Total `Section` objects across the document. | +| `paragraph_count` | int | yes | Architectural requirement (derived) | Total `Paragraph` objects. | +| `table_count` | int | yes | Architectural requirement (derived) | Total `Table` objects. | +| `figure_count` | int | yes | Architectural requirement (derived) | Total `Figure` objects. | +| `equation_count` | int | yes | Architectural requirement (derived) | Total `Equation` objects. | +| `footnote_count` | int | yes | Architectural requirement (derived) | Total `Footnote` objects. | +| `reference_count` | int | yes | Architectural requirement (derived) | Total `Reference` objects. | +| `unresolved_footnote_count` | int | yes | Architectural requirement (derived) | Footnotes whose `attached_object_id` (Section 16) is `None` after Normalizer processing — a direct, queryable signal of how much of the geometric-attachment heuristic (empirical finding 3.2) succeeded on this paper. | + +**Why this object exists at all, given everything in it is derivable.** Every +field here is computable by traversal, so in principle `Statistics` adds no new +information. It exists as an explicit object — rather than leaving consumers to +compute it themselves — because (a) it gives a single, serializable snapshot for +logging/comparison across Normalizer runs without re-parsing the whole tree, and (b) +`unresolved_footnote_count` specifically operationalizes a concern raised directly +in the empirical findings (footnote attachment is a heuristic, not guaranteed) into +a number that can be tracked across the representative paper set as the Normalizer +is built and tuned. + +--- + +## 8. Page + +**Purpose.** One PDF page's structural content, in reading order. + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `id` | str | yes | Architectural requirement | Per Section 2; `canonical_path = "/page/{page_number}"`. | +| `page_number` | int | yes | Marker-observed | Zero-indexed, matching Marker's own page numbering. | +| `provenance` | `StructuralProvenance` | yes | Marker-observed | `marker_block_ids = [the Marker Page block's id]`. | +| `children` | list of (`Section` \| `Paragraph` \| `Table` \| `Figure` \| `Equation` \| `Footnote` \| `PageHeader` \| `PageFooter`) | yes (may be empty) | Marker-observed | Top-level content of the page, in final reading order (Section 3.4). A discriminated union over `block_type`-equivalent kinds, mirroring (but not reusing) Marker's own children-array structure. | +| `is_front_matter` | bool | yes | Marker-observed (heuristic) | True if this page was identified as publisher wrapper content (journal cover, "Submit your article," ISSN-only content, etc.) rather than paper body. | + +**On `is_front_matter`.** Empirical finding 3.8 established that Marker gives no +structural signal distinguishing a wrapper page from a content page — both use +identical block types. This flag is therefore explicitly a **heuristic output of +the Normalizer** (content-pattern based, e.g. presence of "ISSN," "Submit your +article," near-total absence of citation-bearing text), not something copied from +Marker. The field is included now, with its value to be computed later, because the +project's stated requirement is that the schema accommodate this known case without +redesign — per the same logic as the other deferred-population fields in this spec +(Section 22 collects all of them explicitly). + +**Why a discriminated union for `children` rather than `list[Any]` or one generic +`Block` type.** The Raw Marker Model deliberately uses one uniform envelope because +it must stay agnostic to block semantics. The Document Object's job is the opposite: +it exists specifically to make structural type distinctions (a `Table` is not +interchangeable with a `Paragraph` downstream). A discriminated union gives +consumers static type safety and keeps `Page`/`Section` children lists +self-describing in serialized JSON via the discriminator field, with no loss of +ordering since list order is itself the reading-order signal (Section 3.4). + +--- + +## 9. Section + +**Purpose.** A heading-governed grouping of content, derived from Marker's +`section_hierarchy` breadcrumbs (Section 3.5) rather than re-derived from text +pattern-matching on headings. + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `id` | str | yes | Architectural requirement | `canonical_path` built from the governing `SectionHeader` Marker block's own path id. | +| `heading_text` | str | yes | Marker-observed | Verbatim text of the governing `SectionHeader` block. | +| `provenance` | `StructuralProvenance` | yes | Marker-observed | `marker_block_ids = [the SectionHeader block's id]`. | +| `depth` | int | yes | Marker-observed (derived) | Position of this section's heading in the ordered `section_path` list (Section 3.5), zero-indexed from the outermost heading on the page/document. | +| `children` | list of (`Section` \| `Paragraph` \| `Table` \| `Figure` \| `Equation` \| `Footnote`) | yes (may be empty) | Marker-observed | Nested sub-sections and content governed by this heading, in reading order. A `Section` may contain further `Section` objects, giving the hierarchy genuine nesting rather than a flat list with a depth integer alone. | + +**Invariant.** Every leaf or container object elsewhere in the schema that carries +a non-empty `section_path` in its `StructuralProvenance` must have a corresponding +ancestor chain of `Section` objects matching that path exactly — this is guaranteed +by construction (both are derived from the same `section_hierarchy` source, per +Section 3.5) rather than checked as a runtime validator, but it is stated here as a +hard design invariant the Normalizer must not violate. + +**Why `SectionHeader` blocks that are really table/figure labels (e.g. a Marker +`SectionHeader` containing only `"Table 3"`, per Pattern B) do not become `Section` +objects.** Empirical finding 3.1 showed Marker uses the same `SectionHeader` +block type both for genuine paper sections (Methods, Results) and for bare-table +caption labels. The Normalizer must distinguish these by context — a +`SectionHeader` immediately followed by a `Text` block and then a `Table`, with no +intervening structural content, is a caption label being consumed into that +`Table`'s `Caption` (Section 12), not a new `Section`. This rule is recorded here so +the schema's `Section` object is understood to represent only genuine paper +sections; the disambiguation logic itself is Normalizer business logic (out of +scope for this document) but the *consequence* — that some `SectionHeader` Marker +blocks become part of a `Caption` rather than a `Section` — is a structural decision +the schema must support, and it does: `Caption.provenance.marker_block_ids` can +include a `SectionHeader` id (Section 12). + +--- + +## 10. Paragraph + +**Purpose.** A single block of body text — the most common leaf content type. + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `id` | str | yes | Architectural requirement | Per Section 2. | +| `text` | str | yes | Marker-observed | The block's inline HTML content from Marker, **as-is** (e.g. ``, `` tags preserved). | +| `provenance` | `StructuralProvenance` | yes | Marker-observed | `marker_block_ids = [the originating Text/ListItem block's id]`. | + +**Why `text` keeps inline HTML rather than being plain-text-stripped.** Empirical +finding (leaf block dump) confirmed Marker leaf blocks carry real semantic inline +markup (``, ``) directly in their content, not a side annotation. Stripping +it at the Document layer would be a one-way, lossy transformation performed before +any consumer has had a chance to decide whether that markup matters (e.g. a `` +emphasis inside a Methods paragraph could matter to the extraction layer's reasoning +about emphasis on a key term). Per invariant 1.4 (maximum available provenance) and +the general "never lose information without a consumer-side decision to do so" +principle, the Document layer preserves it verbatim; any stripping is a retrieval- +or extraction-layer concern, explicitly out of scope here. + +**Note on `ListItem`/`ListGroup`.** Marker's bibliography uses `ListGroup` containers +of `ListItem` leaves rather than `Text` blocks (this was the basis for separating +references structurally without text pattern-matching). A `ListItem` that is part of +a reference list is **not** modeled as a `Paragraph` — it becomes a `Reference` +(Section 17). A `Paragraph` is reserved for body-text `Text`/generic `ListItem` +content; the Normalizer disambiguates by parent context (a `ListGroup` under the +References section vs. elsewhere), again business logic out of scope here, but the +schema accommodates the distinct outcome via two separate object types. + +--- + +## 11. Caption (supporting type, embedded in Table and Figure) + +**Purpose.** A normalized representation of a table or figure's caption, +collapsing Marker's two empirically observed patterns into one shape. + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `label` | Optional[str] | no | Marker-observed | E.g. `"Table 3"` or `"Figure 1"`. Present whenever a `Caption` block (Pattern A) or a `SectionHeader` label block (Pattern B) was found. | +| `text` | Optional[str] | no | Marker-observed | The descriptive caption sentence. From the `Caption` block's content (Pattern A) or the `Text` block immediately following the label (Pattern B). | +| `trailing_notes` | Optional[str] | no | Marker-observed | The trailing "Note: ..." `Text` block sometimes observed immediately after a `Table`, distinct from both `label`/`text` and from `Footnote` objects (Section 16). Kept as its own field because it was empirically observed to be part of the caption apparatus, not body text, but also not a true Marker `Footnote` block. | +| `provenance` | `StructuralProvenance` | yes | Marker-observed | `marker_block_ids` lists every contributing Marker block (one for Pattern A's single `Caption` block; two or three for Pattern B's `SectionHeader` + `Text` + optional trailing `Text`). Uses `contributing_bboxes` (Section 3.3) rather than a single `bbox` whenever more than one block contributes, since collapsing non-adjacent regions into one bbox would misrepresent the geometry. | + +**Why one normalized shape rather than preserving Marker's two patterns +separately in the schema.** This is the central case the project's "empirically +driven, not speculative" instruction is built around: both patterns were directly +observed (Pattern A on pages 6/10/12, Pattern B on page 7, per finding 3.1), so +normalizing them is not a hypothetical convenience — it is required because every +downstream consumer (extraction layer asking "what is this table about," review UI +displaying "the caption") needs one consistent shape regardless of which pattern the +source PDF happened to produce. Modeling them as two different optional sub-objects +instead would push that disambiguation work onto every consumer rather than once, +inside the Normalizer, where the empirical knowledge of the two patterns actually +lives. + +--- + +## 12. Table + +**Purpose.** A table's logical structure and its evidence-level cell geometry, +kept as two deliberately parallel representations per the empirical recommendation. + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `id` | str | yes | Architectural requirement | Per Section 2. | +| `provenance` | `StructuralProvenance` | yes | Marker-observed | `marker_block_ids = [the Table block's id]` (and the `TableGroup` id too, if Pattern A). | +| `caption` | Optional[`Caption`] | no | Marker-observed | Section 11. `None` only if no caption-bearing blocks were found adjacent to the table at all (not empirically observed in the representative paper, but not excluded as a possibility — captionless tables are not assumed impossible). | +| `raw_html` | str | yes | Marker-observed | The Table block's own `html` field, verbatim — the complete, correctly-nested `...
    ` Marker produces. Treated as the **source of truth for logical structure** (rows, columns, header rows), per the empirical recommendation, precisely because reconstructing structure independently from cell geometry risks disagreeing with Marker's own (already correct) parse. | +| `rows` | list of `TableRow` | yes (may be empty) | Marker-observed (derived) | A structured parse of `raw_html`'s `` elements into row objects (Section 12.1), giving consumers row/column access without re-parsing HTML themselves. Derived from `raw_html`, not an independent reconstruction. | +| `cells` | list of `TableCell` | yes (may be empty) | Marker-observed | The flat list of Marker `TableCell` child blocks, retained **only** as evidence/geometry data (bbox, polygon, provenance) — explicitly not used to derive row/column structure, per the empirical recommendation that `raw_html` is structural truth and `TableCell` geometry is supplementary. | +| `footnote_ids` | list of str | yes (may be empty) | Architectural requirement (deferred population) | Ids of `Footnote` objects geometrically attached to this table (Section 16). Empty until the Normalizer's bbox-proximity heuristic (finding 3.2) runs; the field exists now so that heuristic's output has a defined home without later schema change. | + +### 12.1 TableRow (supporting type, embedded in `Table.rows`) + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `cells` | list of `TableRowCell` | yes | Marker-observed (derived) | Ordered left to right per the source ``. | + +### 12.2 TableRowCell (supporting type, embedded in `TableRow.cells`) + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `text` | str | yes | Marker-observed | Cell text content from the parsed ``/`` element, with any `` wrapper tag stripped and its content treated as equivalent plain text (per empirical finding 3.5 — Marker inconsistently wraps numerically identical `mean ± stderr` values in `` depending on OCR path; the schema does not preserve this distinction since it carries no structural meaning, only an OCR-routing artifact). | +| `is_header` | bool | yes | Marker-observed | True if the source element was ``, false for ``. | +| `structural_notes` | Optional[str] | no | Architectural requirement (deferred) | A free-text slot reserved for a Normalizer-attached structural annotation — most notably, a suspected merged-cell placeholder (empirical finding 3.4: Marker silently flattens merged header cells into duplicated rows with an empty filler cell, with no flag distinguishing this from a genuinely empty cell). The heuristic for populating this field is explicitly **not** decided in this specification — finding 3.4 was flagged as needing more representative papers before a reconstruction rule is chosen. The field is included as an open slot precisely so that decision can be made later without a schema change, consistent with the brief's instruction to accommodate known structural cases without redesign. | + +**Why `TableRowCell` does not have `row_index`/`col_index` integers.** These are +implicit in `Table.rows`' list-of-lists structure itself (a cell's row is its +containing `TableRow`'s position in `rows`; its column is its own position in +`cells`), so adding redundant integer fields would duplicate information already +present in list order, with no Marker-observed justification for storing it twice. + +**Why `TableRowCell` has no `rowspan`/`colspan` field.** Empirical finding 3.4 +confirmed Marker's HTML output never emits `rowspan`/`colspan` attributes even where +the source PDF visually has merged cells — it flattens instead. Adding a +`rowspan`/`colspan` field for a case never observed in Marker's actual output would +violate the "no speculative fields" instruction. If a future representative paper +demonstrates Marker does emit span attributes under some condition, this is the +single place such fields would be added. + +### 12.3 TableCell (supporting type, embedded in `Table.cells`; evidence-only) + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `id` | str | yes | Architectural requirement | Per Section 2; path derived from the Marker `TableCell` block's own id. | +| `text` | str | yes | Marker-observed | Verbatim cell content (not math-stripped here — this object is evidence/geometry, not the logical text consumers should read; `TableRowCell.text` is the cleaned version). | +| `bbox` | `BoundingBox` | yes | Marker-observed | Per-cell geometry, the entire reason this parallel representation is retained (evidence highlighting in the review UI). | +| `polygon` | `Polygon` | yes | Marker-observed | Mirrors `bbox`. | + +**Why this evidence-only `TableCell` and the logical `TableRowCell` are not unified +into one type.** Empirical finding 3.3 established these are genuinely two +different, only partially-corresponding representations Marker provides in +parallel — one (the `` HTML) has correct logical structure but no per-cell +geometry, the other (`TableCell` children) has per-cell geometry but no row/column +index. Forcing them into a single type would require either fabricating row/column +indices on the geometry side (an unverified bbox-clustering reconstruction the +findings explicitly flagged as risky) or discarding per-cell geometry on the logical +side (losing the evidence-highlighting capability entirely). Keeping them separate, +each true to what Marker actually provides, is the choice that adds no +unverified inference. **Open implementation note, not a schema decision:** +positionally correlating a given `TableRowCell` with its corresponding `TableCell` +(for evidence highlighting of a specific logical cell) is left to the Normalizer to +attempt via parse-order correspondence; this spec does not assert that +correspondence is guaranteed, since it was not empirically verified. + +--- + +## 13. Figure + +**Purpose.** A figure region, with its caption normalized the same way as `Table`. + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `id` | str | yes | Architectural requirement | Per Section 2. | +| `provenance` | `StructuralProvenance` | yes | Marker-observed | `marker_block_ids = [the Figure block's id]` (and `FigureGroup` id, if present). | +| `caption` | Optional[`Caption`] | no | Marker-observed | Section 11. Empirically, `FigureGroup` always pairs `[Figure, Caption]` in that order (mirroring `TableGroup`'s pairing, just with reversed order — confirmed, not assumed, per the findings doc). | +| `image_data` | Optional[bytes] | no | Marker-observed | Base64-decoded raster image content from Marker's `images` field, when present. Empirically, in the representative paper, only `Picture` blocks (journal logo, cover thumbnail) carried non-empty `images`; the one `Figure` block had `images: {}`. This field is therefore included (figures plausibly can carry raster data, and the project must not assume they never will) but its emptiness in the current evidence base is recorded explicitly in Section 22 as unconfirmed, not silently assumed resolved. | + +--- + +## 14. Equation + +**Purpose.** A mathematical expression block. + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `id` | str | yes | Architectural requirement | Per Section 2. | +| `provenance` | `StructuralProvenance` | yes | Marker-observed | `marker_block_ids = [the Equation block's id]`. | +| `raw_math` | str | yes | Marker-observed | The verbatim MathML-ish `` content, including any equation number embedded inline (e.g. `"DP = I - IR + P - ETc \pm VR, \qquad (1)"`), per empirical finding 3.7. | +| `equation_number` | Optional[str] | no | Architectural requirement (deferred) | A slot for the parsed-out equation number (e.g. `"1"`), since finding 3.7 confirmed Marker provides no separate field for it — any cross-reference resolution ("using equation (1)" in body text) requires parsing it out of `raw_math`. The parsing logic itself is out of scope for this spec; the field exists so its result has a defined home. | + +--- + +## 15. Footnote + +**Purpose.** A footnote block, with its attachment to a table or figure resolved +geometrically rather than structurally, per empirical finding 3.2. + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `id` | str | yes | Architectural requirement | Per Section 2. | +| `provenance` | `StructuralProvenance` | yes | Marker-observed | `marker_block_ids = [the Footnote block's id]`. Note: this block's own provenance never implies attachment — footnotes are flat page-level siblings, not children of any Table/Figure (finding 3.2), so attachment is recorded separately below. | +| `raw_text` | str | yes | Marker-observed | Verbatim footnote content. | +| `attached_object_id` | Optional[str] | no | Architectural requirement (deferred) | The id of the `Table` or `Figure` this footnote was determined to belong to, via the Normalizer's bbox-proximity heuristic ("nearest preceding Table/Figure on the same page by bbox y-position," per finding 3.2). `None` when unresolved — tracked in aggregate by `Statistics.unresolved_footnote_count` (Section 7). The heuristic itself is Normalizer logic, out of scope here; the field exists so its output, including the legitimate possibility of non-resolution, has a defined, queryable home. | + +**Why attachment is nullable rather than required.** Forcing every footnote to +resolve to a table/figure would hide genuine ambiguity (e.g. a footnote whose +geometric position is equidistant between two candidates, or a page-level +disclaimer footnote unrelated to any table) behind an incorrect best-guess. Per the +project's broader principle (already established for the IR: "fields that cannot be +resolved are marked with an unresolved status rather than silently filled"), the +same discipline applies at the structural layer: `None` is a legitimate, recorded +outcome, not an implementation gap to paper over. + +--- + +## 16. Reference (Bibliography Entry) + +**Purpose.** One bibliography entry, structurally distinguished from body text +because Marker represents references via `ListGroup`/`ListItem`, not `Text` blocks +(observed directly in the page_stats/structure walkthrough, not inferred from +content). + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `id` | str | yes | Architectural requirement | Per Section 2. | +| `provenance` | `StructuralProvenance` | yes | Marker-observed | `marker_block_ids = [the ListItem block's id]`. | +| `raw_text` | str | yes | Marker-observed | Verbatim reference entry text, including any inline markup Marker preserved. | + +**Boundary note.** Like `Metadata` (Section 5), `Reference` deliberately stops at +verbatim text. Parsing a reference string into author/year/journal/DOI fields is +citation-matching — a semantic operation belonging to the IR's `Citation` entity, +not this layer. This object's only job is to say "this `ListItem`, structurally, +is a bibliography entry, not body text," which is information Marker's block typing +already gives for free via the `ListGroup` container. + +--- + +## 17. PageHeader / PageFooter + +**Purpose.** Repeated journal running-header/footer content (e.g. the journal name +repeated on every page, or page-footer branding), retained for completeness and +front-matter heuristics (Section 8) but not expected to be consumed by extraction. + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `id` | str | yes | Architectural requirement | Per Section 2. | +| `provenance` | `StructuralProvenance` | yes | Marker-observed | `marker_block_ids = [the PageHeader/PageFooter block's id]`. | +| `raw_text` | str | yes | Marker-observed | Verbatim content. | + +Modeled as two distinct types (`PageHeader`, `PageFooter`) rather than one generic +"running content" type, simply mirroring Marker's own distinct block types +one-to-one — there is no structural reason to merge them, and merging would lose +the type distinction Marker itself already makes. + +--- + +## 18. Cross-Object Relationships & Invariants Summary + +This section consolidates relationship rules stated piecemeal above, for a single +point of reference. + +- **Containment is exclusively via `children` lists and explicit id-reference + fields (`footnote_ids`, `attached_object_id`) — never via implicit ordering + conventions or id-string parsing.** Any consumer needing "what footnotes belong + to this table" reads `Table.footnote_ids`, never re-derives it from geometry + itself; the Normalizer computes that relationship exactly once. +- **Reading order is a single global property (Section 3.4), independent of any + per-object containment.** Two sibling objects under different `Section`s can be + compared for relative reading order via their `reading_order_index` without + needing to know anything about section nesting. +- **Every non-`Document` object carries exactly one `StructuralProvenance`,** which + is the only place Marker block ids appear outside of `ProcessingMetadata`'s + artifact reference. No object duplicates Marker ids elsewhere in its own fields. +- **No object type defined in this specification has a field referencing an IR, + retrieval, validation, or export concept**, satisfying invariant 1.6 by + construction — this is checked by inspection of this document, not by a runtime + rule, since it is a closed schema with a fixed object list. + +--- + +## 19. Serialization Requirements + +- All models use `model_config = ConfigDict(frozen=True, extra="forbid")`. Unlike + the Raw Marker Model (which intentionally used `extra="allow"` for forward + compatibility with unknown future Marker fields), the Document Object is the project's own + designed contract — an unexpected extra field here indicates a Normalizer bug, + not a benign future Marker addition, so it should fail loudly (`extra="forbid"`) + rather than silently passing through. +- `model_dump_json()` must be deterministic for a given object graph: field order + follows declaration order (Pydantic v2 default), list order follows the + semantically meaningful order already specified per field (reading order for + children, left-to-right for table cells, outermost-to-innermost for + `section_path`) — never a non-deterministic order like dict-hash order. + `datetime` fields serialize as ISO 8601 strings in UTC. +- Every model must round-trip losslessly through `model_dump()` → + `model_validate()` and `model_dump_json()` → `model_validate_json()`, mirroring + the test discipline already established and passing for the Raw Marker Model. +- Bytes fields (`Figure.image_data`) serialize as base64 strings in JSON, matching + Marker's own convention for `images`, so no separate encoding scheme is + introduced at this layer. + +--- + +## 20. Validation Rules + +These are construction-time invariants enforced by each model's own validators, +distinct from the cross-cutting invariants in Section 1 (which are policies the +Normalizer must follow, not all individually mechanically checkable). + +- `BoundingBox`: `x1 >= x0` and `y1 >= y0`. +- `StructuralProvenance`: `marker_block_ids` has at least one element; + `reading_order_index >= 0`; exactly one of `bbox` or `contributing_bboxes` (or + neither, for objects with genuinely no recoverable geometry) is populated — never + both, to avoid two disagreeing geometric claims about the same object. +- `Document`: `pages` non-empty; `page_number` values across `pages` are unique. +- `Page`: `page_number >= 0`. +- `Table`: if `rows` is non-empty, every `TableRow.cells` list has at least one + element (a row with zero cells is not a meaningful row — such input indicates a + parse error in `raw_html`, which should surface as a Normalizer-time error, not a + silently-accepted empty row in the Document Object). +- `Footnote`: no validation forces `attached_object_id` to be set — its absence is + valid by design (Section 15). +- `Statistics`: every count field is `>= 0`; `unresolved_footnote_count <= + footnote_count` (a basic sanity bound the model itself can check independent of + whatever produced the numbers). + +--- + +## 21. Explicitly Deferred — Not Modeled, By Design + +Per the instruction to avoid speculative fields, the following structural cases +identified during evaluation are **intentionally absent** from this schema rather +than represented with a guessed-at field shape, because the representative paper +set does not yet provide enough evidence to know what shape is correct: + +- **Multi-page table continuation.** Not observed in the representative paper (no + table spans a page break). No `continues_on_page` / `continuation_of_table_id` + field is added speculatively. When a representative paper exhibiting this is + evaluated, this section is where such a field would be added — as an addition, + not a redesign, since `Table` already has a stable `id` to reference. +- **Multi-panel figure decomposition.** The representative paper's Figure 3 has 10 + visually lettered sub-panels under one shared caption, but Marker recorded it as + a single flat `Figure` block with no internal panel structure. Since this is the + only data point (n=1) and it shows Marker *not* decomposing panels, no `panels: + list[FigurePanel]` field is added on the strength of a PDF-visual observation that + contradicts what Marker itself outputs. If a future paper shows Marker does + sometimes decompose panels, this is where that field would be introduced. +- **TableGroup-vs-bare-Table triggering condition.** Both patterns are modeled + (via `Caption`'s flexible provenance, Section 11), but *why* Marker chooses one + over the other (single table per region vs. dense multi-table page, per the one + data point available) is not encoded as a schema concept — it doesn't need to be, + since the Document Object normalizes both outcomes into the same `Caption` shape + regardless of cause. +- **Merged-cell reconstruction heuristic.** The *slot* (`TableRowCell.structural_ + notes`) exists (Section 12.2), but the specific rule for populating it (e.g. + "empty cell directly below a filled cell in the same column ⇒ merged-placeholder + suspected") is explicitly not decided here, per finding 3.4's own conclusion that + this needs more representative papers first. + +--- + +## 22. Summary — Field Origin Distribution + +A consolidated view of the distinction requested for this specification: how many +fields per object are direct Marker carry-overs versus existing purely to satisfy +an architectural requirement (provenance, determinism, reproducibility, +serialization) versus reserved as a deferred-population slot for a Normalizer +heuristic not yet designed. + +| Object | Marker-observed fields | Architectural-requirement fields | Deferred-population slots | +|---|---|---|---| +| Document | `pages` | `id`, `source_pdf_identifier`, `metadata`, `processing_metadata`, `statistics` | — | +| Metadata | `title`, `page_count`, `has_front_matter_page` | — | — | +| ProcessingMetadata | `marker_version` | `normalizer_version`, `processed_at`, `source_marker_artifact_ref` | — | +| Statistics | — | all count fields | — | +| Page | `page_number`, `children`, `is_front_matter` (heuristic) | `id`, `provenance` | — | +| Section | `heading_text`, `depth`, `children` | `id`, `provenance` | — | +| Paragraph | `text` | `id`, `provenance` | — | +| Caption | `label`, `text`, `trailing_notes` | `provenance` | — | +| Table | `raw_html`, `rows`, `cells`, `caption` | `id`, `provenance` | `footnote_ids` | +| TableRowCell | `text`, `is_header` | — | `structural_notes` | +| TableCell | `text`, `bbox`, `polygon` | `id` | — | +| Figure | `caption`, `image_data` | `id`, `provenance` | — | +| Equation | `raw_math` | `id`, `provenance` | `equation_number` | +| Footnote | `raw_text` | `id`, `provenance` | `attached_object_id` | +| Reference | `raw_text` | `id`, `provenance` | — | +| PageHeader/PageFooter | `raw_text` | `id`, `provenance` | — | + +--- + +## 23. Exit Criteria for This Specification + +This specification is ready to be frozen and handed to implementation once: + +1. Every object above has a 1:1 or many:1 mapping back to either an observed + Marker block type or a named architectural requirement (satisfied throughout + this document via the "Origin" column on every field table). +2. No field exists whose justification is "might be useful later" rather than + "Marker provides this" or "the architecture requires this for provenance / + determinism / reproducibility / serialization / validation" (satisfied; the one + category that looks speculative — deferred-population slots — is explicitly + justified by the stated requirement to avoid future schema redesign, and is + listed exhaustively in Section 22's third column plus Section 21's explicit + exclusions). +3. No object or field encodes scientific meaning (satisfied — verified against + invariant 1.2 by inspection of the full object list in Sections 4–17). +4. Implementing this specification in Pydantic v2 requires translation, not new + design decisions — the identifier rule (Section 2), provenance rule (Section + 3.3), reading-order rule (Section 3.4), and every per-object field table are + concrete enough to type directly. + +Once reviewed and approved, the next phase is the mechanical translation of this +document into immutable Pydantic v2 models, followed by the Normalizer that +populates them from the Raw Marker Model. diff --git a/docs/document_schema_specification_v1.0.md b/docs/document_schema_specification_v1.0.md new file mode 100644 index 0000000..cb12078 --- /dev/null +++ b/docs/document_schema_specification_v1.0.md @@ -0,0 +1,788 @@ +# Document Schema Specification + +**Project:** LLM-Assisted Extraction of Agronomic and Ecological Experiments into Structured Data +**Layer:** Document Understanding Layer (Document Object only) +**Status:** Draft for review — intended to be frozen upon approval +**Predecessor artifact:** Raw Marker Model (`marker_adapter/raw_model.py`), frozen +**Empirical basis:** `Marker Output — Empirical Findings (Paper 1: Nutrient Cycling, Smukler et al. 2012)` + +This document is an engineering specification, not an implementation. It defines every +object in the canonical Document layer — its purpose, fields, types, relationships, +identifier rule, provenance rule, validation rules, and serialization requirements — +so that implementing the Pydantic v2 Document Object later requires translation, not +design. No parsing, normalization, or business logic is described here; only the shape +of the data those future components will populate. + +--- + +## 0. Relationship to the Raw Marker Model + +The Raw Marker Model is a lossless, uninterpreted mirror of Marker's JSON output: a +single `MarkerBlock` envelope type, `extra="allow"`, no discriminated union, no +semantic interpretation. The Document Object described here is the next layer down +the pipeline: it is produced *from* the Raw Marker Model by the Normalizer (not yet +implemented) and is the first place where **structural** interpretation occurs — +deciding what counts as a Table, a Section, a Footnote's likely attachment — while +still containing zero scientific meaning. + +Every Document-layer object therefore exists in addition to, not instead of, the Raw +Marker Model. The Raw Marker Model remains the permanent ground truth on disk; +the Document Object is a derived, queryable, typed structural view over it. This +specification assumes the Raw Marker Model is available as an immutable input and +focuses entirely on what the Normalizer must produce from it. + +--- + +## 1. Architectural Invariants + +These rules are not per-object — they govern the entire Document layer and every +object defined below conforms to them without restating them per-section. + +**1.1 Immutability.** Every Document-layer model is frozen after construction +(Pydantic v2 `model_config = ConfigDict(frozen=True)`). No object is mutated after +the Normalizer finishes building it. Corrections, re-interpretation, or review +happen in later layers (IR, Scientist Review) and never write back into the +Document Object. + +**1.2 Structural-only content.** No Document-layer object may contain a field whose +purpose is to record scientific meaning. Concretely: no `treatment`, `species`, +`observation`, `management_event`, `variable`, or `trait` field exists anywhere in +this schema, even as an optional placeholder. If a future need arises to record such +a concept, it belongs in the IR, which is built on top of — never inside — the +Document Object. + +**1.3 Deterministic identifiers.** No identifier in this schema is a UUID4 or any +other non-deterministic value. Every identifier is a pure function of stable inputs, +so that re-running the same PDF through the same Marker version and the same +Normalizer version always yields byte-identical identifiers. The exact construction +rule is given in Section 2. + +**1.4 Maximum available provenance.** Every object that originates from one or more +Marker blocks retains a `StructuralProvenance` value (Section 3.3) referencing the +originating Marker block id(s), page number, bounding box, polygon, and reading-order +position. An object is never permitted to "lose" its Marker origin even when the +Normalizer reshapes or merges multiple Marker blocks into one Document object (e.g. +turning a `SectionHeader` + `Text` pair into one normalized `Caption`). + +**1.5 Deterministic, lossless serialization.** Every model in this schema must +support `model_dump()` / `model_dump_json()` and round-trip back through +`model_validate()` without information loss, exactly as already verified for the +Raw Marker Model. Field ordering in dumped JSON is determined by declaration order +in the Pydantic model (not insertion order at runtime) to keep serialized output +byte-stable across runs. + +**1.6 Independence from downstream layers.** Nothing in this schema imports from, +references, or anticipates retrieval, LLM extraction, validation, the IR, or BETYdb +export. The Document Object's public surface is consumed by those layers, but this +schema has zero knowledge of them. + +--- + +## 2. Identifier Strategy + +**Rule.** Every Document-layer object's `id` is computed as: + +``` +id = "doc:" + sha256( document_id + "|" + canonical_path )[:16] +``` + +where `document_id` is the parent Document's own id (Section 4), and +`canonical_path` is a deterministic structural path string specific to each object +type, defined per-object below (generally derived from the originating Marker +block's own path-like id, e.g. `/page/7/Table/2`, when one exists 1:1; or, for objects +synthesized from multiple Marker blocks or with no direct Marker counterpart — such +as a parsed `TableRow` — a path built from the parent object's id plus an ordinal +position among deterministically-ordered siblings, e.g. `.../Table/2/row/3`). + +**Why a hash rather than reusing Marker's path id directly.** Marker's own ids +(`/page/7/Table/2`) are positional/index-based — `Table/2` means "third +Table-typed block encountered in that page's traversal." If a future Marker version +changes internal traversal order, encounters a new block type, or reorders block +discovery, these indices could silently shift between runs on an unchanged PDF, +producing different "stable" ids for the same content. Hashing a path that is +itself still derived from Marker's structure, combined with the document id, +preserves determinism for a fixed Marker/adapter version while making the contract +explicit: **stability is guaranteed within one Marker version, not promised across +Marker upgrades.** The original Marker path is never discarded — it is preserved +verbatim inside every object's `StructuralProvenance.marker_block_id` — so a Marker +version bump that changes traversal order is detectable (ids change) and +diagnosable (provenance still shows the old vs. new Marker ids). + +**document_id construction.** `document_id = "betydoc:" + sha256(source_pdf_identifier)[:16]`, +where `source_pdf_identifier` is a stable external identifier for the source PDF +(DOI if known, else a content hash of the source PDF bytes). This deliberately +excludes Marker version and timestamp from the identity computation: the same PDF +must always resolve to the same `document_id` so that re-processing (e.g. after a +Normalizer bug fix) updates the same logical Document Object rather than minting an +unrelated one. Marker version and processing time are recorded as **metadata about +the materialization**, not folded into identity — see `ProcessingMetadata` (Section +6). + +**Properties guaranteed by this scheme:** +- Same PDF + same Marker version + same Normalizer version ⇒ identical ids + throughout the tree. +- Ids are opaque strings, safe to use as dictionary keys, filenames, or database + foreign keys. +- Every id is traceable backward to a concrete Marker block via + `StructuralProvenance`, satisfying invariant 1.4. + +--- + +## 3. Foundational Supporting Types + +These are not top-level entities; they are embedded value objects used throughout +the schema. + +### 3.1 BoundingBox + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `x0` | float | yes | Marker-observed | Left edge | +| `y0` | float | yes | Marker-observed | Top edge | +| `x1` | float | yes | Marker-observed | Right edge | +| `y1` | float | yes | Marker-observed | Bottom edge | + +Directly carried over from Marker's `bbox` (already typed as `MarkerBBox` in the Raw +Marker Model). Retained at the Document layer because footnote-to-table attachment, +evidence highlighting in the Scientist Review UI, and any future geometric +reconstruction (e.g. merged-cell heuristics) all require it. **Invariant:** `x1 >= +x0` and `y1 >= y0`; the Normalizer is responsible for not constructing a violating +instance, but the model also validates this on construction since the cost of +allowing silently-inverted boxes downstream is high (evidence UI would render +boxes wrong with no error signal). + +### 3.2 Polygon + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `points` | list of 4 `(float, float)` pairs | yes | Marker-observed | Carried over from Marker's `polygon` | + +Retained even though `BoundingBox` is derivable from it, because Marker provides +both independently and the polygon can in principle capture skew that an +axis-aligned bbox cannot. This is a direct empirical carry-over (already present and +typed in the Raw Marker Model) rather than a new design — Document-layer objects +simply forward it unchanged. No Document-layer object computes one from the other; +both come from Marker as-is. + +### 3.3 StructuralProvenance + +This is the single most important supporting type in the schema — it is what +satisfies invariant 1.4 for every object below. + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `marker_block_ids` | list of str | yes (min length 1) | Marker-observed | The originating Marker block id(s), e.g. `["/page/7/Table/2"]`. A list, not a single value, because some Document objects (e.g. a normalized `Caption` under Pattern B) are synthesized from more than one Marker block. | +| `page_number` | int | yes | Marker-observed | The PDF page this object originates from. For objects spanning conceptually across the synthesis of multiple Marker blocks, this is the page of the primary/first contributing block. | +| `bbox` | `BoundingBox` | no | Marker-observed | Present for any object with a single, well-defined originating region. Absent for objects synthesized from multiple non-adjacent blocks where a single bbox would be misleading (e.g. a Pattern-B caption combining a `SectionHeader` far above a trailing `Text` note) — in that case `contributing_bboxes` is populated instead. | +| `contributing_bboxes` | list of `BoundingBox` | no | Marker-observed | Used instead of (or in addition to) `bbox` when more than one Marker block contributes geometry, preserving each one rather than collapsing them into a single misleading box. | +| `polygon` | `Polygon` | no | Marker-observed | Mirrors `bbox`'s optionality logic. | +| `reading_order_index` | int | yes | Architectural requirement | The object's position in the document's global linear reading order (Section 3.4). Required on every provenance instance because every structural object has a place in reading order even if its bbox is ambiguous. | +| `section_path` | list of str | yes (may be empty) | Marker-observed (derived) | The chain of governing `SectionHeader` Marker-block ids from Marker's own `section_hierarchy` map, ordered outermost to innermost. Empty only for objects outside any section (e.g. a journal wrapper page's `Picture`). | + +**Why a list of Marker block ids rather than exactly one.** Empirically, not every +Document-layer concept maps 1:1 to a Marker block. The clearest case is `Table` +captions: under Pattern A (`TableGroup`), the caption is one `Caption` block; under +Pattern B (bare `Table`), the equivalent information is split across a +`SectionHeader` block and a `Text` block, sometimes with a trailing "Note:" `Text` +block. Forcing a single-id provenance field would require silently picking one +contributing block and losing the others. A list preserves all of them, satisfying +invariant 1.4 even when normalization merges several Marker blocks into one +Document concept. + +### 3.4 Reading Order + +Reading order is **not** a field on a supporting type — it is a global integer +sequence assigned by the Normalizer to every leaf and container object during +construction, equal to that object's position in a single depth-first traversal of +the final Document Object tree, in `children` array order. + +This decision is made explicitly here because it was a confirmed empirical finding, +not a default assumption: Marker's own block id local-index numbers (e.g. the +trailing `4` in `/page/7/Footnote/4`) are **not** monotonic with true reading order — +`Footnote/4` and `Footnote/5` physically appear, in the actual children array, after +`Table/8`, despite having lower index numbers. Reading order must therefore be +(re)computed by the Normalizer from final tree position, never inferred from Marker's +id numbering. `StructuralProvenance.reading_order_index` is this recomputed value, +not a copy of any number embedded in a Marker id string. + +### 3.5 Section Path + +`StructuralProvenance.section_path` is populated directly from Marker's own +`section_hierarchy` dict, which the empirical findings confirmed is already a +precomputed breadcrumb (e.g. a deeply nested `TableCell` carrying +`{'1': '/page/1/SectionHeader/1', '4': '/page/7/SectionHeader/0'}`). Two properties +of this dict are carried forward into the spec rather than assumed away: + +- **Depth keys are not contiguous small integers.** The observed keys were `'1'` + and `'4'`, not `'1'` and `'2'`, indicating these correspond to some absolute + nesting depth from Marker's internal traversal rather than a clean rank. The + Document schema therefore stores `section_path` as an **ordered list of + SectionHeader Marker-block ids** (sorted by the numeric value of their original + dict key) rather than preserving Marker's dict-with-gaps shape — this gives + downstream consumers (Retrieval layer, Section nesting) a clean, ordinary list + without forcing them to understand Marker's internal depth-key semantics. +- **The mapping is per-block, not per-Section-object.** Every Marker block — + including deeply nested ones like a `TableCell` — carries its own full path. The + Document Object's `Section` containment hierarchy (Section 9) is derived from this + same data, so `section_path` on any object and that object's ancestor `Section` + chain are guaranteed consistent by construction, not by a separate invariant check. + +--- + +## 4. Document + +**Purpose.** The root container for one processed paper. Holds the page sequence, +top-level metadata, processing metadata, and aggregate statistics. Exactly one +`Document` exists per source PDF per Normalizer run. + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `id` | str | yes | Architectural requirement | `document_id`, Section 2. | +| `source_pdf_identifier` | str | yes | Architectural requirement | The stable external identifier used to compute `id` (DOI or content hash). Stored explicitly so the id's derivation is independently checkable, not just trusted. | +| `metadata` | `Metadata` | yes | Marker-observed + Architectural | Section 5. | +| `processing_metadata` | `ProcessingMetadata` | yes | Architectural requirement | Section 6. | +| `statistics` | `Statistics` | yes | Architectural requirement | Section 7. | +| `pages` | list of `Page` | yes (min length 1) | Marker-observed | Ordered by page number ascending; this ordering is also the top level of global reading order. | + +**Invariants.** +- `pages` is non-empty and sorted ascending by `Page.page_number` with no + duplicate or skipped page numbers other than what Marker itself reported (a + Marker-side page omission is preserved, not silently re-numbered). +- `Document` is the only object in this schema with no `StructuralProvenance` of + its own (there is no single Marker block representing "the whole document" — the + Raw Marker Model's root node was empirically confirmed to have no `id`, `bbox`, + or `polygon` at all). Its provenance is implicitly "the entire Raw Marker Model + file," which `processing_metadata.source_marker_artifact_ref` captures (Section + 6) rather than a `StructuralProvenance` instance. + +--- + +## 5. Metadata + +**Purpose.** Bibliographic and identification facts about the paper, to the extent +they are structurally recoverable (not semantically extracted — see the boundary +note below). + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `title` | Optional[str] | no | Marker-observed | Taken verbatim from the first/top-level `SectionHeader` or title-styled block on the front matter page, if structurally identifiable. | +| `page_count` | int | yes | Marker-observed | Count of `Page` objects; redundant with `len(pages)` but kept as an explicit field since `Statistics` (Section 7) is meant to hold *derived counts*, while this one is a basic identifying fact worth surfacing without traversing the tree. | +| `has_front_matter_page` | bool | yes | Marker-observed (heuristic) | Whether page 0 (or any page) was structurally flagged as publisher wrapper content. See `Page.is_front_matter` (Section 8) for the per-page flag this aggregates. | + +**Boundary note.** `Metadata` deliberately does **not** include authors, journal +name, publication year, or DOI as structured fields, even though these are +intuitively "metadata." Per the empirical findings (3.8), front-matter and +citation-bearing content is **structurally indistinguishable** from other text at +the block-type level — recovering "the authors" or "the journal" requires reading +and interpreting text content, which is scientific/semantic extraction, not +structural parsing. That work belongs to the IR's `Citation` entity (already +specified in the project's broader IR design), built by the extraction layer. This +spec only exposes what is mechanically true of the page structure (title block +location, page count, front-matter flag) — adding speculative `author`/`doi`/`year` +fields here would violate invariant 1.2 and the "no speculative fields" instruction, +since populating them correctly is not a structural operation. + +--- + +## 6. ProcessingMetadata + +**Purpose.** Records *how* this particular Document Object was produced, separate +from *what* it identifies (Section 4's `id`/`source_pdf_identifier`). This is what +makes reproducibility checkable: two Document Objects with the same `id` but +different `ProcessingMetadata` indicate the same paper was processed by a different +Marker or Normalizer version, which is exactly the signal needed to detect drift +without conflating it with document identity. + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `marker_version` | str | yes | Marker-observed | Verbatim from Marker's own output metadata, if present; otherwise the version string of the Marker invocation recorded by the adapter. | +| `normalizer_version` | str | yes | Architectural requirement | Semantic version of the Normalizer code that produced this Document Object. Required so a future schema/logic change is always attributable. | +| `processed_at` | datetime (ISO 8601, UTC) | yes | Architectural requirement | Wall-clock time of this materialization. Explicitly **not** part of `id` computation (Section 2) — recorded for audit/debugging only. | +| `source_marker_artifact_ref` | str | yes | Architectural requirement | A path or content hash identifying the exact Raw Marker Model JSON file this Document Object was normalized from, satisfying the "Document has no own provenance" note in Section 4 by pointing at the file-level artifact instead of a block-level one. | + +**Why this is architectural rather than Marker-observed for most fields.** Only +`marker_version` comes from Marker itself; the rest exist purely because the +project's stated reproducibility requirement ("identical PDFs ... should always +produce identical Document Objects," and detectability of drift) demands a place to +record the inputs that determine reproducibility, even though Marker's own output +has no opinion on them. + +--- + +## 7. Statistics + +**Purpose.** Aggregate counts over the final Document Object tree, useful for +sanity-checking a Normalizer run (e.g. "did this paper produce zero tables when the +PDF clearly has six") without re-traversing the tree ad hoc. + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `page_count` | int | yes | Architectural requirement (derived) | `len(pages)`. | +| `section_count` | int | yes | Architectural requirement (derived) | Total `Section` objects across the document. | +| `paragraph_count` | int | yes | Architectural requirement (derived) | Total `Paragraph` objects. | +| `table_count` | int | yes | Architectural requirement (derived) | Total `Table` objects. | +| `figure_count` | int | yes | Architectural requirement (derived) | Total `Figure` objects. | +| `equation_count` | int | yes | Architectural requirement (derived) | Total `Equation` objects. | +| `footnote_count` | int | yes | Architectural requirement (derived) | Total `Footnote` objects. | +| `reference_count` | int | yes | Architectural requirement (derived) | Total `Reference` objects. | +| `unresolved_footnote_count` | int | yes | Architectural requirement (derived) | Footnotes whose `attached_object_id` (Section 16) is `None` after Normalizer processing — a direct, queryable signal of how much of the geometric-attachment heuristic (empirical finding 3.2) succeeded on this paper. | + +**Why this object exists at all, given everything in it is derivable.** Every +field here is computable by traversal, so in principle `Statistics` adds no new +information. It exists as an explicit object — rather than leaving consumers to +compute it themselves — because (a) it gives a single, serializable snapshot for +logging/comparison across Normalizer runs without re-parsing the whole tree, and (b) +`unresolved_footnote_count` specifically operationalizes a concern raised directly +in the empirical findings (footnote attachment is a heuristic, not guaranteed) into +a number that can be tracked across the representative paper set as the Normalizer +is built and tuned. + +--- + +## 8. Page + +**Purpose.** One PDF page's structural content, in reading order. + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `id` | str | yes | Architectural requirement | Per Section 2; `canonical_path = "/page/{page_number}"`. | +| `page_number` | int | yes | Marker-observed | Zero-indexed, matching Marker's own page numbering. | +| `provenance` | `StructuralProvenance` | yes | Marker-observed | `marker_block_ids = [the Marker Page block's id]`. | +| `children` | list of (`Section` \| `Paragraph` \| `Table` \| `Figure` \| `Equation` \| `Footnote` \| `PageHeader` \| `PageFooter`) | yes (may be empty) | Marker-observed | Top-level content of the page, in final reading order (Section 3.4). A discriminated union over `block_type`-equivalent kinds, mirroring (but not reusing) Marker's own children-array structure. | +| `is_front_matter` | bool | yes | Marker-observed (heuristic) | True if this page was identified as publisher wrapper content (journal cover, "Submit your article," ISSN-only content, etc.) rather than paper body. | + +**On `is_front_matter`.** Empirical finding 3.8 established that Marker gives no +structural signal distinguishing a wrapper page from a content page — both use +identical block types. This flag is therefore explicitly a **heuristic output of +the Normalizer** (content-pattern based, e.g. presence of "ISSN," "Submit your +article," near-total absence of citation-bearing text), not something copied from +Marker. The field is included now, with its value to be computed later, because the +project's stated requirement is that the schema accommodate this known case without +redesign — per the same logic as the other deferred-population fields in this spec +(Section 22 collects all of them explicitly). + +**Why a discriminated union for `children` rather than `list[Any]` or one generic +`Block` type.** The Raw Marker Model deliberately uses one uniform envelope because +it must stay agnostic to block semantics. The Document Object's job is the opposite: +it exists specifically to make structural type distinctions (a `Table` is not +interchangeable with a `Paragraph` downstream). A discriminated union gives +consumers static type safety and keeps `Page`/`Section` children lists +self-describing in serialized JSON via the discriminator field, with no loss of +ordering since list order is itself the reading-order signal (Section 3.4). + +--- + +## 9. Section + +**Purpose.** A heading-governed grouping of content, derived from Marker's +`section_hierarchy` breadcrumbs (Section 3.5) rather than re-derived from text +pattern-matching on headings. + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `id` | str | yes | Architectural requirement | `canonical_path` built from the governing `SectionHeader` Marker block's own path id. | +| `heading_text` | str | yes | Marker-observed | Verbatim text of the governing `SectionHeader` block. | +| `provenance` | `StructuralProvenance` | yes | Marker-observed | `marker_block_ids = [the SectionHeader block's id]`. | +| `depth` | int | yes | Marker-observed (derived) | Position of this section's heading in the ordered `section_path` list (Section 3.5), zero-indexed from the outermost heading on the page/document. | +| `children` | list of (`Section` \| `Paragraph` \| `Table` \| `Figure` \| `Equation` \| `Footnote`) | yes (may be empty) | Marker-observed | Nested sub-sections and content governed by this heading, in reading order. A `Section` may contain further `Section` objects, giving the hierarchy genuine nesting rather than a flat list with a depth integer alone. | + +**Invariant.** Every leaf or container object elsewhere in the schema that carries +a non-empty `section_path` in its `StructuralProvenance` must have a corresponding +ancestor chain of `Section` objects matching that path exactly — this is guaranteed +by construction (both are derived from the same `section_hierarchy` source, per +Section 3.5) rather than checked as a runtime validator, but it is stated here as a +hard design invariant the Normalizer must not violate. + +**Why `SectionHeader` blocks that are really table/figure labels (e.g. a Marker +`SectionHeader` containing only `"Table 3"`, per Pattern B) do not become `Section` +objects.** Empirical finding 3.1 showed Marker uses the same `SectionHeader` +block type both for genuine paper sections (Methods, Results) and for bare-table +caption labels. The Normalizer must distinguish these by context — a +`SectionHeader` immediately followed by a `Text` block and then a `Table`, with no +intervening structural content, is a caption label being consumed into that +`Table`'s `Caption` (Section 12), not a new `Section`. This rule is recorded here so +the schema's `Section` object is understood to represent only genuine paper +sections; the disambiguation logic itself is Normalizer business logic (out of +scope for this document) but the *consequence* — that some `SectionHeader` Marker +blocks become part of a `Caption` rather than a `Section` — is a structural decision +the schema must support, and it does: `Caption.provenance.marker_block_ids` can +include a `SectionHeader` id (Section 12). + +--- + +## 10. Paragraph + +**Purpose.** A single block of body text — the most common leaf content type. + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `id` | str | yes | Architectural requirement | Per Section 2. | +| `text` | str | yes | Marker-observed | The block's inline HTML content from Marker, **as-is** (e.g. ``, `` tags preserved). | +| `provenance` | `StructuralProvenance` | yes | Marker-observed | `marker_block_ids = [the originating Text/ListItem block's id]`. | + +**Why `text` keeps inline HTML rather than being plain-text-stripped.** Empirical +finding (leaf block dump) confirmed Marker leaf blocks carry real semantic inline +markup (``, ``) directly in their content, not a side annotation. Stripping +it at the Document layer would be a one-way, lossy transformation performed before +any consumer has had a chance to decide whether that markup matters (e.g. a `` +emphasis inside a Methods paragraph could matter to the extraction layer's reasoning +about emphasis on a key term). Per invariant 1.4 (maximum available provenance) and +the general "never lose information without a consumer-side decision to do so" +principle, the Document layer preserves it verbatim; any stripping is a retrieval- +or extraction-layer concern, explicitly out of scope here. + +**Note on `ListItem`/`ListGroup`.** Marker's bibliography uses `ListGroup` containers +of `ListItem` leaves rather than `Text` blocks (this was the basis for separating +references structurally without text pattern-matching). A `ListItem` that is part of +a reference list is **not** modeled as a `Paragraph` — it becomes a `Reference` +(Section 17). A `Paragraph` is reserved for body-text `Text`/generic `ListItem` +content; the Normalizer disambiguates by parent context (a `ListGroup` under the +References section vs. elsewhere), again business logic out of scope here, but the +schema accommodates the distinct outcome via two separate object types. + +--- + +## 11. Caption (supporting type, embedded in Table and Figure) + +**Purpose.** A normalized representation of a table or figure's caption, +collapsing Marker's two empirically observed patterns into one shape. + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `label` | Optional[str] | no | Marker-observed | E.g. `"Table 3"` or `"Figure 1"`. Present whenever a `Caption` block (Pattern A) or a `SectionHeader` label block (Pattern B) was found. | +| `text` | Optional[str] | no | Marker-observed | The descriptive caption sentence. From the `Caption` block's content (Pattern A) or the `Text` block immediately following the label (Pattern B). | +| `trailing_notes` | Optional[str] | no | Marker-observed | The trailing "Note: ..." `Text` block sometimes observed immediately after a `Table`, distinct from both `label`/`text` and from `Footnote` objects (Section 16). Kept as its own field because it was empirically observed to be part of the caption apparatus, not body text, but also not a true Marker `Footnote` block. | +| `provenance` | `StructuralProvenance` | yes | Marker-observed | `marker_block_ids` lists every contributing Marker block (one for Pattern A's single `Caption` block; two or three for Pattern B's `SectionHeader` + `Text` + optional trailing `Text`). Uses `contributing_bboxes` (Section 3.3) rather than a single `bbox` whenever more than one block contributes, since collapsing non-adjacent regions into one bbox would misrepresent the geometry. | + +**Why one normalized shape rather than preserving Marker's two patterns +separately in the schema.** This is the central case the project's "empirically +driven, not speculative" instruction is built around: both patterns were directly +observed (Pattern A on pages 6/10/12, Pattern B on page 7, per finding 3.1), so +normalizing them is not a hypothetical convenience — it is required because every +downstream consumer (extraction layer asking "what is this table about," review UI +displaying "the caption") needs one consistent shape regardless of which pattern the +source PDF happened to produce. Modeling them as two different optional sub-objects +instead would push that disambiguation work onto every consumer rather than once, +inside the Normalizer, where the empirical knowledge of the two patterns actually +lives. + +--- + +## 12. Table + +**Purpose.** A table's logical structure and its evidence-level cell geometry, +kept as two deliberately parallel representations per the empirical recommendation. + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `id` | str | yes | Architectural requirement | Per Section 2. | +| `provenance` | `StructuralProvenance` | yes | Marker-observed | `marker_block_ids = [the Table block's id]` (and the `TableGroup` id too, if Pattern A). | +| `caption` | Optional[`Caption`] | no | Marker-observed | Section 11. `None` only if no caption-bearing blocks were found adjacent to the table at all (not empirically observed in the representative paper, but not excluded as a possibility — captionless tables are not assumed impossible). | +| `raw_html` | str | yes | Marker-observed | The Table block's own `html` field, verbatim — the complete, correctly-nested `
    ...
    ` Marker produces. Treated as the **source of truth for logical structure** (rows, columns, header rows), per the empirical recommendation, precisely because reconstructing structure independently from cell geometry risks disagreeing with Marker's own (already correct) parse. | +| `rows` | list of `TableRow` | yes (may be empty) | Marker-observed (derived) | A structured parse of `raw_html`'s `` elements into row objects (Section 12.1), giving consumers row/column access without re-parsing HTML themselves. Derived from `raw_html`, not an independent reconstruction. | +| `cells` | list of `TableCell` | yes (may be empty) | Marker-observed | The flat list of Marker `TableCell` child blocks, retained **only** as evidence/geometry data (bbox, polygon, provenance) — explicitly not used to derive row/column structure, per the empirical recommendation that `raw_html` is structural truth and `TableCell` geometry is supplementary. | +| `footnote_ids` | list of str | yes (may be empty) | Architectural requirement (deferred population) | Ids of `Footnote` objects geometrically attached to this table (Section 16). Empty until the Normalizer's bbox-proximity heuristic (finding 3.2) runs; the field exists now so that heuristic's output has a defined home without later schema change. | + +### 12.1 TableRow (supporting type, embedded in `Table.rows`) + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `cells` | list of `TableRowCell` | yes | Marker-observed (derived) | Ordered left to right per the source ``. | + +### 12.2 TableRowCell (supporting type, embedded in `TableRow.cells`) + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `text` | str | yes | Marker-observed | Cell text content from the parsed ``/`` element, with any `` wrapper tag stripped and its content treated as equivalent plain text (per empirical finding 3.5 — Marker inconsistently wraps numerically identical `mean ± stderr` values in `` depending on OCR path; the schema does not preserve this distinction since it carries no structural meaning, only an OCR-routing artifact). | +| `is_header` | bool | yes | Marker-observed | True if the source element was ``, false for ``. | +| `structural_notes` | Optional[str] | no | Architectural requirement (deferred) | A free-text slot reserved for a Normalizer-attached structural annotation — most notably, a suspected merged-cell placeholder (empirical finding 3.4: Marker silently flattens merged header cells into duplicated rows with an empty filler cell, with no flag distinguishing this from a genuinely empty cell). The heuristic for populating this field is explicitly **not** decided in this specification — finding 3.4 was flagged as needing more representative papers before a reconstruction rule is chosen. The field is included as an open slot precisely so that decision can be made later without a schema change, consistent with the brief's instruction to accommodate known structural cases without redesign. | + +**Why `TableRowCell` does not have `row_index`/`col_index` integers.** These are +implicit in `Table.rows`' list-of-lists structure itself (a cell's row is its +containing `TableRow`'s position in `rows`; its column is its own position in +`cells`), so adding redundant integer fields would duplicate information already +present in list order, with no Marker-observed justification for storing it twice. + +**Why `TableRowCell` has no `rowspan`/`colspan` field.** Empirical finding 3.4 +confirmed Marker's HTML output never emits `rowspan`/`colspan` attributes even where +the source PDF visually has merged cells — it flattens instead. Adding a +`rowspan`/`colspan` field for a case never observed in Marker's actual output would +violate the "no speculative fields" instruction. If a future representative paper +demonstrates Marker does emit span attributes under some condition, this is the +single place such fields would be added. + +### 12.3 TableCell (supporting type, embedded in `Table.cells`; evidence-only) + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `id` | str | yes | Architectural requirement | Per Section 2; path derived from the Marker `TableCell` block's own id. | +| `text` | str | yes | Marker-observed | Verbatim cell content (not math-stripped here — this object is evidence/geometry, not the logical text consumers should read; `TableRowCell.text` is the cleaned version). | +| `bbox` | `BoundingBox` | yes | Marker-observed | Per-cell geometry, the entire reason this parallel representation is retained (evidence highlighting in the review UI). | +| `polygon` | `Polygon` | yes | Marker-observed | Mirrors `bbox`. | + +**Why this evidence-only `TableCell` and the logical `TableRowCell` are not unified +into one type.** Empirical finding 3.3 established these are genuinely two +different, only partially-corresponding representations Marker provides in +parallel — one (the `` HTML) has correct logical structure but no per-cell +geometry, the other (`TableCell` children) has per-cell geometry but no row/column +index. Forcing them into a single type would require either fabricating row/column +indices on the geometry side (an unverified bbox-clustering reconstruction the +findings explicitly flagged as risky) or discarding per-cell geometry on the logical +side (losing the evidence-highlighting capability entirely). Keeping them separate, +each true to what Marker actually provides, is the choice that adds no +unverified inference. **Open implementation note, not a schema decision:** +positionally correlating a given `TableRowCell` with its corresponding `TableCell` +(for evidence highlighting of a specific logical cell) is left to the Normalizer to +attempt via parse-order correspondence; this spec does not assert that +correspondence is guaranteed, since it was not empirically verified. + +--- + +## 13. Figure + +**Purpose.** A figure region, with its caption normalized the same way as `Table`. + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `id` | str | yes | Architectural requirement | Per Section 2. | +| `provenance` | `StructuralProvenance` | yes | Marker-observed | `marker_block_ids = [the Figure block's id]` (and `FigureGroup` id, if present). | +| `caption` | Optional[`Caption`] | no | Marker-observed | Section 11. Empirically, `FigureGroup` always pairs `[Figure, Caption]` in that order (mirroring `TableGroup`'s pairing, just with reversed order — confirmed, not assumed, per the findings doc). | +| `image_data` | Optional[bytes] | no | Marker-observed | Base64-decoded raster image content from Marker's `images` field, when present. Empirically, in the representative paper, only `Picture` blocks (journal logo, cover thumbnail) carried non-empty `images`; the one `Figure` block had `images: {}`. This field is therefore included (figures plausibly can carry raster data, and the project must not assume they never will) but its emptiness in the current evidence base is recorded explicitly in Section 22 as unconfirmed, not silently assumed resolved. | + +--- + +## 14. Equation + +**Purpose.** A mathematical expression block. + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `id` | str | yes | Architectural requirement | Per Section 2. | +| `provenance` | `StructuralProvenance` | yes | Marker-observed | `marker_block_ids = [the Equation block's id]`. | +| `raw_math` | str | yes | Marker-observed | The verbatim MathML-ish `` content, including any equation number embedded inline (e.g. `"DP = I - IR + P - ETc \pm VR, \qquad (1)"`), per empirical finding 3.7. | +| `equation_number` | Optional[str] | no | Architectural requirement (deferred) | A slot for the parsed-out equation number (e.g. `"1"`), since finding 3.7 confirmed Marker provides no separate field for it — any cross-reference resolution ("using equation (1)" in body text) requires parsing it out of `raw_math`. The parsing logic itself is out of scope for this spec; the field exists so its result has a defined home. | + +--- + +## 15. Footnote + +**Purpose.** A footnote block, with its attachment to a table or figure resolved +geometrically rather than structurally, per empirical finding 3.2. + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `id` | str | yes | Architectural requirement | Per Section 2. | +| `provenance` | `StructuralProvenance` | yes | Marker-observed | `marker_block_ids = [the Footnote block's id]`. Note: this block's own provenance never implies attachment — footnotes are flat page-level siblings, not children of any Table/Figure (finding 3.2), so attachment is recorded separately below. | +| `raw_text` | str | yes | Marker-observed | Verbatim footnote content. | +| `attached_object_id` | Optional[str] | no | Architectural requirement (deferred) | The id of the `Table` or `Figure` this footnote was determined to belong to, via the Normalizer's bbox-proximity heuristic ("nearest preceding Table/Figure on the same page by bbox y-position," per finding 3.2). `None` when unresolved — tracked in aggregate by `Statistics.unresolved_footnote_count` (Section 7). The heuristic itself is Normalizer logic, out of scope here; the field exists so its output, including the legitimate possibility of non-resolution, has a defined, queryable home. | + +**Why attachment is nullable rather than required.** Forcing every footnote to +resolve to a table/figure would hide genuine ambiguity (e.g. a footnote whose +geometric position is equidistant between two candidates, or a page-level +disclaimer footnote unrelated to any table) behind an incorrect best-guess. Per the +project's broader principle (already established for the IR: "fields that cannot be +resolved are marked with an unresolved status rather than silently filled"), the +same discipline applies at the structural layer: `None` is a legitimate, recorded +outcome, not an implementation gap to paper over. + +--- + +## 16. Reference (Bibliography Entry) + +**Purpose.** One bibliography entry, structurally distinguished from body text +because Marker represents references via `ListGroup`/`ListItem`, not `Text` blocks +(observed directly in the page_stats/structure walkthrough, not inferred from +content). + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `id` | str | yes | Architectural requirement | Per Section 2. | +| `provenance` | `StructuralProvenance` | yes | Marker-observed | `marker_block_ids = [the ListItem block's id]`. | +| `raw_text` | str | yes | Marker-observed | Verbatim reference entry text, including any inline markup Marker preserved. | + +**Boundary note.** Like `Metadata` (Section 5), `Reference` deliberately stops at +verbatim text. Parsing a reference string into author/year/journal/DOI fields is +citation-matching — a semantic operation belonging to the IR's `Citation` entity, +not this layer. This object's only job is to say "this `ListItem`, structurally, +is a bibliography entry, not body text," which is information Marker's block typing +already gives for free via the `ListGroup` container. + +--- + +## 17. PageHeader / PageFooter + +**Purpose.** Repeated journal running-header/footer content (e.g. the journal name +repeated on every page, or page-footer branding), retained for completeness and +front-matter heuristics (Section 8) but not expected to be consumed by extraction. + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `id` | str | yes | Architectural requirement | Per Section 2. | +| `provenance` | `StructuralProvenance` | yes | Marker-observed | `marker_block_ids = [the PageHeader/PageFooter block's id]`. | +| `raw_text` | str | yes | Marker-observed | Verbatim content. | + +Modeled as two distinct types (`PageHeader`, `PageFooter`) rather than one generic +"running content" type, simply mirroring Marker's own distinct block types +one-to-one — there is no structural reason to merge them, and merging would lose +the type distinction Marker itself already makes. + +--- + +## 18. Cross-Object Relationships & Invariants Summary + +This section consolidates relationship rules stated piecemeal above, for a single +point of reference. + +- **Containment is exclusively via `children` lists and explicit id-reference + fields (`footnote_ids`, `attached_object_id`) — never via implicit ordering + conventions or id-string parsing.** Any consumer needing "what footnotes belong + to this table" reads `Table.footnote_ids`, never re-derives it from geometry + itself; the Normalizer computes that relationship exactly once. +- **Reading order is a single global property (Section 3.4), independent of any + per-object containment.** Two sibling objects under different `Section`s can be + compared for relative reading order via their `reading_order_index` without + needing to know anything about section nesting. +- **Every non-`Document` object carries exactly one `StructuralProvenance`,** which + is the only place Marker block ids appear outside of `ProcessingMetadata`'s + artifact reference. No object duplicates Marker ids elsewhere in its own fields. +- **No object type defined in this specification has a field referencing an IR, + retrieval, validation, or export concept**, satisfying invariant 1.6 by + construction — this is checked by inspection of this document, not by a runtime + rule, since it is a closed schema with a fixed object list. + +--- + +## 19. Serialization Requirements + +- All models use `model_config = ConfigDict(frozen=True, extra="forbid")`. Unlike + the Raw Marker Model (which intentionally used `extra="allow"` for forward + compatibility with unknown future Marker fields), the Document Object is the project's own + designed contract — an unexpected extra field here indicates a Normalizer bug, + not a benign future Marker addition, so it should fail loudly (`extra="forbid"`) + rather than silently passing through. +- `model_dump_json()` must be deterministic for a given object graph: field order + follows declaration order (Pydantic v2 default), list order follows the + semantically meaningful order already specified per field (reading order for + children, left-to-right for table cells, outermost-to-innermost for + `section_path`) — never a non-deterministic order like dict-hash order. + `datetime` fields serialize as ISO 8601 strings in UTC. +- Every model must round-trip losslessly through `model_dump()` → + `model_validate()` and `model_dump_json()` → `model_validate_json()`, mirroring + the test discipline already established and passing for the Raw Marker Model. +- Bytes fields (`Figure.image_data`) serialize as base64 strings in JSON, matching + Marker's own convention for `images`, so no separate encoding scheme is + introduced at this layer. + +--- + +## 20. Validation Rules + +These are construction-time invariants enforced by each model's own validators, +distinct from the cross-cutting invariants in Section 1 (which are policies the +Normalizer must follow, not all individually mechanically checkable). + +- `BoundingBox`: `x1 >= x0` and `y1 >= y0`. +- `StructuralProvenance`: `marker_block_ids` has at least one element; + `reading_order_index >= 0`; exactly one of `bbox` or `contributing_bboxes` (or + neither, for objects with genuinely no recoverable geometry) is populated — never + both, to avoid two disagreeing geometric claims about the same object. +- `Document`: `pages` non-empty; `page_number` values across `pages` are unique. +- `Page`: `page_number >= 0`. +- `Table`: if `rows` is non-empty, every `TableRow.cells` list has at least one + element (a row with zero cells is not a meaningful row — such input indicates a + parse error in `raw_html`, which should surface as a Normalizer-time error, not a + silently-accepted empty row in the Document Object). +- `Footnote`: no validation forces `attached_object_id` to be set — its absence is + valid by design (Section 15). +- `Statistics`: every count field is `>= 0`; `unresolved_footnote_count <= + footnote_count` (a basic sanity bound the model itself can check independent of + whatever produced the numbers). + +--- + +## 21. Explicitly Deferred — Not Modeled, By Design + +Per the instruction to avoid speculative fields, the following structural cases +identified during evaluation are **intentionally absent** from this schema rather +than represented with a guessed-at field shape, because the representative paper +set does not yet provide enough evidence to know what shape is correct: + +- **Multi-page table continuation.** Not observed in the representative paper (no + table spans a page break). No `continues_on_page` / `continuation_of_table_id` + field is added speculatively. When a representative paper exhibiting this is + evaluated, this section is where such a field would be added — as an addition, + not a redesign, since `Table` already has a stable `id` to reference. +- **Multi-panel figure decomposition.** The representative paper's Figure 3 has 10 + visually lettered sub-panels under one shared caption, but Marker recorded it as + a single flat `Figure` block with no internal panel structure. Since this is the + only data point (n=1) and it shows Marker *not* decomposing panels, no `panels: + list[FigurePanel]` field is added on the strength of a PDF-visual observation that + contradicts what Marker itself outputs. If a future paper shows Marker does + sometimes decompose panels, this is where that field would be introduced. +- **TableGroup-vs-bare-Table triggering condition.** Both patterns are modeled + (via `Caption`'s flexible provenance, Section 11), but *why* Marker chooses one + over the other (single table per region vs. dense multi-table page, per the one + data point available) is not encoded as a schema concept — it doesn't need to be, + since the Document Object normalizes both outcomes into the same `Caption` shape + regardless of cause. +- **Merged-cell reconstruction heuristic.** The *slot* (`TableRowCell.structural_ + notes`) exists (Section 12.2), but the specific rule for populating it (e.g. + "empty cell directly below a filled cell in the same column ⇒ merged-placeholder + suspected") is explicitly not decided here, per finding 3.4's own conclusion that + this needs more representative papers first. + +--- + +## 22. Summary — Field Origin Distribution + +A consolidated view of the distinction requested for this specification: how many +fields per object are direct Marker carry-overs versus existing purely to satisfy +an architectural requirement (provenance, determinism, reproducibility, +serialization) versus reserved as a deferred-population slot for a Normalizer +heuristic not yet designed. + +| Object | Marker-observed fields | Architectural-requirement fields | Deferred-population slots | +|---|---|---|---| +| Document | `pages` | `id`, `source_pdf_identifier`, `metadata`, `processing_metadata`, `statistics` | — | +| Metadata | `title`, `page_count`, `has_front_matter_page` | — | — | +| ProcessingMetadata | `marker_version` | `normalizer_version`, `processed_at`, `source_marker_artifact_ref` | — | +| Statistics | — | all count fields | — | +| Page | `page_number`, `children`, `is_front_matter` (heuristic) | `id`, `provenance` | — | +| Section | `heading_text`, `depth`, `children` | `id`, `provenance` | — | +| Paragraph | `text` | `id`, `provenance` | — | +| Caption | `label`, `text`, `trailing_notes` | `provenance` | — | +| Table | `raw_html`, `rows`, `cells`, `caption` | `id`, `provenance` | `footnote_ids` | +| TableRowCell | `text`, `is_header` | — | `structural_notes` | +| TableCell | `text`, `bbox`, `polygon` | `id` | — | +| Figure | `caption`, `image_data` | `id`, `provenance` | — | +| Equation | `raw_math` | `id`, `provenance` | `equation_number` | +| Footnote | `raw_text` | `id`, `provenance` | `attached_object_id` | +| Reference | `raw_text` | `id`, `provenance` | — | +| PageHeader/PageFooter | `raw_text` | `id`, `provenance` | — | + +--- + +## 23. Exit Criteria for This Specification + +This specification is ready to be frozen and handed to implementation once: + +1. Every object above has a 1:1 or many:1 mapping back to either an observed + Marker block type or a named architectural requirement (satisfied throughout + this document via the "Origin" column on every field table). +2. No field exists whose justification is "might be useful later" rather than + "Marker provides this" or "the architecture requires this for provenance / + determinism / reproducibility / serialization / validation" (satisfied; the one + category that looks speculative — deferred-population slots — is explicitly + justified by the stated requirement to avoid future schema redesign, and is + listed exhaustively in Section 22's third column plus Section 21's explicit + exclusions). +3. No object or field encodes scientific meaning (satisfied — verified against + invariant 1.2 by inspection of the full object list in Sections 4–17). +4. Implementing this specification in Pydantic v2 requires translation, not new + design decisions — the identifier rule (Section 2), provenance rule (Section + 3.3), reading-order rule (Section 3.4), and every per-object field table are + concrete enough to type directly. + +Once reviewed and approved, the next phase is the mechanical translation of this +document into immutable Pydantic v2 models, followed by the Normalizer that +populates them from the Raw Marker Model. diff --git a/docs/document_schema_specification_v1.1.md b/docs/document_schema_specification_v1.1.md new file mode 100644 index 0000000..bbf8deb --- /dev/null +++ b/docs/document_schema_specification_v1.1.md @@ -0,0 +1,826 @@ +# Document Schema Specification + +**Project:** LLM-Assisted Extraction of Agronomic and Ecological Experiments into Structured Data +**Layer:** Document Understanding Layer (Document Object only) +**Version:** 1.1 +**Status:** Frozen — implementation contract +**Supersedes:** Version 1.0 (frozen, preserved unmodified as the historical approved specification at `document_schema_specification_v1.0.md`) +**Predecessor artifact:** Raw Marker Model (`marker_adapter/raw_model.py`), frozen +**Empirical basis:** `Marker Output — Empirical Findings (Paper 1: Nutrient Cycling, Smukler et al. 2012)` + +This document is an engineering specification, not an implementation. It defines every +object in the canonical Document layer — its purpose, fields, types, relationships, +identifier rule, provenance rule, validation rules, and serialization requirements — +so that implementing the Pydantic v2 Document Object later requires translation, not +design. No parsing, normalization, or business logic is described here; only the shape +of the data those future components will populate. + +--- + +## Changelog + +**Version 1.1** (current). Resolves a structural omission discovered during +implementation of `Section` (Document Understanding Layer build): `Reference` was +defined as a top-level object type with a `Statistics.reference_count` field implying +its instances are countable across the document tree, but no `Page.children` or +`Section.children` union in Version 1.0 included `Reference` as a member — leaving +`Reference` objects with no defined place in the structural tree. This is a minimal, +scoped correction, not a re-opening of the schema design: + +- `Reference` is added as a valid member of `Section.children` (Section 9). It is + **not** added to `Page.children` (Section 8) — references are always governed by a + "References" heading, structurally identical to any other body section, so this is + the same containment path every other Section-only-bound object would take, not a + new containment rule. +- `NodeKind.REFERENCE` is added so `Reference` can participate in the same + discriminated-union mechanism every other `Section.children` member already uses. +- `Reference` (Section 16) gains a required `kind` discriminator field, following the + exact pattern already used by every other discriminated-union member (`Section`, + `Paragraph`, `Table`, `Figure`, `Equation`, `Footnote`). + +No other field, object, relationship, or invariant defined in Version 1.0 is altered. +Version 1.0 remains the historical record of what was originally approved; Version 1.1 +is the current implementation contract. + +--- + +## 0. Relationship to the Raw Marker Model + +The Raw Marker Model is a lossless, uninterpreted mirror of Marker's JSON output: a +single `MarkerBlock` envelope type, `extra="allow"`, no discriminated union, no +semantic interpretation. The Document Object described here is the next layer down +the pipeline: it is produced *from* the Raw Marker Model by the Normalizer (not yet +implemented) and is the first place where **structural** interpretation occurs — +deciding what counts as a Table, a Section, a Footnote's likely attachment — while +still containing zero scientific meaning. + +Every Document-layer object therefore exists in addition to, not instead of, the Raw +Marker Model. The Raw Marker Model remains the permanent ground truth on disk; +the Document Object is a derived, queryable, typed structural view over it. This +specification assumes the Raw Marker Model is available as an immutable input and +focuses entirely on what the Normalizer must produce from it. + +--- + +## 1. Architectural Invariants + +These rules are not per-object — they govern the entire Document layer and every +object defined below conforms to them without restating them per-section. + +**1.1 Immutability.** Every Document-layer model is frozen after construction +(Pydantic v2 `model_config = ConfigDict(frozen=True)`). No object is mutated after +the Normalizer finishes building it. Corrections, re-interpretation, or review +happen in later layers (IR, Scientist Review) and never write back into the +Document Object. + +**1.2 Structural-only content.** No Document-layer object may contain a field whose +purpose is to record scientific meaning. Concretely: no `treatment`, `species`, +`observation`, `management_event`, `variable`, or `trait` field exists anywhere in +this schema, even as an optional placeholder. If a future need arises to record such +a concept, it belongs in the IR, which is built on top of — never inside — the +Document Object. + +**1.3 Deterministic identifiers.** No identifier in this schema is a UUID4 or any +other non-deterministic value. Every identifier is a pure function of stable inputs, +so that re-running the same PDF through the same Marker version and the same +Normalizer version always yields byte-identical identifiers. The exact construction +rule is given in Section 2. + +**1.4 Maximum available provenance.** Every object that originates from one or more +Marker blocks retains a `StructuralProvenance` value (Section 3.3) referencing the +originating Marker block id(s), page number, bounding box, polygon, and reading-order +position. An object is never permitted to "lose" its Marker origin even when the +Normalizer reshapes or merges multiple Marker blocks into one Document object (e.g. +turning a `SectionHeader` + `Text` pair into one normalized `Caption`). + +**1.5 Deterministic, lossless serialization.** Every model in this schema must +support `model_dump()` / `model_dump_json()` and round-trip back through +`model_validate()` without information loss, exactly as already verified for the +Raw Marker Model. Field ordering in dumped JSON is determined by declaration order +in the Pydantic model (not insertion order at runtime) to keep serialized output +byte-stable across runs. + +**1.6 Independence from downstream layers.** Nothing in this schema imports from, +references, or anticipates retrieval, LLM extraction, validation, the IR, or BETYdb +export. The Document Object's public surface is consumed by those layers, but this +schema has zero knowledge of them. + +--- + +## 2. Identifier Strategy + +**Rule.** Every Document-layer object's `id` is computed as: + +``` +id = "doc:" + sha256( document_id + "|" + canonical_path )[:16] +``` + +where `document_id` is the parent Document's own id (Section 4), and +`canonical_path` is a deterministic structural path string specific to each object +type, defined per-object below (generally derived from the originating Marker +block's own path-like id, e.g. `/page/7/Table/2`, when one exists 1:1; or, for objects +synthesized from multiple Marker blocks or with no direct Marker counterpart — such +as a parsed `TableRow` — a path built from the parent object's id plus an ordinal +position among deterministically-ordered siblings, e.g. `.../Table/2/row/3`). + +**Why a hash rather than reusing Marker's path id directly.** Marker's own ids +(`/page/7/Table/2`) are positional/index-based — `Table/2` means "third +Table-typed block encountered in that page's traversal." If a future Marker version +changes internal traversal order, encounters a new block type, or reorders block +discovery, these indices could silently shift between runs on an unchanged PDF, +producing different "stable" ids for the same content. Hashing a path that is +itself still derived from Marker's structure, combined with the document id, +preserves determinism for a fixed Marker/adapter version while making the contract +explicit: **stability is guaranteed within one Marker version, not promised across +Marker upgrades.** The original Marker path is never discarded — it is preserved +verbatim inside every object's `StructuralProvenance.marker_block_id` — so a Marker +version bump that changes traversal order is detectable (ids change) and +diagnosable (provenance still shows the old vs. new Marker ids). + +**document_id construction.** `document_id = "betydoc:" + sha256(source_pdf_identifier)[:16]`, +where `source_pdf_identifier` is a stable external identifier for the source PDF +(DOI if known, else a content hash of the source PDF bytes). This deliberately +excludes Marker version and timestamp from the identity computation: the same PDF +must always resolve to the same `document_id` so that re-processing (e.g. after a +Normalizer bug fix) updates the same logical Document Object rather than minting an +unrelated one. Marker version and processing time are recorded as **metadata about +the materialization**, not folded into identity — see `ProcessingMetadata` (Section +6). + +**Properties guaranteed by this scheme:** +- Same PDF + same Marker version + same Normalizer version ⇒ identical ids + throughout the tree. +- Ids are opaque strings, safe to use as dictionary keys, filenames, or database + foreign keys. +- Every id is traceable backward to a concrete Marker block via + `StructuralProvenance`, satisfying invariant 1.4. + +--- + +## 3. Foundational Supporting Types + +These are not top-level entities; they are embedded value objects used throughout +the schema. + +### 3.1 BoundingBox + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `x0` | float | yes | Marker-observed | Left edge | +| `y0` | float | yes | Marker-observed | Top edge | +| `x1` | float | yes | Marker-observed | Right edge | +| `y1` | float | yes | Marker-observed | Bottom edge | + +Directly carried over from Marker's `bbox` (already typed as `MarkerBBox` in the Raw +Marker Model). Retained at the Document layer because footnote-to-table attachment, +evidence highlighting in the Scientist Review UI, and any future geometric +reconstruction (e.g. merged-cell heuristics) all require it. **Invariant:** `x1 >= +x0` and `y1 >= y0`; the Normalizer is responsible for not constructing a violating +instance, but the model also validates this on construction since the cost of +allowing silently-inverted boxes downstream is high (evidence UI would render +boxes wrong with no error signal). + +### 3.2 Polygon + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `points` | list of 4 `(float, float)` pairs | yes | Marker-observed | Carried over from Marker's `polygon` | + +Retained even though `BoundingBox` is derivable from it, because Marker provides +both independently and the polygon can in principle capture skew that an +axis-aligned bbox cannot. This is a direct empirical carry-over (already present and +typed in the Raw Marker Model) rather than a new design — Document-layer objects +simply forward it unchanged. No Document-layer object computes one from the other; +both come from Marker as-is. + +### 3.3 StructuralProvenance + +This is the single most important supporting type in the schema — it is what +satisfies invariant 1.4 for every object below. + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `marker_block_ids` | list of str | yes (min length 1) | Marker-observed | The originating Marker block id(s), e.g. `["/page/7/Table/2"]`. A list, not a single value, because some Document objects (e.g. a normalized `Caption` under Pattern B) are synthesized from more than one Marker block. | +| `page_number` | int | yes | Marker-observed | The PDF page this object originates from. For objects spanning conceptually across the synthesis of multiple Marker blocks, this is the page of the primary/first contributing block. | +| `bbox` | `BoundingBox` | no | Marker-observed | Present for any object with a single, well-defined originating region. Absent for objects synthesized from multiple non-adjacent blocks where a single bbox would be misleading (e.g. a Pattern-B caption combining a `SectionHeader` far above a trailing `Text` note) — in that case `contributing_bboxes` is populated instead. | +| `contributing_bboxes` | list of `BoundingBox` | no | Marker-observed | Used instead of (or in addition to) `bbox` when more than one Marker block contributes geometry, preserving each one rather than collapsing them into a single misleading box. | +| `polygon` | `Polygon` | no | Marker-observed | Mirrors `bbox`'s optionality logic. | +| `reading_order_index` | int | yes | Architectural requirement | The object's position in the document's global linear reading order (Section 3.4). Required on every provenance instance because every structural object has a place in reading order even if its bbox is ambiguous. | +| `section_path` | list of str | yes (may be empty) | Marker-observed (derived) | The chain of governing `SectionHeader` Marker-block ids from Marker's own `section_hierarchy` map, ordered outermost to innermost. Empty only for objects outside any section (e.g. a journal wrapper page's `Picture`). | + +**Why a list of Marker block ids rather than exactly one.** Empirically, not every +Document-layer concept maps 1:1 to a Marker block. The clearest case is `Table` +captions: under Pattern A (`TableGroup`), the caption is one `Caption` block; under +Pattern B (bare `Table`), the equivalent information is split across a +`SectionHeader` block and a `Text` block, sometimes with a trailing "Note:" `Text` +block. Forcing a single-id provenance field would require silently picking one +contributing block and losing the others. A list preserves all of them, satisfying +invariant 1.4 even when normalization merges several Marker blocks into one +Document concept. + +### 3.4 Reading Order + +Reading order is **not** a field on a supporting type — it is a global integer +sequence assigned by the Normalizer to every leaf and container object during +construction, equal to that object's position in a single depth-first traversal of +the final Document Object tree, in `children` array order. + +This decision is made explicitly here because it was a confirmed empirical finding, +not a default assumption: Marker's own block id local-index numbers (e.g. the +trailing `4` in `/page/7/Footnote/4`) are **not** monotonic with true reading order — +`Footnote/4` and `Footnote/5` physically appear, in the actual children array, after +`Table/8`, despite having lower index numbers. Reading order must therefore be +(re)computed by the Normalizer from final tree position, never inferred from Marker's +id numbering. `StructuralProvenance.reading_order_index` is this recomputed value, +not a copy of any number embedded in a Marker id string. + +### 3.5 Section Path + +`StructuralProvenance.section_path` is populated directly from Marker's own +`section_hierarchy` dict, which the empirical findings confirmed is already a +precomputed breadcrumb (e.g. a deeply nested `TableCell` carrying +`{'1': '/page/1/SectionHeader/1', '4': '/page/7/SectionHeader/0'}`). Two properties +of this dict are carried forward into the spec rather than assumed away: + +- **Depth keys are not contiguous small integers.** The observed keys were `'1'` + and `'4'`, not `'1'` and `'2'`, indicating these correspond to some absolute + nesting depth from Marker's internal traversal rather than a clean rank. The + Document schema therefore stores `section_path` as an **ordered list of + SectionHeader Marker-block ids** (sorted by the numeric value of their original + dict key) rather than preserving Marker's dict-with-gaps shape — this gives + downstream consumers (Retrieval layer, Section nesting) a clean, ordinary list + without forcing them to understand Marker's internal depth-key semantics. +- **The mapping is per-block, not per-Section-object.** Every Marker block — + including deeply nested ones like a `TableCell` — carries its own full path. The + Document Object's `Section` containment hierarchy (Section 9) is derived from this + same data, so `section_path` on any object and that object's ancestor `Section` + chain are guaranteed consistent by construction, not by a separate invariant check. + +--- + +## 4. Document + +**Purpose.** The root container for one processed paper. Holds the page sequence, +top-level metadata, processing metadata, and aggregate statistics. Exactly one +`Document` exists per source PDF per Normalizer run. + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `id` | str | yes | Architectural requirement | `document_id`, Section 2. | +| `source_pdf_identifier` | str | yes | Architectural requirement | The stable external identifier used to compute `id` (DOI or content hash). Stored explicitly so the id's derivation is independently checkable, not just trusted. | +| `metadata` | `Metadata` | yes | Marker-observed + Architectural | Section 5. | +| `processing_metadata` | `ProcessingMetadata` | yes | Architectural requirement | Section 6. | +| `statistics` | `Statistics` | yes | Architectural requirement | Section 7. | +| `pages` | list of `Page` | yes (min length 1) | Marker-observed | Ordered by page number ascending; this ordering is also the top level of global reading order. | + +**Invariants.** +- `pages` is non-empty and sorted ascending by `Page.page_number` with no + duplicate or skipped page numbers other than what Marker itself reported (a + Marker-side page omission is preserved, not silently re-numbered). +- `Document` is the only object in this schema with no `StructuralProvenance` of + its own (there is no single Marker block representing "the whole document" — the + Raw Marker Model's root node was empirically confirmed to have no `id`, `bbox`, + or `polygon` at all). Its provenance is implicitly "the entire Raw Marker Model + file," which `processing_metadata.source_marker_artifact_ref` captures (Section + 6) rather than a `StructuralProvenance` instance. + +--- + +## 5. Metadata + +**Purpose.** Bibliographic and identification facts about the paper, to the extent +they are structurally recoverable (not semantically extracted — see the boundary +note below). + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `title` | Optional[str] | no | Marker-observed | Taken verbatim from the first/top-level `SectionHeader` or title-styled block on the front matter page, if structurally identifiable. | +| `page_count` | int | yes | Marker-observed | Count of `Page` objects; redundant with `len(pages)` but kept as an explicit field since `Statistics` (Section 7) is meant to hold *derived counts*, while this one is a basic identifying fact worth surfacing without traversing the tree. | +| `has_front_matter_page` | bool | yes | Marker-observed (heuristic) | Whether page 0 (or any page) was structurally flagged as publisher wrapper content. See `Page.is_front_matter` (Section 8) for the per-page flag this aggregates. | + +**Boundary note.** `Metadata` deliberately does **not** include authors, journal +name, publication year, or DOI as structured fields, even though these are +intuitively "metadata." Per the empirical findings (3.8), front-matter and +citation-bearing content is **structurally indistinguishable** from other text at +the block-type level — recovering "the authors" or "the journal" requires reading +and interpreting text content, which is scientific/semantic extraction, not +structural parsing. That work belongs to the IR's `Citation` entity (already +specified in the project's broader IR design), built by the extraction layer. This +spec only exposes what is mechanically true of the page structure (title block +location, page count, front-matter flag) — adding speculative `author`/`doi`/`year` +fields here would violate invariant 1.2 and the "no speculative fields" instruction, +since populating them correctly is not a structural operation. + +--- + +## 6. ProcessingMetadata + +**Purpose.** Records *how* this particular Document Object was produced, separate +from *what* it identifies (Section 4's `id`/`source_pdf_identifier`). This is what +makes reproducibility checkable: two Document Objects with the same `id` but +different `ProcessingMetadata` indicate the same paper was processed by a different +Marker or Normalizer version, which is exactly the signal needed to detect drift +without conflating it with document identity. + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `marker_version` | str | yes | Marker-observed | Verbatim from Marker's own output metadata, if present; otherwise the version string of the Marker invocation recorded by the adapter. | +| `normalizer_version` | str | yes | Architectural requirement | Semantic version of the Normalizer code that produced this Document Object. Required so a future schema/logic change is always attributable. | +| `processed_at` | datetime (ISO 8601, UTC) | yes | Architectural requirement | Wall-clock time of this materialization. Explicitly **not** part of `id` computation (Section 2) — recorded for audit/debugging only. | +| `source_marker_artifact_ref` | str | yes | Architectural requirement | A path or content hash identifying the exact Raw Marker Model JSON file this Document Object was normalized from, satisfying the "Document has no own provenance" note in Section 4 by pointing at the file-level artifact instead of a block-level one. | + +**Why this is architectural rather than Marker-observed for most fields.** Only +`marker_version` comes from Marker itself; the rest exist purely because the +project's stated reproducibility requirement ("identical PDFs ... should always +produce identical Document Objects," and detectability of drift) demands a place to +record the inputs that determine reproducibility, even though Marker's own output +has no opinion on them. + +--- + +## 7. Statistics + +**Purpose.** Aggregate counts over the final Document Object tree, useful for +sanity-checking a Normalizer run (e.g. "did this paper produce zero tables when the +PDF clearly has six") without re-traversing the tree ad hoc. + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `page_count` | int | yes | Architectural requirement (derived) | `len(pages)`. | +| `section_count` | int | yes | Architectural requirement (derived) | Total `Section` objects across the document. | +| `paragraph_count` | int | yes | Architectural requirement (derived) | Total `Paragraph` objects. | +| `table_count` | int | yes | Architectural requirement (derived) | Total `Table` objects. | +| `figure_count` | int | yes | Architectural requirement (derived) | Total `Figure` objects. | +| `equation_count` | int | yes | Architectural requirement (derived) | Total `Equation` objects. | +| `footnote_count` | int | yes | Architectural requirement (derived) | Total `Footnote` objects. | +| `reference_count` | int | yes | Architectural requirement (derived) | Total `Reference` objects. As of Version 1.1, `Reference` objects are reachable as `Section.children` members under a "References" `Section`, so this is a true traversal count, not a count over an out-of-tree collection. | +| `unresolved_footnote_count` | int | yes | Architectural requirement (derived) | Footnotes whose `attached_object_id` (Section 16) is `None` after Normalizer processing — a direct, queryable signal of how much of the geometric-attachment heuristic (empirical finding 3.2) succeeded on this paper. | + +**Why this object exists at all, given everything in it is derivable.** Every +field here is computable by traversal, so in principle `Statistics` adds no new +information. It exists as an explicit object — rather than leaving consumers to +compute it themselves — because (a) it gives a single, serializable snapshot for +logging/comparison across Normalizer runs without re-parsing the whole tree, and (b) +`unresolved_footnote_count` specifically operationalizes a concern raised directly +in the empirical findings (footnote attachment is a heuristic, not guaranteed) into +a number that can be tracked across the representative paper set as the Normalizer +is built and tuned. + +--- + +## 8. Page + +**Purpose.** One PDF page's structural content, in reading order. + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `id` | str | yes | Architectural requirement | Per Section 2; `canonical_path = "/page/{page_number}"`. | +| `page_number` | int | yes | Marker-observed | Zero-indexed, matching Marker's own page numbering. | +| `provenance` | `StructuralProvenance` | yes | Marker-observed | `marker_block_ids = [the Marker Page block's id]`. | +| `children` | list of (`Section` \| `Paragraph` \| `Table` \| `Figure` \| `Equation` \| `Footnote` \| `PageHeader` \| `PageFooter`) | yes (may be empty) | Marker-observed | Top-level content of the page, in final reading order (Section 3.4). A discriminated union over `block_type`-equivalent kinds, mirroring (but not reusing) Marker's own children-array structure. | +| `is_front_matter` | bool | yes | Marker-observed (heuristic) | True if this page was identified as publisher wrapper content (journal cover, "Submit your article," ISSN-only content, etc.) rather than paper body. | + +**On `is_front_matter`.** Empirical finding 3.8 established that Marker gives no +structural signal distinguishing a wrapper page from a content page — both use +identical block types. This flag is therefore explicitly a **heuristic output of +the Normalizer** (content-pattern based, e.g. presence of "ISSN," "Submit your +article," near-total absence of citation-bearing text), not something copied from +Marker. The field is included now, with its value to be computed later, because the +project's stated requirement is that the schema accommodate this known case without +redesign — per the same logic as the other deferred-population fields in this spec +(Section 22 collects all of them explicitly). + +**On `Reference`'s absence from this union (post Version 1.1).** `Reference` was +added to `Section.children` in Version 1.1 but deliberately not to `Page.children`. +A `Reference` only ever appears under a governing "References" `Section`, never as a +direct child of `Page` with no intervening heading — consistent with how every other +content type in this schema reaches the page only by way of a `Section` once any +heading governs it. Adding `Reference` here as well would imply a second, headingless +containment path that no empirical observation supports. + +**Why a discriminated union for `children` rather than `list[Any]` or one generic +`Block` type.** The Raw Marker Model deliberately uses one uniform envelope because +it must stay agnostic to block semantics. The Document Object's job is the opposite: +it exists specifically to make structural type distinctions (a `Table` is not +interchangeable with a `Paragraph` downstream). A discriminated union gives +consumers static type safety and keeps `Page`/`Section` children lists +self-describing in serialized JSON via the discriminator field, with no loss of +ordering since list order is itself the reading-order signal (Section 3.4). + +--- + +## 9. Section + +**Purpose.** A heading-governed grouping of content, derived from Marker's +`section_hierarchy` breadcrumbs (Section 3.5) rather than re-derived from text +pattern-matching on headings. + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `id` | str | yes | Architectural requirement | `canonical_path` built from the governing `SectionHeader` Marker block's own path id. | +| `heading_text` | str | yes | Marker-observed | Verbatim text of the governing `SectionHeader` block. | +| `provenance` | `StructuralProvenance` | yes | Marker-observed | `marker_block_ids = [the SectionHeader block's id]`. | +| `depth` | int | yes | Marker-observed (derived) | Position of this section's heading in the ordered `section_path` list (Section 3.5), zero-indexed from the outermost heading on the page/document. | +| `children` | list of (`Section` \| `Paragraph` \| `Table` \| `Figure` \| `Equation` \| `Footnote` \| `Reference`) | yes (may be empty) | Marker-observed | Nested sub-sections and content governed by this heading, in reading order. A `Section` may contain further `Section` objects, giving the hierarchy genuine nesting rather than a flat list with a depth integer alone. `Reference` was added in Version 1.1 (see Changelog) — `ListItem` blocks under a "References" heading appear here as `Reference` children, exactly as any other content type appears under its governing `Section`. | + +**Invariant.** Every leaf or container object elsewhere in the schema that carries +a non-empty `section_path` in its `StructuralProvenance` must have a corresponding +ancestor chain of `Section` objects matching that path exactly — this is guaranteed +by construction (both are derived from the same `section_hierarchy` source, per +Section 3.5) rather than checked as a runtime validator, but it is stated here as a +hard design invariant the Normalizer must not violate. + +**Why `SectionHeader` blocks that are really table/figure labels (e.g. a Marker +`SectionHeader` containing only `"Table 3"`, per Pattern B) do not become `Section` +objects.** Empirical finding 3.1 showed Marker uses the same `SectionHeader` +block type both for genuine paper sections (Methods, Results) and for bare-table +caption labels. The Normalizer must distinguish these by context — a +`SectionHeader` immediately followed by a `Text` block and then a `Table`, with no +intervening structural content, is a caption label being consumed into that +`Table`'s `Caption` (Section 12), not a new `Section`. This rule is recorded here so +the schema's `Section` object is understood to represent only genuine paper +sections; the disambiguation logic itself is Normalizer business logic (out of +scope for this document) but the *consequence* — that some `SectionHeader` Marker +blocks become part of a `Caption` rather than a `Section` — is a structural decision +the schema must support, and it does: `Caption.provenance.marker_block_ids` can +include a `SectionHeader` id (Section 12). + +--- + +## 10. Paragraph + +**Purpose.** A single block of body text — the most common leaf content type. + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `id` | str | yes | Architectural requirement | Per Section 2. | +| `text` | str | yes | Marker-observed | The block's inline HTML content from Marker, **as-is** (e.g. ``, `` tags preserved). | +| `provenance` | `StructuralProvenance` | yes | Marker-observed | `marker_block_ids = [the originating Text/ListItem block's id]`. | + +**Why `text` keeps inline HTML rather than being plain-text-stripped.** Empirical +finding (leaf block dump) confirmed Marker leaf blocks carry real semantic inline +markup (``, ``) directly in their content, not a side annotation. Stripping +it at the Document layer would be a one-way, lossy transformation performed before +any consumer has had a chance to decide whether that markup matters (e.g. a `` +emphasis inside a Methods paragraph could matter to the extraction layer's reasoning +about emphasis on a key term). Per invariant 1.4 (maximum available provenance) and +the general "never lose information without a consumer-side decision to do so" +principle, the Document layer preserves it verbatim; any stripping is a retrieval- +or extraction-layer concern, explicitly out of scope here. + +**Note on `ListItem`/`ListGroup`.** Marker's bibliography uses `ListGroup` containers +of `ListItem` leaves rather than `Text` blocks (this was the basis for separating +references structurally without text pattern-matching). A `ListItem` that is part of +a reference list is **not** modeled as a `Paragraph` — it becomes a `Reference` +(Section 17). A `Paragraph` is reserved for body-text `Text`/generic `ListItem` +content; the Normalizer disambiguates by parent context (a `ListGroup` under the +References section vs. elsewhere), again business logic out of scope here, but the +schema accommodates the distinct outcome via two separate object types. + +--- + +## 11. Caption (supporting type, embedded in Table and Figure) + +**Purpose.** A normalized representation of a table or figure's caption, +collapsing Marker's two empirically observed patterns into one shape. + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `label` | Optional[str] | no | Marker-observed | E.g. `"Table 3"` or `"Figure 1"`. Present whenever a `Caption` block (Pattern A) or a `SectionHeader` label block (Pattern B) was found. | +| `text` | Optional[str] | no | Marker-observed | The descriptive caption sentence. From the `Caption` block's content (Pattern A) or the `Text` block immediately following the label (Pattern B). | +| `trailing_notes` | Optional[str] | no | Marker-observed | The trailing "Note: ..." `Text` block sometimes observed immediately after a `Table`, distinct from both `label`/`text` and from `Footnote` objects (Section 16). Kept as its own field because it was empirically observed to be part of the caption apparatus, not body text, but also not a true Marker `Footnote` block. | +| `provenance` | `StructuralProvenance` | yes | Marker-observed | `marker_block_ids` lists every contributing Marker block (one for Pattern A's single `Caption` block; two or three for Pattern B's `SectionHeader` + `Text` + optional trailing `Text`). Uses `contributing_bboxes` (Section 3.3) rather than a single `bbox` whenever more than one block contributes, since collapsing non-adjacent regions into one bbox would misrepresent the geometry. | + +**Why one normalized shape rather than preserving Marker's two patterns +separately in the schema.** This is the central case the project's "empirically +driven, not speculative" instruction is built around: both patterns were directly +observed (Pattern A on pages 6/10/12, Pattern B on page 7, per finding 3.1), so +normalizing them is not a hypothetical convenience — it is required because every +downstream consumer (extraction layer asking "what is this table about," review UI +displaying "the caption") needs one consistent shape regardless of which pattern the +source PDF happened to produce. Modeling them as two different optional sub-objects +instead would push that disambiguation work onto every consumer rather than once, +inside the Normalizer, where the empirical knowledge of the two patterns actually +lives. + +--- + +## 12. Table + +**Purpose.** A table's logical structure and its evidence-level cell geometry, +kept as two deliberately parallel representations per the empirical recommendation. + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `id` | str | yes | Architectural requirement | Per Section 2. | +| `provenance` | `StructuralProvenance` | yes | Marker-observed | `marker_block_ids = [the Table block's id]` (and the `TableGroup` id too, if Pattern A). | +| `caption` | Optional[`Caption`] | no | Marker-observed | Section 11. `None` only if no caption-bearing blocks were found adjacent to the table at all (not empirically observed in the representative paper, but not excluded as a possibility — captionless tables are not assumed impossible). | +| `raw_html` | str | yes | Marker-observed | The Table block's own `html` field, verbatim — the complete, correctly-nested `
    ...
    ` Marker produces. Treated as the **source of truth for logical structure** (rows, columns, header rows), per the empirical recommendation, precisely because reconstructing structure independently from cell geometry risks disagreeing with Marker's own (already correct) parse. | +| `rows` | list of `TableRow` | yes (may be empty) | Marker-observed (derived) | A structured parse of `raw_html`'s `` elements into row objects (Section 12.1), giving consumers row/column access without re-parsing HTML themselves. Derived from `raw_html`, not an independent reconstruction. | +| `cells` | list of `TableCell` | yes (may be empty) | Marker-observed | The flat list of Marker `TableCell` child blocks, retained **only** as evidence/geometry data (bbox, polygon, provenance) — explicitly not used to derive row/column structure, per the empirical recommendation that `raw_html` is structural truth and `TableCell` geometry is supplementary. | +| `footnote_ids` | list of str | yes (may be empty) | Architectural requirement (deferred population) | Ids of `Footnote` objects geometrically attached to this table (Section 16). Empty until the Normalizer's bbox-proximity heuristic (finding 3.2) runs; the field exists now so that heuristic's output has a defined home without later schema change. | + +### 12.1 TableRow (supporting type, embedded in `Table.rows`) + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `cells` | list of `TableRowCell` | yes | Marker-observed (derived) | Ordered left to right per the source ``. | + +### 12.2 TableRowCell (supporting type, embedded in `TableRow.cells`) + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `text` | str | yes | Marker-observed | Cell text content from the parsed ``/`` element, with any `` wrapper tag stripped and its content treated as equivalent plain text (per empirical finding 3.5 — Marker inconsistently wraps numerically identical `mean ± stderr` values in `` depending on OCR path; the schema does not preserve this distinction since it carries no structural meaning, only an OCR-routing artifact). | +| `is_header` | bool | yes | Marker-observed | True if the source element was ``, false for ``. | +| `structural_notes` | Optional[str] | no | Architectural requirement (deferred) | A free-text slot reserved for a Normalizer-attached structural annotation — most notably, a suspected merged-cell placeholder (empirical finding 3.4: Marker silently flattens merged header cells into duplicated rows with an empty filler cell, with no flag distinguishing this from a genuinely empty cell). The heuristic for populating this field is explicitly **not** decided in this specification — finding 3.4 was flagged as needing more representative papers before a reconstruction rule is chosen. The field is included as an open slot precisely so that decision can be made later without a schema change, consistent with the brief's instruction to accommodate known structural cases without redesign. | + +**Why `TableRowCell` does not have `row_index`/`col_index` integers.** These are +implicit in `Table.rows`' list-of-lists structure itself (a cell's row is its +containing `TableRow`'s position in `rows`; its column is its own position in +`cells`), so adding redundant integer fields would duplicate information already +present in list order, with no Marker-observed justification for storing it twice. + +**Why `TableRowCell` has no `rowspan`/`colspan` field.** Empirical finding 3.4 +confirmed Marker's HTML output never emits `rowspan`/`colspan` attributes even where +the source PDF visually has merged cells — it flattens instead. Adding a +`rowspan`/`colspan` field for a case never observed in Marker's actual output would +violate the "no speculative fields" instruction. If a future representative paper +demonstrates Marker does emit span attributes under some condition, this is the +single place such fields would be added. + +### 12.3 TableCell (supporting type, embedded in `Table.cells`; evidence-only) + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `id` | str | yes | Architectural requirement | Per Section 2; path derived from the Marker `TableCell` block's own id. | +| `text` | str | yes | Marker-observed | Verbatim cell content (not math-stripped here — this object is evidence/geometry, not the logical text consumers should read; `TableRowCell.text` is the cleaned version). | +| `bbox` | `BoundingBox` | yes | Marker-observed | Per-cell geometry, the entire reason this parallel representation is retained (evidence highlighting in the review UI). | +| `polygon` | `Polygon` | yes | Marker-observed | Mirrors `bbox`. | + +**Why this evidence-only `TableCell` and the logical `TableRowCell` are not unified +into one type.** Empirical finding 3.3 established these are genuinely two +different, only partially-corresponding representations Marker provides in +parallel — one (the `` HTML) has correct logical structure but no per-cell +geometry, the other (`TableCell` children) has per-cell geometry but no row/column +index. Forcing them into a single type would require either fabricating row/column +indices on the geometry side (an unverified bbox-clustering reconstruction the +findings explicitly flagged as risky) or discarding per-cell geometry on the logical +side (losing the evidence-highlighting capability entirely). Keeping them separate, +each true to what Marker actually provides, is the choice that adds no +unverified inference. **Open implementation note, not a schema decision:** +positionally correlating a given `TableRowCell` with its corresponding `TableCell` +(for evidence highlighting of a specific logical cell) is left to the Normalizer to +attempt via parse-order correspondence; this spec does not assert that +correspondence is guaranteed, since it was not empirically verified. + +--- + +## 13. Figure + +**Purpose.** A figure region, with its caption normalized the same way as `Table`. + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `id` | str | yes | Architectural requirement | Per Section 2. | +| `provenance` | `StructuralProvenance` | yes | Marker-observed | `marker_block_ids = [the Figure block's id]` (and `FigureGroup` id, if present). | +| `caption` | Optional[`Caption`] | no | Marker-observed | Section 11. Empirically, `FigureGroup` always pairs `[Figure, Caption]` in that order (mirroring `TableGroup`'s pairing, just with reversed order — confirmed, not assumed, per the findings doc). | +| `image_data` | Optional[bytes] | no | Marker-observed | Base64-decoded raster image content from Marker's `images` field, when present. Empirically, in the representative paper, only `Picture` blocks (journal logo, cover thumbnail) carried non-empty `images`; the one `Figure` block had `images: {}`. This field is therefore included (figures plausibly can carry raster data, and the project must not assume they never will) but its emptiness in the current evidence base is recorded explicitly in Section 22 as unconfirmed, not silently assumed resolved. | + +--- + +## 14. Equation + +**Purpose.** A mathematical expression block. + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `id` | str | yes | Architectural requirement | Per Section 2. | +| `provenance` | `StructuralProvenance` | yes | Marker-observed | `marker_block_ids = [the Equation block's id]`. | +| `raw_math` | str | yes | Marker-observed | The verbatim MathML-ish `` content, including any equation number embedded inline (e.g. `"DP = I - IR + P - ETc \pm VR, \qquad (1)"`), per empirical finding 3.7. | +| `equation_number` | Optional[str] | no | Architectural requirement (deferred) | A slot for the parsed-out equation number (e.g. `"1"`), since finding 3.7 confirmed Marker provides no separate field for it — any cross-reference resolution ("using equation (1)" in body text) requires parsing it out of `raw_math`. The parsing logic itself is out of scope for this spec; the field exists so its result has a defined home. | + +--- + +## 15. Footnote + +**Purpose.** A footnote block, with its attachment to a table or figure resolved +geometrically rather than structurally, per empirical finding 3.2. + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `id` | str | yes | Architectural requirement | Per Section 2. | +| `provenance` | `StructuralProvenance` | yes | Marker-observed | `marker_block_ids = [the Footnote block's id]`. Note: this block's own provenance never implies attachment — footnotes are flat page-level siblings, not children of any Table/Figure (finding 3.2), so attachment is recorded separately below. | +| `raw_text` | str | yes | Marker-observed | Verbatim footnote content. | +| `attached_object_id` | Optional[str] | no | Architectural requirement (deferred) | The id of the `Table` or `Figure` this footnote was determined to belong to, via the Normalizer's bbox-proximity heuristic ("nearest preceding Table/Figure on the same page by bbox y-position," per finding 3.2). `None` when unresolved — tracked in aggregate by `Statistics.unresolved_footnote_count` (Section 7). The heuristic itself is Normalizer logic, out of scope here; the field exists so its output, including the legitimate possibility of non-resolution, has a defined, queryable home. | + +**Why attachment is nullable rather than required.** Forcing every footnote to +resolve to a table/figure would hide genuine ambiguity (e.g. a footnote whose +geometric position is equidistant between two candidates, or a page-level +disclaimer footnote unrelated to any table) behind an incorrect best-guess. Per the +project's broader principle (already established for the IR: "fields that cannot be +resolved are marked with an unresolved status rather than silently filled"), the +same discipline applies at the structural layer: `None` is a legitimate, recorded +outcome, not an implementation gap to paper over. + +--- + +## 16. Reference (Bibliography Entry) + +**Purpose.** One bibliography entry, structurally distinguished from body text +because Marker represents references via `ListGroup`/`ListItem`, not `Text` blocks +(observed directly in the page_stats/structure walkthrough, not inferred from +content). + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `kind` | `Literal[NodeKind.REFERENCE]` | yes | Architectural requirement | Added in Version 1.1 (see Changelog). Discriminator for the `Section.children` union, following the same pattern as every other discriminated-union member. | +| `id` | str | yes | Architectural requirement | Per Section 2. | +| `provenance` | `StructuralProvenance` | yes | Marker-observed | `marker_block_ids = [the ListItem block's id]`. | +| `raw_text` | str | yes | Marker-observed | Verbatim reference entry text, including any inline markup Marker preserved. | + +**Boundary note.** Like `Metadata` (Section 5), `Reference` deliberately stops at +verbatim text. Parsing a reference string into author/year/journal/DOI fields is +citation-matching — a semantic operation belonging to the IR's `Citation` entity, +not this layer. This object's only job is to say "this `ListItem`, structurally, +is a bibliography entry, not body text," which is information Marker's block typing +already gives for free via the `ListGroup` container. + +--- + +## 17. PageHeader / PageFooter + +**Purpose.** Repeated journal running-header/footer content (e.g. the journal name +repeated on every page, or page-footer branding), retained for completeness and +front-matter heuristics (Section 8) but not expected to be consumed by extraction. + +| Field | Type | Required | Origin | Notes | +|---|---|---|---|---| +| `id` | str | yes | Architectural requirement | Per Section 2. | +| `provenance` | `StructuralProvenance` | yes | Marker-observed | `marker_block_ids = [the PageHeader/PageFooter block's id]`. | +| `raw_text` | str | yes | Marker-observed | Verbatim content. | + +Modeled as two distinct types (`PageHeader`, `PageFooter`) rather than one generic +"running content" type, simply mirroring Marker's own distinct block types +one-to-one — there is no structural reason to merge them, and merging would lose +the type distinction Marker itself already makes. + +--- + +## 18. Cross-Object Relationships & Invariants Summary + +This section consolidates relationship rules stated piecemeal above, for a single +point of reference. + +- **Containment is exclusively via `children` lists and explicit id-reference + fields (`footnote_ids`, `attached_object_id`) — never via implicit ordering + conventions or id-string parsing.** Any consumer needing "what footnotes belong + to this table" reads `Table.footnote_ids`, never re-derives it from geometry + itself; the Normalizer computes that relationship exactly once. +- **Reading order is a single global property (Section 3.4), independent of any + per-object containment.** Two sibling objects under different `Section`s can be + compared for relative reading order via their `reading_order_index` without + needing to know anything about section nesting. +- **Every non-`Document` object carries exactly one `StructuralProvenance`,** which + is the only place Marker block ids appear outside of `ProcessingMetadata`'s + artifact reference. No object duplicates Marker ids elsewhere in its own fields. +- **No object type defined in this specification has a field referencing an IR, + retrieval, validation, or export concept**, satisfying invariant 1.6 by + construction — this is checked by inspection of this document, not by a runtime + rule, since it is a closed schema with a fixed object list. + +--- + +## 19. Serialization Requirements + +- All models use `model_config = ConfigDict(frozen=True, extra="forbid")`. Unlike + the Raw Marker Model (which intentionally used `extra="allow"` for forward + compatibility with unknown future Marker fields), the Document Object is the project's own + designed contract — an unexpected extra field here indicates a Normalizer bug, + not a benign future Marker addition, so it should fail loudly (`extra="forbid"`) + rather than silently passing through. +- `model_dump_json()` must be deterministic for a given object graph: field order + follows declaration order (Pydantic v2 default), list order follows the + semantically meaningful order already specified per field (reading order for + children, left-to-right for table cells, outermost-to-innermost for + `section_path`) — never a non-deterministic order like dict-hash order. + `datetime` fields serialize as ISO 8601 strings in UTC. +- Every model must round-trip losslessly through `model_dump()` → + `model_validate()` and `model_dump_json()` → `model_validate_json()`, mirroring + the test discipline already established and passing for the Raw Marker Model. +- Bytes fields (`Figure.image_data`) serialize as base64 strings in JSON, matching + Marker's own convention for `images`, so no separate encoding scheme is + introduced at this layer. + +--- + +## 20. Validation Rules + +These are construction-time invariants enforced by each model's own validators, +distinct from the cross-cutting invariants in Section 1 (which are policies the +Normalizer must follow, not all individually mechanically checkable). + +- `BoundingBox`: `x1 >= x0` and `y1 >= y0`. +- `StructuralProvenance`: `marker_block_ids` has at least one element; + `reading_order_index >= 0`; exactly one of `bbox` or `contributing_bboxes` (or + neither, for objects with genuinely no recoverable geometry) is populated — never + both, to avoid two disagreeing geometric claims about the same object. +- `Document`: `pages` non-empty; `page_number` values across `pages` are unique. +- `Page`: `page_number >= 0`. +- `Table`: if `rows` is non-empty, every `TableRow.cells` list has at least one + element (a row with zero cells is not a meaningful row — such input indicates a + parse error in `raw_html`, which should surface as a Normalizer-time error, not a + silently-accepted empty row in the Document Object). +- `Footnote`: no validation forces `attached_object_id` to be set — its absence is + valid by design (Section 15). +- `Statistics`: every count field is `>= 0`; `unresolved_footnote_count <= + footnote_count` (a basic sanity bound the model itself can check independent of + whatever produced the numbers). + +--- + +## 21. Explicitly Deferred — Not Modeled, By Design + +Per the instruction to avoid speculative fields, the following structural cases +identified during evaluation are **intentionally absent** from this schema rather +than represented with a guessed-at field shape, because the representative paper +set does not yet provide enough evidence to know what shape is correct: + +- **Multi-page table continuation.** Not observed in the representative paper (no + table spans a page break). No `continues_on_page` / `continuation_of_table_id` + field is added speculatively. When a representative paper exhibiting this is + evaluated, this section is where such a field would be added — as an addition, + not a redesign, since `Table` already has a stable `id` to reference. +- **Multi-panel figure decomposition.** The representative paper's Figure 3 has 10 + visually lettered sub-panels under one shared caption, but Marker recorded it as + a single flat `Figure` block with no internal panel structure. Since this is the + only data point (n=1) and it shows Marker *not* decomposing panels, no `panels: + list[FigurePanel]` field is added on the strength of a PDF-visual observation that + contradicts what Marker itself outputs. If a future paper shows Marker does + sometimes decompose panels, this is where that field would be introduced. +- **TableGroup-vs-bare-Table triggering condition.** Both patterns are modeled + (via `Caption`'s flexible provenance, Section 11), but *why* Marker chooses one + over the other (single table per region vs. dense multi-table page, per the one + data point available) is not encoded as a schema concept — it doesn't need to be, + since the Document Object normalizes both outcomes into the same `Caption` shape + regardless of cause. +- **Merged-cell reconstruction heuristic.** The *slot* (`TableRowCell.structural_ + notes`) exists (Section 12.2), but the specific rule for populating it (e.g. + "empty cell directly below a filled cell in the same column ⇒ merged-placeholder + suspected") is explicitly not decided here, per finding 3.4's own conclusion that + this needs more representative papers first. + +--- + +## 22. Summary — Field Origin Distribution + +A consolidated view of the distinction requested for this specification: how many +fields per object are direct Marker carry-overs versus existing purely to satisfy +an architectural requirement (provenance, determinism, reproducibility, +serialization) versus reserved as a deferred-population slot for a Normalizer +heuristic not yet designed. + +| Object | Marker-observed fields | Architectural-requirement fields | Deferred-population slots | +|---|---|---|---| +| Document | `pages` | `id`, `source_pdf_identifier`, `metadata`, `processing_metadata`, `statistics` | — | +| Metadata | `title`, `page_count`, `has_front_matter_page` | — | — | +| ProcessingMetadata | `marker_version` | `normalizer_version`, `processed_at`, `source_marker_artifact_ref` | — | +| Statistics | — | all count fields | — | +| Page | `page_number`, `children`, `is_front_matter` (heuristic) | `id`, `provenance` | — | +| Section | `heading_text`, `depth`, `children` | `id`, `provenance` | — | +| Paragraph | `text` | `id`, `provenance` | — | +| Caption | `label`, `text`, `trailing_notes` | `provenance` | — | +| Table | `raw_html`, `rows`, `cells`, `caption` | `id`, `provenance` | `footnote_ids` | +| TableRowCell | `text`, `is_header` | — | `structural_notes` | +| TableCell | `text`, `bbox`, `polygon` | `id` | — | +| Figure | `caption`, `image_data` | `id`, `provenance` | — | +| Equation | `raw_math` | `id`, `provenance` | `equation_number` | +| Footnote | `raw_text` | `id`, `provenance` | `attached_object_id` | +| Reference | `raw_text` | `id`, `provenance` | — | +| PageHeader/PageFooter | `raw_text` | `id`, `provenance` | — | + +--- + +## 23. Exit Criteria for This Specification + +This specification is ready to be frozen and handed to implementation once: + +1. Every object above has a 1:1 or many:1 mapping back to either an observed + Marker block type or a named architectural requirement (satisfied throughout + this document via the "Origin" column on every field table). +2. No field exists whose justification is "might be useful later" rather than + "Marker provides this" or "the architecture requires this for provenance / + determinism / reproducibility / serialization / validation" (satisfied; the one + category that looks speculative — deferred-population slots — is explicitly + justified by the stated requirement to avoid future schema redesign, and is + listed exhaustively in Section 22's third column plus Section 21's explicit + exclusions). +3. No object or field encodes scientific meaning (satisfied — verified against + invariant 1.2 by inspection of the full object list in Sections 4–17). +4. Implementing this specification in Pydantic v2 requires translation, not new + design decisions — the identifier rule (Section 2), provenance rule (Section + 3.3), reading-order rule (Section 3.4), and every per-object field table are + concrete enough to type directly. + +Once reviewed and approved, the next phase is the mechanical translation of this +document into immutable Pydantic v2 models, followed by the Normalizer that +populates them from the Raw Marker Model. diff --git a/docs/jetstream_environment.md b/docs/jetstream_environment.md new file mode 100644 index 0000000..e5a21d0 --- /dev/null +++ b/docs/jetstream_environment.md @@ -0,0 +1,20 @@ +Hostname: bety-db-llm-gpu + +CPU: +16 AMD EPYC cores + +RAM: +58 GB + +GPU: +A100 20GB + +CUDA: +12.2 + +Storage: +484 GB local +9.8 TB shared mount (/software) + +Python: +3.12.3 \ No newline at end of file diff --git a/docs/marker_empirical_findings_paper1.md b/docs/marker_empirical_findings_paper1.md new file mode 100644 index 0000000..b2ab3ca --- /dev/null +++ b/docs/marker_empirical_findings_paper1.md @@ -0,0 +1,223 @@ +# Marker Output — Empirical Findings (Paper 1: Nutrient Cycling, Smukler et al. 2012) + +Source: `/mnt/user-data/uploads/1781681908897_Nutrient-cycling.json` +17 pages. Tree rooted at a single `Document` node. + +## 1. Universal block envelope + +Every node in the tree, container or leaf, shares the identical field set: + +``` +id : str e.g. "/page/7/Table/2" (path-like, encodes page + type + local index) +block_type : str e.g. "Table", "Text", "Page", "Document" +html : str either real inline HTML content (leaf), or a manifest of + pointers to children (container) +polygon : list 4 [x,y] corner points +bbox : list [x0, y0, x1, y1] +children : list | None nested block objects, or None for true leaves +section_hierarchy : dict {depth_str: section_header_id} — governing heading path +images : dict | None base64 image data keyed by this block's own id + (populated only for Picture blocks in this paper; {} otherwise) +``` + +No block type adds extra fields beyond this envelope. This means the schema's base +`MarkerBlock` type can be a single shape; specialization happens through `block_type` +plus type-specific *interpretation* of `html`/`children`, not through extra fields. + +## 2. Global block-type census (this paper) + +| block_type | count | notes | +|---------------|-------|-------| +| TableCell | 781 | always leaf, always child of a Table | +| Text | 105 | leaf; generic paragraph/caption-fragment/note | +| ListItem | 81 | leaf; reference entries | +| PageFooter | 49 | leaf; repeated journal footer per page | +| Page | 17 | container; one per PDF page | +| SectionHeader | 13 | leaf; includes real section headers AND table/figure labels ("Table 3") | +| Caption | 7 | leaf; only appears inside TableGroup/FigureGroup wrapping | +| Table | 7 | container of TableCells; html also contains a parallel full `
    ` HTML repr | +| Figure | 6 | leaf (no children); images field empty in this paper (no embedded raster found) | +| ListGroup | 6 | container of ListItems (reference list chunks) | +| Footnote | 5 | leaf; NOT nested under their related Table — flat page-level siblings | +| TableGroup | 3 | container: always exactly [Caption, Table] in that order | +| Picture | 2 | leaf; carries actual base64 raster in `images` (journal logo/cover thumbnail) | +| Document | 1 | root | +| PageHeader | 1 | leaf | +| FigureGroup | 1 | container: always exactly [Figure, Caption] in that order | +| Equation | 1 | leaf; html contains MathML-ish `` with the equation number INLINE in the math string | + +Note: `Span` and `Line`, which appeared in the `page_stats` summary block_counts, +do NOT appear anywhere in the actual tree. They are lower-level OCR primitives that +Marker collapses into the parent block's `html` string and does not expose as tree +nodes. The schema should not plan to consume Span/Line directly. + +## 3. Confirmed structural patterns + +### 3.1 Caption pairing is inconsistent across two different mechanisms + +**Pattern A — wrapped (TableGroup / FigureGroup):** +`TableGroup` and `FigureGroup` are containers whose children are ALWAYS exactly +`[Caption, Table]` or `[Figure, Caption]` respectively (order differs!: caption comes +*before* Table in TableGroup, *after* Figure in FigureGroup). Caption is a single +block of html content. + +**Pattern B — unwrapped (bare Table directly under Page):** +No TableGroup wrapper exists. Instead, caption information is split across TWO +separate sibling blocks immediately preceding the Table: + - a `SectionHeader` containing only the label, e.g. `

    Table 3

    ` + - a `Text` block containing the full descriptive caption sentence +A trailing `Text` block ("Note: ...") may also follow the table — this is part of +the caption/footnote apparatus, not body text. + +Confirmed on page 7 (Tables 3 and 4, both unwrapped) vs. pages 6/10/12 (wrapped). +**The Document Schema must normalize both patterns into one canonical Table.caption +field** — the Normalizer needs a rule: "if TableGroup, caption = the Caption child's +text; if bare Table, caption = nearest-preceding SectionHeader text + nearest text +block before the Table, concatenated." + +### 3.2 Footnote-to-Table attachment requires geometric inference + +Footnotes are NEVER nested inside their related Table, and are NOT reliably +ordered/numbered adjacent to it either. On page 7: Table 3 (id .../Table/2), +Table 4 (id .../Table/8), and three Footnotes (ids .../Footnote/4, /5, /10) are +all flat siblings of the Page. + +The only reliable signal for attachment is **bbox y-coordinate ordering**: +Footnote/4 (y: 271–280) and Footnote/5 (y: 283–292) fall between Table 3's +"Note" text (y: 259–268) and Table 4's header (y: 342) → belong to Table 3. +Footnote/10 (y: 663–674) falls after Table 4's Note (y: 652–661) → belongs to Table 4. + +**Required Normalizer rule:** assign each Footnote to the nearest preceding +Table/Figure on the same page by bbox y-position, not by id adjacency or tree +nesting (id adjacency is NOT reliable — see Footnote ids 4,5 vs Table id 2, +and Footnote id 10 vs Table id 8; the numbering interleaves with other blocks). + +### 3.3 Tables carry two parallel, partially redundant representations + +A `Table` block's own `html` field contains a COMPLETE, correctly nested +`
    ...
    ...
    ...
    ` structure +with correct logical row/column grouping. + +Separately, the same Table also has N `TableCell` children, each with its own +bbox/polygon, but **no explicit row index or column index field** — row/column +membership is implicit and would need to be reconstructed by clustering bbox +y-ranges (rows) and x-ranges (columns) if cell-level geometry is needed. + +**Schema decision needed:** does the Document Object's canonical Table representation +parse structure from the `html` (reliable logical structure, no per-cell geometry), +from the `TableCell` children (per-cell geometry, structure must be inferred), or +both (html for logical truth, TableCell bboxes for evidence-highlighting only)? +Recommendation to evaluate against more papers: treat `html` as the source of +truth for logical table structure (rows/cols/headers), and TableCell geometry as +supplementary evidence-location data only, since reconstructing rows/cols from +bbox clustering independently risks disagreeing with Marker's own html parse. + +### 3.4 Merged/spanning cells are silently flattened, not marked + +Table 3 in the source PDF has merged row labels (e.g. "Irrigated Y1" / "(South Field)" +spans two visual rows as one label). Marker's output html does NOT use `rowspan`; +instead it duplicates the row structure and leaves the second row's corresponding +cell empty (``). There is no flag distinguishing "genuinely empty cell" +from "this is a placeholder for a merged cell above." This is invisible information +loss unless the schema explicitly accounts for it. + +**Open question for the Document Schema:** do we attempt to reconstruct rowspans +heuristically (empty cell directly below a filled cell in the same column = merged), +or do we accept Marker's flattening as-is and rely on the original row-group label +(e.g. "Irrigated Y1") being unambiguous from context alone? Needs testing against +more papers with merged cells before deciding — flagging as deferred per the +agreed scoping (don't over-design from one example). + +### 3.5 Inline math/value formatting is inconsistent within the same table + +Numerically identical value formats (`mean ± stderr`) appear sometimes as plain +text (`7.9 ± 0.1`) and sometimes wrapped in `7.9 \pm 0.1` within the +SAME table, seemingly depending on which OCR path (`pdftext` vs `surya`) produced +that specific cell. This matches the evaluation's noted concern about superscript/ +subscript/symbol OCR artifacts. + +**Required Normalizer rule:** strip/unwrap `` tags during cell text +extraction and treat their content as equivalent plain text — do not let table +schema or downstream parsing branch on whether a cell happens to contain a +`` wrapper. + +### 3.6 Significance markers are embedded in cell text, not structured + +Asterisks and daggers indicating statistical significance (`**`, `*`, `†`) are +appended directly to the numeric text inside table cells (e.g. `"64.5 ± 16.7**"`), +rather than being a separate, structured annotation. Linking a given cell's +significance marker to its meaning requires (a) parsing trailing marker characters +off the cell text, and (b) resolving them against the page's Footnote blocks +(per 3.2) which define what `*`, `**`, `†` mean for that table. + +**Required IR/Evidence implication:** a Measurement object extracted from such +a cell needs a place to carry a `significance_annotation` (raw marker + resolved +meaning), sourced from combining the cell text parse with the resolved footnote. + +### 3.7 Equation numbering is embedded in the math string, not a separate field + +The single Equation block in this paper has html: +`

    DP = I - IR + P - ETc \pm VR, \qquad (1)

    ` + +The `(1)` equation number is part of the math content string itself. There is no +separate `equation_number` field. Any cross-reference resolution (body text says +"using equation (1)") will need to parse the number out of the math string. + +### 3.8 Front-matter / non-scientific content is structurally indistinguishable + +Page 0 (journal cover/routing page — ISSN, "Submit your article," "Article views: 133", +Taylor & Francis branding) uses the exact same block types (Picture, SectionHeader, +Text, Figure, PageFooter) as genuine content pages. Nothing in block_type or +structure flags this page as non-scientific front matter — Marker has no concept +of "this entire page is publisher wrapper, not part of the paper." This must be +detected, if needed, by content heuristics (presence of "ISSN", "Submit your +article", DOI-only content, etc.) or simply accepted into the Document Object +and filtered downstream during scientific extraction (retrieval layer would +simply never retrieve it because it's not relevant to any scientific query). + +### 3.9 `section_hierarchy` gives a live heading path per block + +Every block carries a `section_hierarchy` dict mapping a depth-index string to +the id of the governing SectionHeader at that depth — e.g. a TableCell deep +inside Table 3 carries `{'1': '/page/1/SectionHeader/1', '4': '/page/7/SectionHeader/0'}`, +meaning "under top-level heading from page 1 (likely 'Materials and Methods'), +under nearer heading from page 7 ('Table 3')." This is effectively a precomputed +breadcrumb and is very useful — the Document Object's Section nesting can likely +be derived directly from this rather than re-deriving it from reading order. + +## 4. Implications carried forward to Document Schema Specification + +1. Base `MarkerBlock` envelope is uniform — confirms a single ingestion parser + can handle all block types polymorphically by switching on `block_type`. +2. Table.caption must be normalized across two different Marker patterns (3.1). +3. Footnote attachment must be resolved by geometric proximity, not tree + structure or id adjacency (3.2) — Normalizer needs explicit bbox-based rule. +4. Table logical structure should likely be sourced from `html`, not reconstructed + from TableCell bboxes (3.3) — pending confirmation against more papers. +5. Merged-cell handling is an open/deferred question, not yet resolved (3.4). +6. `` wrapper inconsistency must be normalized away at ingestion (3.5). +7. Significance markers need a dedicated annotation slot in the IR, sourced from + combined cell-text-parsing + footnote-resolution (3.6). +8. Equation cross-references require number extraction from math string (3.7). +9. Front-matter detection is a content-heuristic problem, not structurally free (3.8). +10. `section_hierarchy` likely gives us Section nesting almost for free (3.9) — + worth designing the Normalizer to lean on this rather than re-deriving nesting. + +## 5. Still unverified / needs a second paper to confirm or refute + +- Is TableGroup-wrapping vs bare-Table-with-SectionHeader purely a function of + PDF layout (single table per region vs dense multi-table page), or something + else? (Page 7 has 2 dense tables back-to-back and got the bare pattern; pages + 6/10/12 have one table each and got TableGroup.) Single-paper evidence only. +- Does Figure ever carry actual `images` data, or was the empty `images: {}` we + saw specific to this paper's figures (which are likely vector/map graphics, + not raster photos)? The only non-empty `images` we found were on the two + Picture blocks (journal logo, cover thumbnail), not on any Figure block. +- Multi-page table continuation: NOT observed in this paper — no table spans + a page break here. Still an open edge case requiring a different example paper. +- Multi-panel figures with one shared caption (e.g. Figure 3's panels a–j): the + PDF clearly shows Figure 3 as 10 lettered sub-panels under one caption, but + Marker recorded it as a single `Figure` block (id /page/8/Figure/...) with + no internal panel structure. Need to confirm: does Marker ever decompose + multi-panel figures, or does it always flatten to one Figure block regardless + of internal panel count? This paper suggests always-flatten, but n=1. diff --git a/docs/normalizer.md b/docs/normalizer.md new file mode 100644 index 0000000..84efb10 --- /dev/null +++ b/docs/normalizer.md @@ -0,0 +1,1226 @@ +# Normalizer Design Specification + +**Layer:** Document Understanding Layer (Normalizer) +**Version:** 1.1 +**Status:** Approved — implementation contract +**Upstream contract:** Raw Marker Model (`marker_adapter/raw_model.py`), frozen +**Downstream contract:** Document Schema Specification v1.1, frozen +**Empirical basis:** Marker Output — Empirical Findings (Paper 1: Nutrient Cycling, +Smukler et al. 2012), plus the empirical evaluation referenced across three +representative papers + +This is an engineering specification, not implementation. It defines what the +Normalizer must do, in what order, under what invariants, and where its +responsibility ends — so that implementing it in Python becomes translation, not +design. No code is written here. + +--- + +## Revision Record + +**Version 1.1 (current, approved).** Resolves the following issues identified +during the implementation-readiness audit of v1.0. Each issue is identified by +the audit code; the correction applied is described at its point of change and +summarised here. + +- **CRITICAL-1** (ungoverned content and top-level `Section` interleaving order in + `PageBuilder.children` unspecified): resolved in §5 Stage 7 by stating + explicitly that all items placed into `PageBuilder.children` — ungoverned leaf + builders and top-level `SectionBuilder`s alike — are inserted in the order their + originating block first appears in the page's Stage-2 flat classified sequence, + via a single forward pass. +- **CRITICAL-2** (Stage 7 step 5's `SectionBuilder` nesting algorithm assumed + `SectionHeader` blocks carry their own `section_hierarchy` field, which is not + confirmed): resolved in §5 Stage 7 by replacing the forward-path assumption with + a confirmed reverse-lookup algorithm: a `SectionBuilder`'s depth and parent + section are determined by finding where its heading block's Marker id appears in + the `section_hierarchy` dicts of the content blocks it governs. +- **CRITICAL-3** (`kind` discriminator field required by Schema v1.1's discriminated + union never assigned in any stage): resolved in §5 Stage 3 and Appendix A by + adding a blanket rule that each builder's `kind` field is set at Stage 3 + construction to the fixed string literal required by Schema v1.1 for that object + type. +- **CRITICAL-4** (Stage 2's `CAPTION_LABEL` override condition required lookahead + into not-yet-classified blocks by referencing their `disposition` values, which do + not exist at classification time): resolved in §5 Stage 2 by replacing the + disposition-based lookahead with a `block_type`-based lookahead — the unwrapped + sequence's `block_type` fields are available before classification runs. +- **MAJOR-1** (Stage 0 signal S4 referenced "the section-heading vocabulary defined + in Stage 2," which Stage 2 does not define as a general vocabulary): resolved in + §5 Stage 0 by redefining S4 as a structural check — any block whose `block_type` + is `SectionHeader` present on the page, with no vocabulary list required. +- **MAJOR-2** (builder field lists were illustrative examples for `PageBuilder` only; + other builder types had no complete definition): resolved in §4.1 by adding a + blanket rule covering all builder types uniformly. +- **MAJOR-3** (§24 missing a whole-tree invariant verifying that every Stage-3 + object builder appears exactly once in the final materialized tree): resolved in + §24 by adding invariant 8. +- **MAJOR-4** (Stage 5 gave no rule for the `cells` list of a bare `Table` block + with `wrapper_context = None`): resolved in §5 Stage 5 by stating explicitly that + bare tables have `cells = []`. +- **MAJOR-5** (Stage 6 tie-break unspecified when two candidates share the same + maximum `bbox.y1`): resolved in §5 Stage 6 by specifying page-array index as the + tie-break. +- **MAJOR-6** (Stage 10 step 7 said "`SectionBuilder`s, innermost-first" without + specifying the traversal algorithm for arbitrary nesting depth): resolved in §5 + Stage 10 by naming post-order depth-first traversal explicitly. +- **MAJOR-7** (`ProcessingMetadata` construction absent from Stage 10's numbered + steps): resolved in §5 Stage 10 by adding step 9. +- **MAJOR-8** (`Document` root constructor call never specified in any stage): + resolved in §5 Stage 10 by adding step 10. + +**Version 1.0.** Resolved all issues from the v1.0-draft freeze review (CRITICAL-1 +through CRITICAL-2, MAJOR-1 through MAJOR-10, MINOR-1 through MINOR-8 of that +review). See v1.0 revision record for details. + +--- + +## 1. Overall Responsibility and Scope + +The Normalizer is the single component permitted to transform a `MarkerDocument` +(Raw Marker Model) into a `Document` (Document Object, per Schema v1.1). It is a +pure function in spirit, if not literally in implementation: given the same +`MarkerDocument` and the same `source_pdf_identifier`, it always produces a +byte-identical `Document`. + +Its responsibility is exactly the set of decisions Schema v1.1 explicitly delegates +to "Normalizer business logic" — every place the schema says "this is a Normalizer +concern" is a place this specification must give a concrete, ordered rule. The +Normalizer's job is: + +- Unwrapping Marker's three wrapper block types (`TableGroup`, `FigureGroup`, + `ListGroup`) from the raw children array before any classification occurs. +- Deciding which Marker blocks become which Document Object types (the + `block_type` → disposition mapping). +- Resolving the two confirmed caption patterns (Pattern A: `TableGroup`/`FigureGroup` + wrapper; Pattern B: bare `CAPTION_LABEL` + `Text` + object + optional trailing + `Text`) into one normalized `Caption` per finding 3.1. +- Resolving footnote-to-table/figure attachment via bbox-proximity geometry, + per finding 3.2. +- Distinguishing genuine paper `Section`s from `SectionHeader` blocks that are + actually caption labels, per Schema v1.1 §9. +- Distinguishing body-text `Paragraph`s from bibliography `Reference`s when both + arrive as Marker `ListItem` blocks, per Schema v1.1 §10/§16. +- Computing every deterministic `id` per Schema v1.1 §2. +- Computing global `reading_order_index` values from final tree position, per + Schema v1.1 §3.4. +- Detecting front-matter pages heuristically (`Page.is_front_matter`), per + Schema v1.1 §8. +- Parsing `Table.raw_html` into `TableRow`/`TableRowCell` structure, per Schema + v1.1 §§12.1–12.2. +- Assembling `Statistics`, `Metadata`, and `ProcessingMetadata` as a final derived + pass over the completed tree. + +What the Normalizer never does: it never recognizes a species, treatment, +management event, variable, or any scientific concept; it never maps anything to a +BETYdb field or ontology term; it never parses a citation into author/year/journal; +it never assigns a confidence score to a scientific claim. Every one of those is +explicitly out of scope per Schema v1.1 invariant 1.2. + +--- + +## 2. Inputs and Outputs + +**Inputs (exactly three, all required):** + +1. `marker_document: MarkerDocument` — the parsed Raw Marker Model for one PDF, + already validated and immutable. +2. `source_pdf_identifier: str` — the stable external identifier for the source + PDF (DOI if known, else a content hash of the source PDF bytes), per Schema + v1.1 §2's `document_id` construction. The Normalizer does not compute this + itself — it is supplied by the caller. +3. `processing_context: NormalizerProcessingContext` — a small explicit value + bundle defined as follows: + +```python +@dataclass(frozen=True) +class NormalizerProcessingContext: + marker_version: str # Version string of the Marker run that produced + # the MarkerDocument. Verbatim into + # ProcessingMetadata.marker_version. + normalizer_version: str # Semantic version of this Normalizer code. + # Verbatim into ProcessingMetadata.normalizer_version. + source_marker_artifact_ref: str # Path or content hash identifying the Raw + # Marker Model JSON file this Document Object + # is derived from. Verbatim into + # ProcessingMetadata.source_marker_artifact_ref. + processed_at: datetime # Wall-clock UTC datetime of this materialization. + # Must be timezone-aware UTC. Verbatim into + # ProcessingMetadata.processed_at. +``` + + These map directly onto `ProcessingMetadata` (Schema v1.1 §6) but are supplied + explicitly rather than invented internally, since "what Marker version was run" + and "what artifact file is this" are facts about the calling environment that the + Normalizer cannot derive from the `MarkerDocument` tree alone — the empirical + findings confirmed the Raw Marker root block carries no version metadata field. + +**Output (exactly one):** `document: Document` — a fully constructed, valid Schema +v1.1 `Document` object, or the Normalizer raises (see §22) without returning a +partially-built object. There is no "best effort, partially populated" return mode. + +**Non-goals of the output:** the returned `Document` is not written to disk, +not serialized, not logged. Persistence and serialization are caller responsibilities. + +--- + +## 3. Public API + +```python +def normalize( + marker_document: MarkerDocument, + source_pdf_identifier: str, + processing_context: NormalizerProcessingContext, +) -> Document: + ... +``` + +This is the only public entry point. Everything else (per-object builder functions, +the bbox-proximity matcher, the caption-pattern resolver, the unwrapper) is an +internal implementation detail of the `normalizer` package and must not be imported +or relied upon by any other layer. + +A single narrow exception is permitted for testability: the internal stage +functions may be exposed as a separate, explicitly-not-public module +(e.g. `normalizer._internal`) so that unit tests can exercise individual stages in +isolation without requiring a full `MarkerDocument` fixture for every test. This +does not change the public contract — `normalize()` remains the only function any +other layer may call. + +--- + +## 4. Internal Architecture + +### 4.1 The Builder Pattern + +Schema v1.1 declares all Document Object models as `frozen=True`. A frozen Pydantic +model cannot have any field mutated after construction. The pipeline requires +populating different fields of the same logical object at different stages. +These two facts are reconciled by a single architectural decision: **the Normalizer +never constructs a frozen Schema v1.1 model until all of that model's fields are +finalized.** + +During pipeline execution (Stages 0–9), the Normalizer works exclusively with +**mutable builder objects** — plain Python `dataclass` instances, one per Schema +v1.1 model type — that accumulate field values across stages. At **Stage 10 +(Materialization)**, each builder is converted to its corresponding frozen Schema +v1.1 model exactly once, in a single bottom-up pass. No frozen Schema v1.1 model +exists at any point before Stage 10. + +**Complete builder field rule (resolves MAJOR-2):** Every builder type — without +exception — is defined as a Python `dataclass` containing exactly the fields of its +corresponding Schema v1.1 model, all typed `Optional` and defaulting to `None`, +plus one additional field not present in Schema v1.1: `canonical_path: str | None += None`, which is populated by Stage 9 and consumed by Stage 10 to compute `id`. +No other fields are added to any builder. This rule is stated here once and applies +to every builder type named anywhere in this specification; individual stage +descriptions do not re-state it. + +**`kind` field rule (resolves CRITICAL-3):** Schema v1.1 uses a discriminated +union for `Page.children` and `Section.children`, requiring a `kind` literal field +on every union member. Each builder's `kind` field is set at construction time in +Stage 3 (or Stage 4 for `CaptionBuilder`) to the fixed string literal required by +Schema v1.1 for that object type, and is carried through to Stage 10 unchanged. +`kind` is never left `None` on any builder after Stage 3. The complete mapping is +determined by Schema v1.1's own discriminated union declarations and is not +reproduced here — the mapping is a read-once-from-schema value, not a design +decision. + +The builder types mirror the Schema v1.1 models field-for-field, but with all +fields typed as `Optional` and defaulting to `None`, and with no `frozen=True`: + +```python +# Example — illustrative only; every Schema v1.1 model type has a corresponding +# builder following the complete builder field rule above. +@dataclass +class PageBuilder: + kind: str | None = None # Set at Stage 3; Schema v1.1 literal value + page_number: int | None = None + is_front_matter: bool | None = None + provenance: ProvenanceBuilder | None = None + children: list[Any] = field(default_factory=list) + canonical_path: str | None = None # Added field; Stage 9 only + +@dataclass +class ProvenanceBuilder: + marker_block_ids: list[str] = field(default_factory=list) + page_number: int | None = None + bbox: BoundingBox | None = None + contributing_bboxes: list[BoundingBox] | None = None + polygon: Polygon | None = None + reading_order_index: int | None = None + section_path: list[str] = field(default_factory=list) +``` + +`BoundingBox` and `Polygon` are constructed directly from Marker geometry data at +the stage where that geometry is first processed — they are leaf value types with no +fields that depend on later stages, so constructing them immediately is correct and +safe. All other frozen Schema v1.1 models are deferred to Stage 10. + +Stage 10 is named **Materialization** in this specification to make clear that its +primary role is the builder-to-model conversion pass. Metadata, Statistics, and +ProcessingMetadata assembly remain part of Stage 10 but are secondary to +materialization. + +### 4.2 Wrapper Unwrapping + +Marker's output contains three wrapper block types that bundle related children +under a common parent: `TableGroup` (wraps `[Caption, Table]`), `FigureGroup` +(wraps `[Figure, Caption]` — reversed order relative to `TableGroup`, confirmed +empirically), and `ListGroup` (wraps `[ListItem, ...]`). These wrappers exist in +Marker's tree but have no corresponding Document Object type in Schema v1.1. Stage +2 cannot classify their children as siblings alongside other blocks unless the +wrappers are first removed. + +**Resolution:** A new **Stage 1.5 (Wrapper Unwrapping)** runs immediately after +Stage 1 and before Stage 2. Its sole job is to produce a flat, ordered sequence of +`(MarkerBlock, wrapper_context)` pairs from each page's raw `children` array, where +`wrapper_context` records the parent wrapper block's id and type for any block that +was inside a wrapper, and is `None` for blocks that were direct page children. Stage +2 and all subsequent stages work exclusively against this unwrapped sequence, never +against the original nested tree. + +The complete unwrapping rule is: + +- A `TableGroup` block is replaced in the sequence by its children in original + order, each tagged with + `wrapper_context = WrapperContext(wrapper_type="TableGroup", block_id=)`. +- A `FigureGroup` block is replaced in the sequence by its children in original + order, each tagged with + `wrapper_context = WrapperContext(wrapper_type="FigureGroup", block_id=)`. +- A `ListGroup` block is replaced in the sequence by its children in original + order, each tagged with + `wrapper_context = WrapperContext(wrapper_type="ListGroup", block_id=)`. +- Any other block type is placed into the sequence unchanged with + `wrapper_context = None`. +- Unwrapping is exactly one level deep. If a wrapper contains a nested wrapper + (not observed empirically but structurally possible in Marker), the inner wrapper + is NOT recursively unwrapped — it is placed into the sequence as an ordinary + block, and Stage 2's `UnrecognizedBlockTypeError` path (§22) handles it if it is + not a recognized non-wrapper block type. This is a deliberately conservative + rule: if nested wrappers appear in a future paper, the failure is loud, not + silent. + +After Stage 1.5, every subsequent stage sees only a flat, ordered +`list[UnwrappedBlock]` per page: + +```python +@dataclass(frozen=True) +class WrapperContext: + wrapper_type: str # "TableGroup" | "FigureGroup" | "ListGroup" + block_id: str # The wrapper block's own Marker id + +@dataclass(frozen=True) +class UnwrappedBlock: + block: MarkerBlock + wrapper_context: WrapperContext | None +``` + +Stage 4 (Caption resolution) uses `wrapper_context` to determine which pattern +applies: a `Table` or `Figure` block with `wrapper_context.wrapper_type in +{"TableGroup", "FigureGroup"}` is Pattern A; one with `wrapper_context = None` is +Pattern B. The original wrapper block id is included in provenance (as an additional +entry in `marker_block_ids`) when Pattern A applies, per Schema v1.1 §12/§13. + +### 4.3 Stage Sequence + +``` +MarkerDocument + │ + ▼ +[Stage 0] Front-matter detection (per page, produces is_front_matter flags) + │ + ▼ +[Stage 1] Page builder construction (PageBuilder shells, one per page) + │ + ▼ +[Stage 1.5] Wrapper unwrapping (produces flat UnwrappedBlock sequences per page) + │ + ▼ +[Stage 2] Block classification (UnwrappedBlock → disposition tag per block) + │ + ▼ +[Stage 3] Leaf builder construction (all object builders except id, + reading_order_index, section_path, caption, rows/cells, + footnote linkage — those come from later stages) + │ + ▼ +[Stage 4] Caption resolution (Table/Figure builders gain caption builders) + │ + ▼ +[Stage 5] Table internal structure (raw_html → TableRow/TableRowCell builders; + flat TableCell builders from Marker TableCell children) + │ + ▼ +[Stage 6] Footnote attachment (bbox-proximity → footnote_ids / attached_object_id + on builder objects, both sides set atomically) + │ + ▼ +[Stage 7] Section tree assembly (flat builders + section_hierarchy breadcrumbs + → nested SectionBuilder tree; section_path on each builder populated) + │ + ▼ +[Stage 8] Global reading-order assignment (depth-first traversal of builder tree + → reading_order_index on every ProvenanceBuilder) + │ + ▼ +[Stage 9] Canonical path computation (every builder's canonical_path computed; + used by Stage 10 to compute ids) + │ + ▼ +[Stage 10] Materialization (builders → frozen Schema v1.1 models, bottom-up; + id computed per Schema v1.1 §2 during this pass; + Metadata, Statistics, and ProcessingMetadata assembled; + Document root object constructed) + │ + ▼ +[Stage 11] Whole-tree validation (cross-object invariants, §24) + │ + ▼ +Document +``` + +**Why Stage 9 (canonical path computation) precedes Stage 10 (materialization) +rather than being merged into it.** Stage 10 materializes objects bottom-up, +meaning a leaf object's frozen model is constructed before its parent's. A leaf's +`canonical_path` (and therefore its `id`) depends only on its own position in the +final tree, not on its parent's id — Schema v1.1 §2's hash rule is +`sha256(document_id + "|" + canonical_path)`, and `document_id` is computed once +at the start of Stage 10 from `source_pdf_identifier`. So `id` computation is +mechanical once `canonical_path` is known. Stage 9 computes and stores +`canonical_path` as a field on each builder, so Stage 10 can compute each object's +`id` during its own bottom-up pass without needing to traverse the tree again. + +**Why reading-order (Stage 8) precedes canonical-path (Stage 9).** Some synthesized +objects (e.g. a `TableRow` with no direct 1:1 Marker block) have a `canonical_path` +that incorporates their ordinal position among siblings. `reading_order_index` is +the only ordering that is both deterministic and tree-position-derived for such +objects. Computing reading order before canonical paths ensures synthesized-object +paths are stable. + +The Stage 0–11 sequence is a hard contract. No stage may be reordered without a +reviewed revision of this specification. + +--- + +## 5. Processing Pipeline — Stage-by-Stage Detail + +### Stage 0: Front-matter detection + +**Input:** each page's `MarkerBlock` from `marker_document.children` (the top-level +page blocks). +**Output:** a `dict[int, bool]` mapping page index → `is_front_matter`, consumed by +Stage 1. + +Per Schema v1.1 §8 and finding 3.8, Marker gives no structural signal distinguishing +a publisher wrapper page from a content page. Detection is a content-pattern +heuristic applied to the concatenated plain text of all `Text`-typed blocks on the +page. The following signals are checked: + +| Signal | Type | Pattern | +|--------|------|---------| +| S1 | Fixed string (case-sensitive) | `"Submit your article"` | +| S2 | Regex | `r"\bISSN\s*[\d\-]{4,10}"` | +| S3 | Regex | `r"Article views:\s*\d+"` | +| S4 | Structural | No block with `block_type == "SectionHeader"` is present anywhere in the page's raw `children` array (before unwrapping). | + +**S4 correction (resolves MAJOR-1):** S4 was previously defined as checking +against "the section-heading vocabulary defined in Stage 2," which Stage 2 does not +define as a general vocabulary. S4 is now a purely structural check — the presence +or absence of any `SectionHeader`-typed block on the page, requiring no vocabulary +list. S4 fires (contributes toward the two-signal threshold) when no +`SectionHeader` block exists on the page at all. This check runs against the raw +`children` array, before Stage 1.5 unwrapping, because it is a property of the +page's block-type composition, not of classified dispositions. + +A page is flagged `is_front_matter = True` if and only if **at least two** of S1, +S2, S3, S4 fire. S4 alone is not sufficient. No signal beyond these four is +introduced speculatively — additional signals require a new representative paper +to confirm. + +This stage produces only the boolean flags; it does not alter, skip, or omit +processing of any page's content in any later stage. + +### Stage 1: Page builder construction + +**Input:** `marker_document.children` (the list of top-level page blocks), Stage 0 +flags. +**Output:** a list of `PageBuilder` instances, one per page, with `page_number` and +`is_front_matter` set; `provenance.marker_block_ids = [page_block.id]`; +`provenance.bbox` set from `page_block.bbox` if present; `children` empty. + +Page numbering is taken from the block's position in `marker_document.children` +(0-indexed array position), not parsed from the Marker block's `id` string. + +Note on the Raw Marker Model root: the Marker root node (`MarkerDocument`) was +empirically confirmed to have no `id`, `bbox`, or `polygon` of its own. `Document` +therefore has no `StructuralProvenance` (Schema v1.1 §4). Stage 1 does not attempt +to construct provenance for the `Document` itself. + +### Stage 1.5: Wrapper unwrapping + +**Input:** each page's raw Marker `children` array. +**Output:** per page, a `list[UnwrappedBlock]` as defined in §4.2. + +Unwrapping is exactly one level deep and covers exactly `TableGroup`, `FigureGroup`, +and `ListGroup` block types. All three wrapper types are unwrapped +unconditionally — there is no precondition check. The `wrapper_context` field on +each extracted child records the wrapper's type and block id for use by Stage 4. + +Any block type not listed above that appears in the original children array is +placed into the unwrapped sequence unchanged with `wrapper_context = None`, +regardless of whether it itself has children — Stage 1.5 is a single-level +operation only. + +The original Marker `children` array is preserved unmodified (it is part of the +immutable `MarkerDocument`) — Stage 1.5 produces a separate derived sequence and +does not alter the input. + +### Stage 2: Block classification + +**Input:** each page's `list[UnwrappedBlock]` from Stage 1.5. +**Output:** a `list[ClassifiedBlock]` per page. + +```python +@dataclass(frozen=True) +class ClassifiedBlock: + unwrapped: UnwrappedBlock + disposition: Disposition +``` + +**Complete disposition enum** (all block types confirmed present in the Raw Marker +Model are covered; `UnrecognizedBlockTypeError` fires only for block types not in +this table): + +| Marker `block_type` | Default disposition | Override conditions | +|---------------------|--------------------|--------------------| +| `SectionHeader` | `GENUINE_SECTION_HEADER` | → `CAPTION_LABEL` if both: (a) text matches `^(Table\|Figure)\s+\d+[\.:]?\s*$` (case-insensitive), AND (b) within the next three blocks in the unwrapped sequence, a block whose **`block_type`** is `"Table"` or `"Figure"` exists. | +| `Text` | `BODY_PARAGRAPH` | — | +| `Table` | `TABLE_SHELL` | — | +| `Figure` | `FIGURE_SHELL` | — | +| `Caption` | `CAPTION_TEXT` | Only appears inside a `TableGroup`/`FigureGroup` wrapper | +| `TableCell` | `TABLE_CELL_EVIDENCE` | Only appears as a sibling of `Table` inside a `TableGroup` | +| `Equation` | `EQUATION` | — | +| `Footnote` | `FOOTNOTE` | — | +| `PageHeader` | `PAGE_HEADER` | — | +| `PageFooter` | `PAGE_FOOTER` | — | +| `ListItem` | `BODY_PARAGRAPH` | → `REFERENCE_ENTRY` if governing section heading text matches the references-heading vocabulary (see below) | +| `Picture` | `PICTURE` | Dropped — no builder constructed; log entry produced (§23) | + +**CAPTION_LABEL override lookahead (resolves CRITICAL-4):** The override condition +checks whether "within the next three blocks in the unwrapped sequence, a block +whose `block_type` is `"Table"` or `"Figure"` exists." This uses the raw +`block_type` field of the `UnwrappedBlock.block`, not the block's `disposition` — +`disposition` values for subsequent blocks are not yet assigned when this +`SectionHeader` is being classified. Using `block_type` directly is correct and +unambiguous because Stage 1.5 has already unwrapped all wrapper containers, so any +`Table` or `Figure` block in the flat sequence is visible with its literal +`block_type` value. The three-block window accommodates Pattern B's confirmed +structure (CAPTION_LABEL block, then a Text block, then the Table/Figure block — +two intervening blocks at most, with one block of margin). + +**`ListItem` → `REFERENCE_ENTRY` rule:** a `ListItem`'s governing section is +determined by taking its originating Marker block's `section_hierarchy` field, +sorting the depth-key strings numerically, and resolving the deepest entry to its +corresponding `GENUINE_SECTION_HEADER`-classified block (already determined in this +same Stage 2 pass, processing blocks in array order so that all headings before this +`ListItem` in reading order have already been classified). The references-heading +vocabulary is a fixed list: `{"references", "bibliography", "works cited", +"literature cited"}` (all compared case-insensitively). A `ListItem` whose deepest +governing section heading does not match this vocabulary, or which has no governing +section at all, is classified `BODY_PARAGRAPH`. + +**Adjacency transparency for `PICTURE` blocks:** `PICTURE`-classified blocks are +ignored when evaluating the Pattern B adjacency checks in Stage 4. They are present +in the classified sequence but treated as transparent — their positions do not count +toward "immediately preceding" or "immediately following" relationships. This rule +applies only to Stage 4's adjacency scan; all other stage passes treat +`PICTURE`-classified blocks as ordinary (non-builder-producing) entries. + +### Stage 3: Leaf builder construction + +**Input:** `list[ClassifiedBlock]` per page from Stage 2. +**Output:** per page, a flat `list[ObjectBuilder]` — one builder per classified +block that maps to a Document Object (see exclusions below), with all directly +available fields populated. + +**`kind` field assignment (resolves CRITICAL-3):** Every builder constructed in +Stage 3 has its `kind` field set immediately at construction to the fixed string +literal required by Schema v1.1's discriminated union for that object type. `kind` +is never `None` after Stage 3. The literal value for each type is read from Schema +v1.1's own union declarations and treated as a constant; it is not a computed or +derived value. + +Fields populated at Stage 3 for each builder type: + +| Builder | Fields populated at Stage 3 | Fields deferred | +|---------|----------------------------|-----------------| +| `ParagraphBuilder` | `kind`, `text` (source block `html` verbatim), `provenance.marker_block_ids`, `provenance.bbox`, `provenance.polygon`, `provenance.page_number` | `provenance.reading_order_index` (Stage 8), `provenance.section_path` (Stage 7), `canonical_path` (Stage 9), `id` (Stage 10) | +| `EquationBuilder` | `kind`, `raw_math` (source block `html` verbatim), `equation_number = None`, provenance fields as above | same deferred set | +| `FootnoteBuilder` | `kind`, `raw_text` (source block `html` verbatim), provenance as above, `attached_object_id = None` | `attached_object_id` (Stage 6), deferred set | +| `ReferenceBuilder` | `kind`, `raw_text` (source block `html` verbatim), provenance as above | deferred set | +| `PageHeaderBuilder` | `kind`, `raw_text` (source block `html` verbatim), provenance as above | deferred set | +| `PageFooterBuilder` | `kind`, `raw_text` (source block `html` verbatim), provenance as above | deferred set | +| `TableBuilder` | `kind`, `raw_html` (source block `html` verbatim), provenance as above, `caption = None`, `rows = []`, `cells = []`, `footnote_ids = []` | `caption` (Stage 4), `rows`/`cells` (Stage 5), `footnote_ids` (Stage 6), deferred set | +| `FigureBuilder` | `kind`, `image_data` (from source block `images` if non-empty, else `None`), provenance as above, `caption = None`, `footnote_ids = []` | `caption` (Stage 4), `footnote_ids` (Stage 6), deferred set | + +`CAPTION_TEXT`-classified blocks, `TABLE_CELL_EVIDENCE`-classified blocks, and +`PICTURE`-classified blocks produce no builder at Stage 3. `CAPTION_TEXT` and +`TABLE_CELL_EVIDENCE` are consumed by Stages 4 and 5 respectively by looking them +up in the classified sequence by disposition; `PICTURE` blocks are discarded with a +log entry (§23). + +`GENUINE_SECTION_HEADER`-classified blocks produce no builder at Stage 3 either — +`Section` builders are created by Stage 7, because a `Section`'s final shape +cannot be determined until Stage 7 has resolved the full section nesting across all +pages. + +### Stage 4: Caption resolution + +**Input:** the flat `list[ObjectBuilder]` per page from Stage 3; the +`list[ClassifiedBlock]` from Stage 2 (for adjacent-block lookup); `WrapperContext` +data on each `UnwrappedBlock`. +**Output:** each `TableBuilder` and `FigureBuilder` gains a populated +`caption: CaptionBuilder | None`. + +**Pattern A (wrapped).** A `TableBuilder` or `FigureBuilder` whose originating +block has a non-`None` `wrapper_context` uses Pattern A. The sibling `CAPTION_TEXT` +block in the same wrapper is found by scanning the `ClassifiedBlock` sequence for +the entry with `disposition == CAPTION_TEXT` and the same `wrapper_context.block_id`. +This lookup uses `wrapper_context.block_id` matching, not positional index, so it is +correct for both `TableGroup` (`[Caption, Table]` child order) and `FigureGroup` +(`[Figure, Caption]` child order — reversed relative to `TableGroup`). + +For Pattern A: + +- `CaptionBuilder.kind`: set to the Schema v1.1 literal for `Caption`. +- `CaptionBuilder.label`: extracted via `^(Table|Figure)\s+\d+[\.:]?\s*` regex + match against the `CAPTION_TEXT` block's `html` content; the matched prefix only + (e.g. `"Table 3"`), stripped of trailing punctuation. `None` if no match. +- `CaptionBuilder.text`: the remainder of the `CAPTION_TEXT` block's `html` after + the label prefix (stripped of leading whitespace/punctuation), or the entire + `html` if no label prefix was found. `None` if the `html` is empty after + stripping. +- `CaptionBuilder.trailing_notes = None`. +- `CaptionBuilder.provenance.marker_block_ids`: `[caption_block.id, + wrapper_block.id]`. +- `CaptionBuilder.provenance.bbox = caption_block.bbox` if present. +- `CaptionBuilder.provenance.contributing_bboxes = None`. + +**Pattern B (bare).** A `TableBuilder` or `FigureBuilder` whose originating block +has `wrapper_context = None` uses Pattern B. `PICTURE`-classified blocks are +transparent to all adjacency checks in this pattern (they do not count as +intervening blocks). + +Adjacent blocks are located by scanning the classified sequence: + +- **Label block:** the immediately preceding non-`PICTURE` block must have + `disposition == CAPTION_LABEL`. If not, caption resolution falls through to + `None`. +- **Caption-text block:** the non-`PICTURE` block immediately following the + `CAPTION_LABEL` block (i.e. between the label and the table/figure) must have + `disposition == BODY_PARAGRAPH`. If not, caption resolution falls through to + `None`. +- **Trailing-notes block:** the non-`PICTURE` block immediately following the + `TABLE_SHELL`/`FIGURE_SHELL` block itself, if and only if its `html` content + starts with the literal prefix `"Note:"` (case-sensitive). Any other content in + that position is left as `BODY_PARAGRAPH` content for Stage 7, not consumed as + a trailing note. + +For Pattern B: + +- `CaptionBuilder.kind`: set to the Schema v1.1 literal for `Caption`. +- `CaptionBuilder.label`: the `CAPTION_LABEL` block's `html` content verbatim. +- `CaptionBuilder.text`: the caption-text block's `html` content verbatim. +- `CaptionBuilder.trailing_notes`: the trailing-notes block's `html` if found, + else `None`. +- `CaptionBuilder.provenance.marker_block_ids`: `[label_block.id, + caption_text_block.id]` plus `[trailing_notes_block.id]` if present. +- `CaptionBuilder.provenance.bbox = None` (multiple non-adjacent source blocks). +- `CaptionBuilder.provenance.contributing_bboxes`: list of bboxes of all + contributing blocks, in order. + +**No caption case.** If neither pattern's preconditions are met, `caption = None`. +A log entry is produced (§23). + +### Stage 5: Table internal structure + +**Input:** `TableBuilder` instances from Stage 3 (with `raw_html` already set), +plus the flat classified sequence (for locating `TABLE_CELL_EVIDENCE` blocks +belonging to each table's original wrapper). +**Output:** each `TableBuilder.rows` populated with `TableRowBuilder` instances; +each `TableBuilder.cells` populated with `TableCellBuilder` instances. + +`raw_html` is parsed using a standard HTML table parser (not hand-rolled). For each +`
    `, a `TableRowBuilder` is constructed; for each ``/``, a +`TableRowCellBuilder` is constructed: + +- `TableRowCellBuilder.text`: inner content of the ``/`` element, with any + `...` wrapper tag stripped and its inner content substituted as plain + text — per finding 3.5 and Schema v1.1 §12.2. +- `TableRowCellBuilder.is_header`: `True` for ``, `False` for ``. +- `TableRowCellBuilder.structural_notes = None`. + +**`cells` list for wrapped tables:** The flat `cells` list is populated from +`TABLE_CELL_EVIDENCE`-classified blocks in the same wrapper as this table (located +via `wrapper_context.block_id` matching on the classified sequence — all +`TABLE_CELL_EVIDENCE` blocks sharing this table's `wrapper_context.block_id`). For +each such block, a `TableCellBuilder` is constructed with `text` taken verbatim (not +math-stripped), `bbox` and `polygon` from the source block. + +**`cells` list for bare tables (resolves MAJOR-4):** A `TableBuilder` with +`wrapper_context = None` has `cells = []`. `TABLE_CELL_EVIDENCE` blocks are only +present as children of `TableGroup` wrappers (confirmed empirically); a bare `Table` +block has no corresponding flat cell evidence. `cells = []` is a legitimate final +value for bare tables, not an error or placeholder. + +No positional correspondence between `rows[i].cells[j]` and `cells[k]` is asserted +or relied upon (Schema v1.1 §12.3 explicitly disclaims this correspondence). + +### Stage 6: Footnote attachment + +**Input:** `FootnoteBuilder` instances from Stage 3; `TableBuilder` and +`FigureBuilder` instances from the same page (post Stage 5). +**Output:** `FootnoteBuilder.attached_object_id` set (or left `None`); +corresponding `TableBuilder.footnote_ids` or `FigureBuilder.footnote_ids` updated. + +**Rule:** for each `FootnoteBuilder` on a page, the candidates are all +`TableBuilder` and `FigureBuilder` instances on the **same page** (never +cross-page) whose `provenance.bbox.y1` (bottom edge) is strictly less than the +footnote's `provenance.bbox.y0` (top edge). Among these candidates, the one with +the maximum `provenance.bbox.y1` is selected. **Tie-break (resolves MAJOR-5):** +if two or more candidates share the same maximum `provenance.bbox.y1`, the one +with the smaller page-array index (i.e. appearing earlier in the page's Stage-2 +classified sequence) is selected. This tie-break is deterministic and requires no +additional data beyond what Stage 2 already produces. + +If no candidate exists, `attached_object_id` is left `None`. + +If a footnote's `provenance.bbox` is `None`, `attached_object_id` is left `None` +and a log entry is produced (§23). + +Both sides of the relationship are set atomically: `FootnoteBuilder.attached_object_id` +and the corresponding target's `footnote_ids` list are updated together from the +same single matching result. + +### Stage 7: Section tree assembly + +**Input:** every page's flat `list[ObjectBuilder]` from Stage 3 (updated through +Stages 4–6); the `section_hierarchy` field of each originating Marker block; +the complete set of `GENUINE_SECTION_HEADER`-classified blocks from Stage 2 across +all pages. +**Output:** the final nested `SectionBuilder` tree; `section_path` populated on +every builder's `ProvenanceBuilder`. + +**Multi-page section accumulation.** A single `Section` in a scientific paper +commonly spans multiple pages. The section tree is built once across the entire +document, not independently per page. + +The algorithm proceeds in six steps: + +**Step 1 — Build the global heading registry.** Traverse `marker_document.children` +(all pages) in reading order. For each `GENUINE_SECTION_HEADER`-classified block +encountered, create a `SectionBuilder` initialized with `heading_text` (from the +source block's `html` verbatim), its `provenance` fields (per Stage 3 provenance +rules), an empty `children` list, and record it in a +`dict[marker_block_id → SectionBuilder]`. + +**Step 2 — Resolve each SectionBuilder's depth and parent using reverse lookup +(resolves CRITICAL-2).** `SectionHeader` Marker blocks are not confirmed to carry +their own `section_hierarchy` field; this field is confirmed only on content blocks +(non-heading blocks). Therefore nesting is resolved by reverse lookup: for each +`SectionBuilder`, scan all content blocks across all pages and collect every +`section_hierarchy` dict that references this heading's Marker id. The depth of +this heading is the zero-indexed numeric depth-key position at which its Marker id +appears in those dicts (this value is consistent across all content blocks that +reference it, by Marker's own construction). The parent of a `SectionBuilder` with +depth N is the `SectionBuilder` whose heading Marker id appears at depth-key +position N−1 in the same `section_hierarchy` dicts. A `SectionBuilder` with depth +0 has no parent and is placed directly in a `PageBuilder.children`. A +`SectionBuilder` whose heading Marker id appears in no content block's +`section_hierarchy` at all has no governed content and is omitted from the tree +(Schema v1.1 §9: a `Section` is only materialized if at least one piece of content +is governed by it). + +**Step 3 — Resolve each content builder's governing path.** For each non-Section +object builder, take its originating Marker block's `section_hierarchy`, sort +depth-key strings numerically, and resolve each entry to its `SectionBuilder` from +the global registry. The resulting ordered list of `SectionBuilder` references +(outermost first) is the object's governing path. The last (deepest) entry is the +object's immediate parent section. Store the list of Marker block ids (outermost +first) as `ProvenanceBuilder.section_path`. Objects with an empty `section_hierarchy` +(no governing section) have `section_path = []`. + +**Step 4 — Place content builders into their immediate parent.** Each content +builder is appended to its immediate parent `SectionBuilder.children`. Objects with +`section_path = []` are appended to the owning `PageBuilder.children`. + +**Step 5 — Nest SectionBuilders within each other.** Each `SectionBuilder` with +a parent (determined in Step 2) is appended to that parent `SectionBuilder.children`. +Top-level `SectionBuilder`s (depth 0) are appended to the `PageBuilder.children` of +the page where their heading block appears. + +**Step 6 — Establish final children order (resolves CRITICAL-1).** After Steps 4 +and 5, each `PageBuilder.children` list and each `SectionBuilder.children` list +contains a mix of content builders and nested `SectionBuilder`s. These are sorted +into their final order by a single rule: **all items in a given `children` list are +ordered by the position of their originating block (the heading block for a +`SectionBuilder`; the source Marker block for a leaf builder) in the page's Stage-2 +flat classified sequence, ascending.** The classified sequence's original array +order is the single authoritative ordering source. No secondary sort key is needed +— two items in the same `children` list always originate from different positions +in the classified sequence. + +For content that spans multiple pages: a `SectionBuilder`'s children that originate +from different pages are ordered first by page index ascending, then by classified- +sequence position within that page ascending. + +**Result:** after Step 6, `section_path` is populated on every content builder's +`ProvenanceBuilder`, and every `PageBuilder.children` and `SectionBuilder.children` +list is in its final, deterministic order. + +### Stage 8: Global reading-order assignment + +**Input:** fully assembled builder tree from Stage 7. +**Output:** `ProvenanceBuilder.reading_order_index` populated on every builder. + +A single depth-first pre-order traversal with a single global counter starting at 0: + +1. Iterate `PageBuilder` instances in `page_number` ascending order. +2. Within each `PageBuilder`, iterate `children` in array order (the order + established by Stage 7 Step 6). +3. When a `SectionBuilder` is encountered: assign it the next counter value, then + recurse into its `children` (which may themselves contain a mix of leaf builders + and nested `SectionBuilder`s, processed in array order) before moving to the next + sibling. +4. When any non-Section builder is encountered: assign it the next counter value; + no recursion. + +A `SectionBuilder.children` list may contain both leaf object builders and nested +`SectionBuilder`s interleaved. The traversal assigns indices in array order, +recursing into any `SectionBuilder` encountered before continuing to the next +sibling — regardless of whether that sibling is a leaf or a nested section. + +This is the only stage that writes `reading_order_index` values. No earlier stage +sets a non-`None` value, and no later stage revises one. The counter is shared +across all pages — it is a single global counter for the entire document. + +### Stage 9: Canonical path computation + +**Input:** fully assembled, reading-order-finalized builder tree. +**Output:** `canonical_path: str` set on every builder. + +Per Schema v1.1 §2 and the complete canonical path table in §9 of this +specification, each builder's `canonical_path` is computed from its own +already-fixed tree position. `document_id` is not needed here — Stage 9 computes +paths only; ids are computed in Stage 10. + +### Stage 10: Materialization + +**Input:** fully assembled, reading-order-finalized, canonical-path-bearing builder +tree; `source_pdf_identifier`; `processing_context`. +**Output:** fully constructed, frozen Schema v1.1 `Document` object. + +`document_id = compute_document_id(source_pdf_identifier)` is computed exactly once +at the start of this stage. + +**Section materialization order (resolves MAJOR-6):** Section materialization uses +**post-order depth-first traversal** of the `SectionBuilder` tree — recurse into +all children of a `SectionBuilder` and materialize them before constructing the +parent `Section`. This ensures every child is already a frozen model when the +parent `Section` is constructed. For a three-level hierarchy (A contains B contains +C), the materialization order is: C's leaf children → C → B's other children → B → +A's other children → A. + +Materialization proceeds bottom-up across the full tree: + +1. `TableCellBuilder` → `TableCell` +2. `TableRowCellBuilder` → `TableRowCell`; `TableRowBuilder` → `TableRow` +3. `CaptionBuilder` → `Caption` +4. All leaf builders (`ParagraphBuilder`, `EquationBuilder`, `FootnoteBuilder`, + `ReferenceBuilder`, `PageHeaderBuilder`, `PageFooterBuilder`) → their + corresponding frozen models +5. `TableBuilder` (now has materialized `Caption`, `TableRow`, `TableCell` + children) → `Table` +6. `FigureBuilder` (now has materialized `Caption`) → `Figure` +7. `SectionBuilder`s, via post-order DFS as described above → `Section` +8. `PageBuilder`s (now have materialized children) → `Page` +9. **(resolves MAJOR-7)** `ProcessingMetadata` constructed from `processing_context`: + `marker_version`, `normalizer_version`, `source_marker_artifact_ref`, and + `processed_at` transferred verbatim from the `NormalizerProcessingContext` input. + No field is computed, transformed, or defaulted — all four values are caller- + supplied and passed through unchanged. +10. **(resolves MAJOR-8)** `Document` assembled with: `pages` (the list of + materialized `Page` objects from step 8, in `page_number` ascending order), + `metadata` (assembled in this step from the materialized page list — + `page_count`, `has_front_matter_page`, `title` as per the rule below), + `statistics` (assembled in this step by type-counting traversal of the + materialized page list), `processing_metadata` (from step 9), and + `source_pdf_identifier` (the `source_pdf_identifier` input parameter, verbatim). + This `Document` instance is the object passed to Stage 11 and returned to the + caller if Stage 11 passes. + +**Metadata assembly (part of step 10):** +- `Metadata.page_count = len(pages)` +- `Metadata.has_front_matter_page = any(p.is_front_matter for p in pages)` +- `Metadata.title`: the `html` content of the first `GENUINE_SECTION_HEADER`- + classified block on the first non-front-matter page, taken verbatim. `None` if + no `GENUINE_SECTION_HEADER` block exists on any non-front-matter page. +- `Statistics.unresolved_footnote_count`: count of `Footnote` objects with + `attached_object_id is None`. + +Each frozen model is constructed with all fields finalized — no field is left at +its builder default of `None` at construction time unless Schema v1.1 explicitly +declares that field as `Optional`. + +### Stage 11: Whole-tree validation + +See §24 for the complete invariant list. This stage raises on any violation — per +§1's and §2's fail-whole contract. + +--- + +## 6. Ordered Sequence of Stages (Summary Table) + +| # | Stage | Primary output | Depends on | +|---|---|---|---| +| 0 | Front-matter detection | `is_front_matter` flags | Raw page text content | +| 1 | Page builder construction | `PageBuilder` shells | Stage 0 | +| 1.5 | Wrapper unwrapping | `list[UnwrappedBlock]` per page | Stage 1 | +| 2 | Block classification | Disposition tags per block | Stage 1.5 | +| 3 | Leaf builder construction | Object builders (partial) | Stage 2 | +| 4 | Caption resolution | `caption` on Table/Figure builders | Stage 3 | +| 5 | Table internal structure | `rows`/`cells` on Table builders | Stage 4 | +| 6 | Footnote attachment | `footnote_ids`/`attached_object_id` | Stage 5 | +| 7 | Section tree assembly | Nested SectionBuilder tree; `section_path` | Stage 6 | +| 8 | Global reading order | `reading_order_index` everywhere | Stage 7 | +| 9 | Canonical path computation | `canonical_path` on every builder | Stage 8 | +| 10 | Materialization | Frozen Schema v1.1 models; `Metadata`/`Statistics`/`ProcessingMetadata`/`Document` | Stage 9 | +| 11 | Whole-tree validation | Pass / raise | Stage 10 | + +This ordering is a hard contract. + +--- + +## 7. Rules Governing Deterministic Behavior + +1. **No wall-clock or random input affects structural content.** `processed_at` is + stored verbatim into `ProcessingMetadata` and influences nothing else. +2. **No dict iteration order is relied upon.** `section_hierarchy` dicts are always + sorted by numeric depth-key before use (Stage 7). +3. **All heuristic thresholds are fixed constants** — the front-matter two-signal + threshold (Stage 0), the caption-window three-block lookforward (Stage 2), the + bbox comparison and tie-break in Stage 6. +4. **Identical input invariably produces identical output.** Verified operationally + via a required test: running `normalize()` twice on the same inputs (with + `processed_at` held fixed) must produce two `Document` objects whose + `model_dump_json()` outputs are byte-identical. +5. **Stage ordering is fixed and non-parallelized.** +6. **Builder construction order within a stage is array order** (the unwrapped + sequence's existing order), never sorted or permuted by the stage itself unless + the stage description explicitly specifies an ordering operation. + +--- + +## 8. Provenance Propagation Strategy + +Every Schema v1.1 object carries a `StructuralProvenance`. Provenance fields are +populated incrementally across stages on the `ProvenanceBuilder`: + +| Provenance field | Populated at | Source | +|------------------|-------------|--------| +| `marker_block_ids` | Stage 3 (or Stage 4 for Caption) | Directly from Marker block id(s) | +| `page_number` | Stage 3 | From page index (Stage 1) | +| `bbox` | Stage 3 (or Stage 4 for Caption) | From Marker block `bbox` field | +| `contributing_bboxes` | Stage 3 or 4 | Multi-block sources only (Pattern B captions) | +| `polygon` | Stage 3 | From Marker block `polygon` field | +| `section_path` | Stage 7 | Derived from `section_hierarchy` reverse-lookup resolution | +| `reading_order_index` | Stage 8 | Depth-first traversal counter | + +No stage downstream of an object's initial construction ever modifies +`marker_block_ids`, `bbox`, `contributing_bboxes`, or `polygon`. `section_path` +and `reading_order_index` are each written exactly once, at their respective stages. + +--- + +## 9. Identifier Generation and Canonical Path Table + +Identifier computation: `id = "doc:" + sha256(document_id + "|" + canonical_path)[:16]` + +`document_id` is computed once per document at the start of Stage 10. The complete +`canonical_path` rule for every object type: + +| Object type | Canonical path rule | Notes | +|-------------|--------------------|-| +| `Page` | `/page/{page_number}` | 0-indexed array position from Stage 1 | +| `Section` | `{page_canonical_path}/section/{heading_marker_block_id}` | Uses the governing `SectionHeader` block's Marker id; page component is the page where the heading block appears | +| `Paragraph` | `{page_canonical_path}/paragraph/{reading_order_index}` | Page-scoped prefix regardless of Section containment (see note below) | +| `Table` | `{page_canonical_path}/table/{marker_block_id}` | — | +| `TableRow` | `{table_canonical_path}/row/{row_ordinal}` | 0-indexed position in `Table.rows` | +| `TableRowCell` | `{tablerow_canonical_path}/cell/{cell_ordinal}` | 0-indexed position in `TableRow.cells` | +| `TableCell` | `{table_canonical_path}/cell_evidence/{marker_block_id}` | `cell_evidence` namespace avoids collision with `TableRowCell` paths | +| `Figure` | `{page_canonical_path}/figure/{marker_block_id}` | — | +| `Caption` | `{parent_canonical_path}/caption` | Parent is the `Table` or `Figure` | +| `Equation` | `{page_canonical_path}/equation/{reading_order_index}` | Same rationale as `Paragraph` | +| `Footnote` | `{page_canonical_path}/footnote/{marker_block_id}` | — | +| `Reference` | `{page_canonical_path}/reference/{reading_order_index}` | Same rationale as `Paragraph` | +| `PageHeader` | `{page_canonical_path}/page_header/{marker_block_id}` | — | +| `PageFooter` | `{page_canonical_path}/page_footer/{marker_block_id}` | — | + +**Path prefix rule for section-nested objects (resolves MINOR-4 from +implementation-readiness audit):** Canonical paths for all objects except `Page` +and `Section` use `{page_canonical_path}` as their prefix regardless of whether +the object is contained in a `Section`. Containment depth is not reflected in +canonical paths. This is a deliberate choice: paths are stable identifiers, not +location strings. An object moved between sections by a future document revision +retains the same `id`. + +**Why some objects use `marker_block_id` and others use `reading_order_index`:** +Objects with a 1:1 Marker block use that block's id as the unique path component. +`Paragraph`, `Equation`, and `Reference` objects use `reading_order_index` because +their Marker ids contain a trailing ordinal confirmed non-monotonic with reading +order (finding 3.4) — `reading_order_index` produces paths that are both unique +and ordered by final reading position. + +**Uniqueness guarantee:** because `document_id` is fixed per document and each +`canonical_path` is unique within the document, `id` values are unique with +overwhelming probability. Stage 11 invariant 5 (§24) verifies uniqueness directly. + +--- + +## 10. Reading-Order Preservation + +Fully specified in Stage 8 (§5). `reading_order_index` is a single global counter +assigned by one traversal, comparable across the entire document regardless of +section nesting depth — the property Schema v1.1 §18 requires. + +--- + +## 11. Section Construction Algorithm + +Fully specified in Stage 7 (§5). The six-step algorithm handles multi-page section +accumulation, depth/parent resolution via reverse lookup (not forward-path +assumption), content placement, section nesting, and final children ordering. + +--- + +## 12. Page Construction + +Fully specified in Stage 1 (shell) and completed across Stages 2–7. A +`PageBuilder.children` list is not finalized until Stage 7 Step 6. + +--- + +## 13. Paragraph Normalization + +Stage 3 and Schema v1.1 §10. `Paragraph.text` is the source Marker block's `html` +verbatim. No content transformation is applied. + +--- + +## 14. Table Normalization + +Across Stages 3 (builder shell), 4 (caption), 5 (rows/cells), and 6 (footnote +attachment). + +--- + +## 15. Caption Attachment + +Stage 4 (§5) and Schema v1.1 §11. Two-pattern resolution implements finding 3.1. + +--- + +## 16. Figure Normalization + +Identical to Table normalization minus Stage 5's row/cell parsing. Stage 4's +Pattern A lookup uses `wrapper_context.block_id` matching (position-independent), +correctly handling `FigureGroup`'s `[Figure, Caption]` child order without +positional assumptions. + +--- + +## 17. Equation Normalization + +Stage 3 and Schema v1.1 §14. `raw_math` is verbatim. `equation_number = None`. + +--- + +## 18. Footnote Attachment + +Stage 6 (§5) and Schema v1.1 §15. Page-scoped only. + +--- + +## 19. Reference Normalization + +Stages 2 (classification) and 3 (construction) and Schema v1.1 §16. `raw_text` +is verbatim. + +--- + +## 20. Metadata Construction + +Stage 10 (§5) and Schema v1.1 §5. `Metadata` never gains author/journal/year/DOI +fields at this layer. + +--- + +## 21. Statistics Computation + +Stage 10 (§5) and Schema v1.1 §7. All fields are derived counts over the +materialized tree. + +--- + +## 22. Error Handling Philosophy + +The Normalizer follows a **fail-loud, fail-whole** philosophy: + +- **Pydantic validation errors** propagate immediately. +- **Resolution failures modeled as legitimate outcomes** (`caption = None`; + `attached_object_id = None`; `Metadata.title = None`) are NOT errors. +- **Unrecognized Marker block types** (not in Stage 2's classification table): + `UnrecognizedBlockTypeError`. `TableGroup`, `FigureGroup`, and `ListGroup` are + recognized container types handled by Stage 1.5 and never reach Stage 2's + classification pass as unrecognized blocks. +- **No retry, fallback parser, or degraded-mode operation.** + +Exception types: + +```python +class NormalizerError(Exception): + """Base class for all Normalizer-raised exceptions.""" + +class UnrecognizedBlockTypeError(NormalizerError): + block_type: str + marker_block_id: str + page_index: int + +class WholeTreeInvariantViolationError(NormalizerError): + invariant_number: int + description: str + offending_object_ids: list[str] +``` + +--- + +## 23. Logging Strategy + +Structured log records at the following points only: + +- **Stage 0, per flagged page:** which signals (S1–S4) fired, and whether the + page was flagged. +- **Stage 2, per `PICTURE` block discarded:** Marker block id, page index. +- **Stage 4, per caption resolved:** pattern used (A or B); or which + `Table`/`Figure` builder produced `caption = None` and why. +- **Stage 6, per footnote:** whether attached and to which object; whether a + missing-bbox case prevented matching. +- **Stage 10 completion:** final `Statistics` field set plus + `processing_context.normalizer_version`. +- **At error time:** full identifying context (Marker block id, page index, stage + name) before propagating. + +No log record contains scientific interpretation of content. + +--- + +## 24. Validation Invariants (Whole-Tree, Stage 11) + +1. **Reading order strictly increasing** across the full depth-first traversal of + the materialized tree. +2. **Every non-`None` `Footnote.attached_object_id` matches the `id` of a `Table` + or `Figure` that exists in the tree.** +3. **Symmetric footnote-table/figure consistency:** every `Table`/`Figure.footnote_ids` + entry matches the `id` of a `Footnote` in the tree whose `attached_object_id` + points back to this same `Table`/`Figure`. +4. **Every object's `section_path` matches an actual ancestor `Section` chain in + the final tree.** +5. **No two objects in the tree share the same `id`.** +6. **`Document.pages` is sorted ascending by `page_number` with no duplicates.** +7. **Every count in `Statistics` matches an independent re-traversal:** a second + recursive tree walk using a separate counting function compares field-by-field + against Stage 10's assembled `Statistics`; any discrepancy raises + `WholeTreeInvariantViolationError` with `invariant_number=7`. +8. **(resolves MAJOR-3) Every builder constructed in Stage 3 appears exactly once + in the materialized tree — neither omitted nor duplicated.** Checked by + collecting the set of `id` values of all materialized leaf and composite objects + and comparing it against the set of expected objects derived from Stage 3's + output list. Builders excluded from this check (legitimate non-tree participants): + those originating from `CAPTION_TEXT`-classified blocks (consumed into + `Caption` objects, checked indirectly via invariant 2/3 equivalents), + `TABLE_CELL_EVIDENCE`-classified blocks (checked via `Table.cells` membership), + and `PICTURE`-classified blocks (legitimately discarded). All other Stage-3 + builders must appear in the tree exactly once. + +--- + +## 25. Extension Points for Future Marker Versions + +- **Stage 2's classification table** is the single point for every recognized + `block_type`. A new Marker block type requires exactly one addition here. +- **Stage 1.5's unwrapper** lists all wrapper block types. A new wrapper type + requires exactly one addition here plus one `WrapperContext.wrapper_type` value. +- **Stage 0's front-matter signals** are an explicit four-signal table. New signals + require a confirmed representative paper. +- **Stage 4's caption patterns** are a named two-pattern enumeration. A third + pattern follows the same template without touching A or B. +- **Stage 6's footnote-matching strategy** is one geometric strategy. A second + strategy is added alongside it; arbitration is deferred until designed. + +--- + +## 26. Explicit Boundaries with Later Phases + +- **Retrieval:** building any query interface over the finished `Document`. +- **Extraction:** recognizing treatments, species, variables, traits, or any + scientific entity from text fields. +- **IR construction:** `Treatment`, `Observation`, `Measurement`, etc. Parsing + `Reference.raw_text` into author/year/journal/DOI is IR-layer only. +- **Validation (scientific):** unit consistency, impossible-value detection, + ontology compliance. +- **Scientist Review:** any UI, approval workflow, or evidence presentation. +- **BETYdb Export:** any mapping from IR objects to BETYdb schema or records. + +--- + +## Appendix A: Issue Resolution Index + +Every issue from the v1.0-draft freeze review and the v1.0 implementation-readiness +audit is listed here with a pointer to its resolution. + +### From v1.0-draft freeze review (resolved in v1.0, carried forward) + +- **CRITICAL-1** (frozen model population contradiction): §4.1 builder pattern. +- **CRITICAL-2** (TableGroup/FigureGroup/ListGroup unwrapping undefined): §4.2 and + §5 Stage 1.5. +- **MAJOR-1** through **MAJOR-10**, **MINOR-1** through **MINOR-8**: resolved in + v1.0; see v1.0 revision record. + +### From v1.0 implementation-readiness audit (resolved in v1.1) + +- **CRITICAL-1** (ungoverned content and Section interleaving order unspecified): + §5 Stage 7 Step 6. +- **CRITICAL-2** (SectionBuilder nesting assumed SectionHeader blocks carry + `section_hierarchy`): §5 Stage 7 Step 2 — replaced with reverse-lookup algorithm. +- **CRITICAL-3** (`kind` discriminator never assigned): §4.1 `kind` field rule; + §5 Stage 3 `kind` field assignment paragraph. +- **CRITICAL-4** (Stage 2 CAPTION_LABEL lookahead used not-yet-assigned + dispositions): §5 Stage 2 CAPTION_LABEL override rule — changed to `block_type` + check. +- **MAJOR-1** (S4 referenced undefined "section-heading vocabulary"): §5 Stage 0 + S4 row — changed to structural `block_type == "SectionHeader"` check. +- **MAJOR-2** (builder field lists incomplete for most types): §4.1 complete builder + field rule. +- **MAJOR-3** (§24 missing invariant for Stage-3 object coverage): §24 invariant 8. +- **MAJOR-4** (no rule for `cells` list of bare tables): §5 Stage 5 bare-tables + paragraph. +- **MAJOR-5** (Stage 6 tie-break unspecified): §5 Stage 6 tie-break clause. +- **MAJOR-6** (Stage 10 Section materialization order unstated): §5 Stage 10 post- + order DFS paragraph. +- **MAJOR-7** (`ProcessingMetadata` construction absent from Stage 10 steps): §5 + Stage 10 step 9. +- **MAJOR-8** (`Document` root constructor call never specified): §5 Stage 10 + step 10. +- **MINOR-4** (canonical path prefix for section-nested objects ambiguous): §9 + path prefix rule paragraph. \ No newline at end of file diff --git a/docs/paper_analysis_template.md b/docs/paper_analysis_template.md new file mode 100644 index 0000000..170b77c --- /dev/null +++ b/docs/paper_analysis_template.md @@ -0,0 +1,21 @@ +Citation + +Site + +Species + +Treatments + +Controls + +Management Events + +Traits/Yields + +Important Tables + +Important Figures + +Ambiguities + +Potential Extraction Challenges \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..d591df2 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,12 @@ +[project] +name = "betydb-extraction" +version = "0.1.0" +requires-python = ">=3.12" +dependencies = ["pydantic>=2.0"] + +[tool.setuptools.packages.find] +where = ["src"] + +[build-system] +requires = ["setuptools>=68"] +build-backend = "setuptools.build_meta" diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..9e901cc --- /dev/null +++ b/requirements.txt @@ -0,0 +1,6 @@ +pandas +numpy +pydantic +pytest +jupyter +python-dotenv \ No newline at end of file diff --git a/scripts/run_normalize_smoke_test.py b/scripts/run_normalize_smoke_test.py new file mode 100644 index 0000000..d23f86b --- /dev/null +++ b/scripts/run_normalize_smoke_test.py @@ -0,0 +1,78 @@ +""" +Smoke test: run normalize() end-to-end on real Marker output. +Not a pytest file — a standalone script to prove the pipeline runs. +""" +from __future__ import annotations + +import json +import sys +import traceback +from datetime import datetime, timezone +from pathlib import Path + +from betydb_extraction.marker_adapter.raw_model import MarkerDocument +from betydb_extraction.normalizer.api import normalize +from betydb_extraction.normalizer.context import NormalizerProcessingContext + +PAPERS = { + "pecan": "data/marker_output/pecan/pecan.json", + "nutrient_cycling": "data/marker_output/Nutrient-cycling/Nutrient-cycling.json", + "culti_mixtures": "data/marker_output/culti-mixtures/culti-mixtures.json", +} + +OUTPUT_DIR = Path("data/normalized_output") + + +def _dump_document(document, path: Path) -> None: + """Write a materialized Document out as JSON for manual inspection. + Tries Pydantic v2's model_dump_json first, falls back to v1's .json().""" + path.parent.mkdir(parents=True, exist_ok=True) + if hasattr(document, "model_dump_json"): + path.write_text(document.model_dump_json(indent=2)) + else: + path.write_text(document.json(indent=2)) + +def run_one(name: str, path: str) -> bool: + print(f"\n{'='*70}\n{name} ({path})\n{'='*70}") + try: + raw_json = json.loads(Path(path).read_text()) + marker_doc = MarkerDocument(root=raw_json, source_marker_json_path=path) + + ctx = NormalizerProcessingContext( + marker_version="unknown", + normalizer_version="0.1.0-smoketest", + source_marker_artifact_ref=path, + processed_at=datetime.now(timezone.utc), + ) + + document = normalize( + marker_document=marker_doc, + source_pdf_identifier=f"smoketest:{name}", + processing_context=ctx, + ) + + print(f"OK — pages={len(document.pages)}") + print(f" metadata.title = {document.metadata.title!r}") + print(f" statistics = {document.statistics}") + out_path = OUTPUT_DIR / f"{name}.json" + _dump_document(document, out_path) + print(f" saved to {out_path}") + return True + + except Exception: + print(f"FAILED on {name}") + traceback.print_exc() + return False + + +def main() -> None: + results = {name: run_one(name, path) for name, path in PAPERS.items()} + print(f"\n{'='*70}\nSUMMARY\n{'='*70}") + for name, ok in results.items(): + print(f" {name}: {'PASS' if ok else 'FAIL'}") + if not all(results.values()): + sys.exit(1) + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/src/betydb_extraction/__init__.py b/src/betydb_extraction/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/betydb_extraction/document/__init__.py b/src/betydb_extraction/document/__init__.py new file mode 100644 index 0000000..7fbea68 --- /dev/null +++ b/src/betydb_extraction/document/__init__.py @@ -0,0 +1,72 @@ +from __future__ import annotations + +from betydb_extraction.document.caption import Caption +from betydb_extraction.document.document import Document +from betydb_extraction.document.enums import NodeKind +from betydb_extraction.document.equation import Equation +from betydb_extraction.document.figure import Figure +from betydb_extraction.document.footnote import Footnote +from betydb_extraction.document.identifiers import ( + DOCUMENT_ID_PREFIX, + OBJECT_ID_PREFIX, + compute_document_id, + compute_object_id, + is_valid_document_id, + is_valid_object_id, + validate_document_id_shape, + validate_object_id_shape, +) +from betydb_extraction.document.metadata import Metadata +from betydb_extraction.document.page import Page, PageChild +from betydb_extraction.document.page_furniture import PageFooter, PageHeader +from betydb_extraction.document.paragraph import Paragraph +from betydb_extraction.document.processing_metadata import ProcessingMetadata +from betydb_extraction.document.provenance import ( + BoundingBox, + Polygon, + StructuralProvenance, +) +from betydb_extraction.document.reference import Reference +from betydb_extraction.document.section import Section, SectionChild +from betydb_extraction.document.statistics import Statistics +from betydb_extraction.document.table import Table, TableCell, TableRow, TableRowCell + +__all__ = [ + "BoundingBox", + "Caption", + "DOCUMENT_ID_PREFIX", + "Document", + "Equation", + "Figure", + "Footnote", + "Metadata", + "NodeKind", + "OBJECT_ID_PREFIX", + "Page", + "PageChild", + "PageFooter", + "PageHeader", + "Paragraph", + "Polygon", + "ProcessingMetadata", + "Reference", + "Section", + "SectionChild", + "Statistics", + "StructuralProvenance", + "Table", + "TableCell", + "TableRow", + "TableRowCell", + "compute_document_id", + "compute_object_id", + "is_valid_document_id", + "is_valid_object_id", + "validate_document_id_shape", + "validate_object_id_shape", +] + +# Resolve forward references for the recursive Section <-> SectionChild +# union and the Page -> Section dependency, in dependency order. +Page.model_rebuild() +Document.model_rebuild() \ No newline at end of file diff --git a/src/betydb_extraction/document/caption.py b/src/betydb_extraction/document/caption.py new file mode 100644 index 0000000..5ee9633 --- /dev/null +++ b/src/betydb_extraction/document/caption.py @@ -0,0 +1,51 @@ +from __future__ import annotations + +from pydantic import BaseModel, ConfigDict, Field + +from betydb_extraction.document.provenance import StructuralProvenance + +__all__ = ["Caption"] + + +class Caption(BaseModel): + + model_config = ConfigDict(frozen=True, extra="forbid") + + label: str | None = Field( + default=None, + description=( + "E.g. 'Table 3' or 'Figure 1'. Present whenever a Caption block " + "(Pattern A) or a SectionHeader label block (Pattern B) was " + "found." + ), + ) + text: str | None = Field( + default=None, + description=( + "The descriptive caption sentence. From the Caption block's " + "content (Pattern A) or the Text block immediately following " + "the label (Pattern B)." + ), + ) + trailing_notes: str | None = Field( + default=None, + description=( + "The trailing 'Note: ...' Text block sometimes observed " + "immediately after a Table, distinct from both label/text and " + "from Footnote objects (Section 15). Kept as its own field " + "because it was empirically observed to be part of the caption " + "apparatus, not body text, but also not a true Marker Footnote " + "block." + ), + ) + provenance: StructuralProvenance = Field( + description=( + "marker_block_ids lists every contributing Marker block (one " + "for Pattern A's single Caption block; two or three for " + "Pattern B's SectionHeader + Text + optional trailing Text). " + "Uses contributing_bboxes rather than a single bbox whenever " + "more than one block contributes, since collapsing " + "non-adjacent regions into one bbox would misrepresent the " + "geometry." + ) + ) \ No newline at end of file diff --git a/src/betydb_extraction/document/document.py b/src/betydb_extraction/document/document.py new file mode 100644 index 0000000..69f2164 --- /dev/null +++ b/src/betydb_extraction/document/document.py @@ -0,0 +1,65 @@ +"""The Document model. + +Implements the Document Schema Specification, Section 4 ("Document"): the +root container for one processed paper. +""" +from __future__ import annotations + +from pydantic import BaseModel, ConfigDict, Field, field_validator, model_validator + +from betydb_extraction.document.identifiers import validate_document_id_shape +from betydb_extraction.document.metadata import Metadata +from betydb_extraction.document.page import Page +from betydb_extraction.document.processing_metadata import ProcessingMetadata +from betydb_extraction.document.statistics import Statistics + +__all__ = ["Document"] + + +class Document(BaseModel): + + model_config = ConfigDict(frozen=True, extra="forbid") + + id: str = Field(description="document_id, per Spec Section 2.") + source_pdf_identifier: str = Field( + description=( + "The stable external identifier used to compute id (DOI or " + "content hash). Stored explicitly so the id's derivation is " + "independently checkable, not just trusted." + ) + ) + metadata: Metadata = Field(description="Spec Section 5.") + processing_metadata: ProcessingMetadata = Field(description="Spec Section 6.") + statistics: Statistics = Field(description="Spec Section 7.") + pages: list[Page] = Field( + min_length=1, + description=( + "Ordered by page number ascending; this ordering is also the " + "top level of global reading order." + ), + ) + + @field_validator("id") + @classmethod + def _check_id_shape(cls, value: str) -> str: + return validate_document_id_shape(value) + + @model_validator(mode="after") + def _check_pages_sorted_and_unique(self) -> "Document": + page_numbers = [page.page_number for page in self.pages] + + if len(page_numbers) != len(set(page_numbers)): + raise ValueError( + "Document.pages contains duplicate page_number values; " + "each page must have a unique page_number." + ) + + if page_numbers != sorted(page_numbers): + raise ValueError( + "Document.pages must be ordered ascending by page_number. " + "A Marker-side page omission (a gap in the sequence) is " + "permitted and preserved, not silently re-numbered -- but " + "the list order itself must still be ascending." + ) + + return self \ No newline at end of file diff --git a/src/betydb_extraction/document/enums.py b/src/betydb_extraction/document/enums.py new file mode 100644 index 0000000..c7f61ee --- /dev/null +++ b/src/betydb_extraction/document/enums.py @@ -0,0 +1,18 @@ +from __future__ import annotations + +from enum import Enum + +__all__ = ["NodeKind"] + + +class NodeKind(str, Enum): + + SECTION = "section" + PARAGRAPH = "paragraph" + TABLE = "table" + FIGURE = "figure" + EQUATION = "equation" + FOOTNOTE = "footnote" + PAGE_HEADER = "page_header" + PAGE_FOOTER = "page_footer" + REFERENCE = "reference" \ No newline at end of file diff --git a/src/betydb_extraction/document/equation.py b/src/betydb_extraction/document/equation.py new file mode 100644 index 0000000..74fefed --- /dev/null +++ b/src/betydb_extraction/document/equation.py @@ -0,0 +1,48 @@ +"""The Equation model. + +Implements the Document Schema Specification, Section 14 ("Equation"). +""" + +from __future__ import annotations + +from typing import Literal + +from pydantic import BaseModel, ConfigDict, Field, field_validator + +from betydb_extraction.document.enums import NodeKind +from betydb_extraction.document.identifiers import validate_object_id_shape +from betydb_extraction.document.provenance import StructuralProvenance + +__all__ = ["Equation"] + + +class Equation(BaseModel): + + model_config = ConfigDict(frozen=True, extra="forbid") + + kind: Literal[NodeKind.EQUATION] = Field( + default=NodeKind.EQUATION, + description="Discriminator for Page/Section children unions.", + ) + id: str = Field(description="Deterministic identifier, per Spec Section 2.") + provenance: StructuralProvenance = Field( + description="marker_block_ids = [the Equation block's id]." + ) + raw_math: str = Field( + description=( + "The verbatim MathML-ish content, including any equation " + "number embedded inline." + ) + ) + equation_number: str | None = Field( + default=None, + description=( + "A slot for the parsed-out equation number, e.g. '1'. Population " + "logic is out of scope for this layer." + ), + ) + + @field_validator("id") + @classmethod + def _check_id_shape(cls, value: str) -> str: + return validate_object_id_shape(value) \ No newline at end of file diff --git a/src/betydb_extraction/document/figure.py b/src/betydb_extraction/document/figure.py new file mode 100644 index 0000000..47a39ce --- /dev/null +++ b/src/betydb_extraction/document/figure.py @@ -0,0 +1,56 @@ +"""The Figure model. + +Implements the Document Schema Specification, Section 13 ("Figure"). +""" +from __future__ import annotations +import base64 +from typing import Literal +from pydantic import BaseModel, ConfigDict, Field, field_serializer, field_validator +from betydb_extraction.document.caption import Caption +from betydb_extraction.document.enums import NodeKind +from betydb_extraction.document.identifiers import validate_object_id_shape +from betydb_extraction.document.provenance import StructuralProvenance +__all__ = ["Figure"] +class Figure(BaseModel): + model_config = ConfigDict(frozen=True, extra="forbid") + kind: Literal[NodeKind.FIGURE] = Field( + default=NodeKind.FIGURE, + description="Discriminator for Page/Section children unions.", + ) + id: str = Field(description="Deterministic identifier, per Spec Section 2.") + provenance: StructuralProvenance = Field( + description=( + "marker_block_ids = [the Figure block's id] (and FigureGroup id, " + "if present)." + ) + ) + caption: Caption | None = Field(default=None) + image_data: bytes | None = Field( + default=None, + description=( + "Base64-decoded raster image content from Marker's images field, " + "when present. Serializes as a base64 string in JSON per Spec " + "Section 19." + ), + ) + @field_validator("id") + @classmethod + def _check_id_shape(cls, value: str) -> str: + return validate_object_id_shape(value) + + @field_serializer("image_data", when_used="json") + def _serialize_image_data(self, value: bytes | None) -> str | None: + if value is None: + return None + return base64.b64encode(value).decode("ascii") + + @field_validator("image_data", mode="before") + @classmethod + def _decode_image_data(cls, value): + # Accepts a base64 string (e.g. when re-hydrating from JSON) or + # raw bytes (e.g. direct Python construction by the Normalizer) + # interchangeably, so model_validate_json -> model_validate_json + # and direct construction both work uniformly. + if isinstance(value, str): + return base64.b64decode(value) + return value \ No newline at end of file diff --git a/src/betydb_extraction/document/footnote.py b/src/betydb_extraction/document/footnote.py new file mode 100644 index 0000000..86e9045 --- /dev/null +++ b/src/betydb_extraction/document/footnote.py @@ -0,0 +1,43 @@ +"""The Footnote model. + +Implements the Document Schema Specification, Section 15 ("Footnote"). +""" + +from __future__ import annotations + +from typing import Literal + +from pydantic import BaseModel, ConfigDict, Field, field_validator + +from betydb_extraction.document.enums import NodeKind +from betydb_extraction.document.identifiers import validate_object_id_shape +from betydb_extraction.document.provenance import StructuralProvenance + +__all__ = ["Footnote"] + + +class Footnote(BaseModel): + model_config = ConfigDict(frozen=True, extra="forbid") + + kind: Literal[NodeKind.FOOTNOTE] = Field( + default=NodeKind.FOOTNOTE, + description="Discriminator for Page/Section children unions.", + ) + id: str = Field(description="Deterministic identifier, per Spec Section 2.") + provenance: StructuralProvenance = Field( + description="marker_block_ids = [the Footnote block's id]." + ) + raw_text: str = Field(description="Verbatim footnote content.") + attached_object_id: str | None = Field( + default=None, + description=( + "The id of the Table or Figure this footnote was determined to " + "belong to. None when unresolved -- a legitimate outcome, not an " + "implementation gap." + ), + ) + + @field_validator("id") + @classmethod + def _check_id_shape(cls, value: str) -> str: + return validate_object_id_shape(value) \ No newline at end of file diff --git a/src/betydb_extraction/document/identifiers.py b/src/betydb_extraction/document/identifiers.py new file mode 100644 index 0000000..e17256b --- /dev/null +++ b/src/betydb_extraction/document/identifiers.py @@ -0,0 +1,69 @@ +from __future__ import annotations + +import hashlib +import re + +__all__ = [ + "DOCUMENT_ID_PREFIX", + "OBJECT_ID_PREFIX", + "compute_document_id", + "compute_object_id", + "is_valid_document_id", + "is_valid_object_id", + "validate_document_id_shape", + "validate_object_id_shape", +] + +DOCUMENT_ID_PREFIX = "betydoc:" +OBJECT_ID_PREFIX = "doc:" + +_HASH_TRUNCATION_LENGTH = 16 + +# An identifier is the literal prefix followed by exactly 16 lowercase +# hexadecimal characters, per the truncation length used by both +# compute_document_id and compute_object_id below. +_DOCUMENT_ID_PATTERN = re.compile( + rf"^{re.escape(DOCUMENT_ID_PREFIX)}[0-9a-f]{{{_HASH_TRUNCATION_LENGTH}}}$" +) +_OBJECT_ID_PATTERN = re.compile( + rf"^{re.escape(OBJECT_ID_PREFIX)}[0-9a-f]{{{_HASH_TRUNCATION_LENGTH}}}$" +) + + +def compute_document_id(source_pdf_identifier: str) -> str: + digest = hashlib.sha256(source_pdf_identifier.encode("utf-8")).hexdigest() + return DOCUMENT_ID_PREFIX + digest[:_HASH_TRUNCATION_LENGTH] + + +def compute_object_id(document_id: str, canonical_path: str) -> str: + payload = f"{document_id}|{canonical_path}".encode("utf-8") + digest = hashlib.sha256(payload).hexdigest() + return OBJECT_ID_PREFIX + digest[:_HASH_TRUNCATION_LENGTH] + + +def is_valid_document_id(value: str) -> bool: + return bool(_DOCUMENT_ID_PATTERN.match(value)) + + +def is_valid_object_id(value: str) -> bool: + return bool(_OBJECT_ID_PATTERN.match(value)) + + +def validate_document_id_shape(value: str) -> str: + if not is_valid_document_id(value): + raise ValueError( + f"{value!r} is not a valid Document id " + f"(expected '{DOCUMENT_ID_PREFIX}' + " + f"{_HASH_TRUNCATION_LENGTH} lowercase hex characters)" + ) + return value + + +def validate_object_id_shape(value: str) -> str: + if not is_valid_object_id(value): + raise ValueError( + f"{value!r} is not a valid object id " + f"(expected '{OBJECT_ID_PREFIX}' + " + f"{_HASH_TRUNCATION_LENGTH} lowercase hex characters)" + ) + return value \ No newline at end of file diff --git a/src/betydb_extraction/document/metadata.py b/src/betydb_extraction/document/metadata.py new file mode 100644 index 0000000..2aba05d --- /dev/null +++ b/src/betydb_extraction/document/metadata.py @@ -0,0 +1,40 @@ +"""The Metadata model. + +Implements the Document Schema Specification, Section 5 ("Metadata"): +bibliographic and identification facts about the paper, to the extent +they are structurally recoverable. +""" +from __future__ import annotations + +from pydantic import BaseModel, ConfigDict, Field + +__all__ = ["Metadata"] + + +class Metadata(BaseModel): + model_config = ConfigDict(frozen=True, extra="forbid") + + title: str | None = Field( + default=None, + description=( + "Taken verbatim from the first/top-level SectionHeader or " + "title-styled block on the front matter page, if structurally " + "identifiable." + ), + ) + page_count: int = Field( + ge=0, + description=( + "Count of Page objects. Redundant with len(Document.pages) but " + "kept as an explicit field since Statistics is meant to hold " + "derived counts, while this is a basic identifying fact worth " + "surfacing without traversing the tree." + ), + ) + has_front_matter_page: bool = Field( + description=( + "Whether any page was structurally flagged as publisher wrapper " + "content. Aggregates Page.is_front_matter (Section 8) across all " + "pages." + ) + ) \ No newline at end of file diff --git a/src/betydb_extraction/document/page.py b/src/betydb_extraction/document/page.py new file mode 100644 index 0000000..879a584 --- /dev/null +++ b/src/betydb_extraction/document/page.py @@ -0,0 +1,65 @@ +"""The Page model. + +Implements the Document Schema Specification, Section 8 ("Page"): one +PDF page's structural content, in reading order. +""" +from __future__ import annotations + +from typing import Annotated, Union + +from pydantic import BaseModel, ConfigDict, Field, field_validator + +from betydb_extraction.document.equation import Equation +from betydb_extraction.document.figure import Figure +from betydb_extraction.document.footnote import Footnote +from betydb_extraction.document.identifiers import validate_object_id_shape +from betydb_extraction.document.page_furniture import PageFooter, PageHeader +from betydb_extraction.document.paragraph import Paragraph +from betydb_extraction.document.provenance import StructuralProvenance +from betydb_extraction.document.section import Section +from betydb_extraction.document.table import Table + +__all__ = ["Page", "PageChild"] + + +PageChild = Annotated[ + Union[Section, Paragraph, Table, Figure, Equation, Footnote, PageHeader, PageFooter], + Field(discriminator="kind"), +] + +class Page(BaseModel): + model_config = ConfigDict(frozen=True, extra="forbid") + + id: str = Field( + description=( + "Deterministic identifier, per Spec Section 2; " + "canonical_path = '/page/{page_number}'." + ) + ) + page_number: int = Field( + ge=0, + description="Zero-indexed, matching Marker's own page numbering.", + ) + provenance: StructuralProvenance = Field( + description="marker_block_ids = [the Marker Page block's id]." + ) + children: list[PageChild] = Field( + default_factory=list, + description=( + "Top-level content of the page, in final reading order (Spec " + "Section 3.4)." + ), + ) + is_front_matter: bool = Field( + description=( + "True if this page was identified as publisher wrapper content " + "(journal cover, 'Submit your article,' ISSN-only content, " + "etc.) rather than paper body. A Normalizer heuristic output, " + "not Marker-observed." + ) + ) + + @field_validator("id") + @classmethod + def _check_id_shape(cls, value: str) -> str: + return validate_object_id_shape(value) \ No newline at end of file diff --git a/src/betydb_extraction/document/page_furniture.py b/src/betydb_extraction/document/page_furniture.py new file mode 100644 index 0000000..efa56ba --- /dev/null +++ b/src/betydb_extraction/document/page_furniture.py @@ -0,0 +1,39 @@ +"""The PageHeader and PageFooter models. +""" +from __future__ import annotations +from typing import Literal +from pydantic import BaseModel, ConfigDict, Field, field_validator +from betydb_extraction.document.enums import NodeKind +from betydb_extraction.document.identifiers import validate_object_id_shape +from betydb_extraction.document.provenance import StructuralProvenance +__all__ = ["PageFooter", "PageHeader"] +class PageHeader(BaseModel): + model_config = ConfigDict(frozen=True, extra="forbid") + kind: Literal[NodeKind.PAGE_HEADER] = Field( + default=NodeKind.PAGE_HEADER, + description="Discriminator for Page children unions.", + ) + id: str = Field(description="Deterministic identifier, per Spec Section 2.") + provenance: StructuralProvenance = Field( + description="marker_block_ids = [the PageHeader block's id]." + ) + raw_text: str = Field(description="Verbatim content.") + @field_validator("id") + @classmethod + def _check_id_shape(cls, value: str) -> str: + return validate_object_id_shape(value) +class PageFooter(BaseModel): + model_config = ConfigDict(frozen=True, extra="forbid") + kind: Literal[NodeKind.PAGE_FOOTER] = Field( + default=NodeKind.PAGE_FOOTER, + description="Discriminator for Page children unions.", + ) + id: str = Field(description="Deterministic identifier, per Spec Section 2.") + provenance: StructuralProvenance = Field( + description="marker_block_ids = [the PageFooter block's id]." + ) + raw_text: str = Field(description="Verbatim content.") + @field_validator("id") + @classmethod + def _check_id_shape(cls, value: str) -> str: + return validate_object_id_shape(value) \ No newline at end of file diff --git a/src/betydb_extraction/document/paragraph.py b/src/betydb_extraction/document/paragraph.py new file mode 100644 index 0000000..4263706 --- /dev/null +++ b/src/betydb_extraction/document/paragraph.py @@ -0,0 +1,41 @@ +"""The Paragraph model. + +Implements the Document Schema Specification, Section 10 ("Paragraph"): +a single block of body text, the most common leaf content type. +""" + +from __future__ import annotations + +from typing import Literal + +from pydantic import BaseModel, ConfigDict, Field, field_validator + +from betydb_extraction.document.enums import NodeKind +from betydb_extraction.document.identifiers import validate_object_id_shape +from betydb_extraction.document.provenance import StructuralProvenance + +__all__ = ["Paragraph"] + + +class Paragraph(BaseModel): + model_config = ConfigDict(frozen=True, extra="forbid") + + kind: Literal[NodeKind.PARAGRAPH] = Field( + default=NodeKind.PARAGRAPH, + description="Discriminator for Page/Section children unions.", + ) + id: str = Field(description="Deterministic identifier, per Spec Section 2.") + text: str = Field( + description=( + "The block's inline HTML content from Marker, as-is, including " + "any inline markup tags." + ) + ) + provenance: StructuralProvenance = Field( + description="marker_block_ids = [the originating Text/ListItem block's id]." + ) + + @field_validator("id") + @classmethod + def _check_id_shape(cls, value: str) -> str: + return validate_object_id_shape(value) \ No newline at end of file diff --git a/src/betydb_extraction/document/processing_metadata.py b/src/betydb_extraction/document/processing_metadata.py new file mode 100644 index 0000000..5bc56d9 --- /dev/null +++ b/src/betydb_extraction/document/processing_metadata.py @@ -0,0 +1,59 @@ +"""The ProcessingMetadata model. +""" +from __future__ import annotations + +from datetime import datetime, timezone + +from pydantic import BaseModel, ConfigDict, Field, field_validator + +__all__ = ["ProcessingMetadata"] + + +class ProcessingMetadata(BaseModel): + model_config = ConfigDict(frozen=True, extra="forbid") + + marker_version: str = Field( + description=( + "Verbatim from Marker's own output metadata, if present; " + "otherwise the version string of the Marker invocation recorded " + "by the adapter." + ) + ) + normalizer_version: str = Field( + description=( + "Semantic version of the Normalizer code that produced this " + "Document Object. Required so a future schema/logic change is " + "always attributable." + ) + ) + processed_at: datetime = Field( + description=( + "Wall-clock time of this materialization, ISO 8601 UTC. " + "Explicitly not part of id computation (Section 2) -- recorded " + "for audit/debugging only." + ) + ) + source_marker_artifact_ref: str = Field( + description=( + "A path or content hash identifying the exact Raw Marker Model " + "JSON file this Document Object was normalized from, satisfying " + "the 'Document has no own provenance' note in Section 4 by " + "pointing at the file-level artifact instead of a block-level " + "one." + ) + ) + + @field_validator("processed_at") + @classmethod + def _check_processed_at_is_utc(cls, value: datetime) -> datetime: + if value.tzinfo is None: + raise ValueError( + "ProcessingMetadata.processed_at must be timezone-aware " + "(ISO 8601 UTC per Spec Section 19); got a naive datetime." + ) + if value.utcoffset() != timezone.utc.utcoffset(None): + raise ValueError( + "ProcessingMetadata.processed_at must be UTC per Spec " + f"Section 19; got offset {value.utcoffset()}." + ) + return value \ No newline at end of file diff --git a/src/betydb_extraction/document/provenance.py b/src/betydb_extraction/document/provenance.py new file mode 100644 index 0000000..3210fda --- /dev/null +++ b/src/betydb_extraction/document/provenance.py @@ -0,0 +1,116 @@ +"""Foundational supporting value objects: geometry and provenance. + +Implements the Document Schema Specification, Section 3 ("Foundational +Supporting Types"): ``BoundingBox`` (3.1), ``Polygon`` (3.2), and +``StructuralProvenance`` (3.3). +""" + +from __future__ import annotations + +from pydantic import BaseModel, ConfigDict, Field, model_validator + +__all__ = ["BoundingBox", "Polygon", "StructuralProvenance"] + + +class BoundingBox(BaseModel): + model_config = ConfigDict(frozen=True, extra="forbid") + + x0: float = Field(description="Left edge.") + y0: float = Field(description="Top edge.") + x1: float = Field(description="Right edge.") + y1: float = Field(description="Bottom edge.") + + @model_validator(mode="after") + def _check_box_is_not_inverted(self) -> "BoundingBox": + if self.x1 < self.x0: + raise ValueError( + f"BoundingBox is inverted on the x-axis: x1={self.x1} < x0={self.x0}" + ) + if self.y1 < self.y0: + raise ValueError( + f"BoundingBox is inverted on the y-axis: y1={self.y1} < y0={self.y0}" + ) + return self + + +class Polygon(BaseModel): + + model_config = ConfigDict(frozen=True, extra="forbid") + + points: tuple[ + tuple[float, float], + tuple[float, float], + tuple[float, float], + tuple[float, float], + ] = Field(description="Exactly four (x, y) corner points, as emitted by Marker.") + + +class StructuralProvenance(BaseModel): + + model_config = ConfigDict(frozen=True, extra="forbid") + + marker_block_ids: list[str] = Field( + min_length=1, + description=( + "The originating Marker block id(s), e.g. ['/page/7/Table/2']. " + "A list rather than a single value because some Document objects " + "(e.g. a normalized Caption under Pattern B) are synthesized from " + "more than one Marker block." + ), + ) + page_number: int = Field( + description=( + "The PDF page this object originates from. For objects synthesized " + "from multiple blocks, the page of the primary/first contributing " + "block." + ) + ) + bbox: BoundingBox | None = Field( + default=None, + description=( + "Present for an object with a single, well-defined originating " + "region. Mutually exclusive with contributing_bboxes." + ), + ) + contributing_bboxes: list[BoundingBox] | None = Field( + default=None, + description=( + "Used instead of bbox when more than one Marker block contributes " + "geometry, preserving each box rather than collapsing them into a " + "single misleading region. Mutually exclusive with bbox." + ), + ) + polygon: Polygon | None = Field( + default=None, + description="Mirrors bbox's optionality logic.", + ) + reading_order_index: int = Field( + ge=0, + description=( + "The object's position in the document's global linear reading " + "order (Spec Section 3.4), recomputed by the Normalizer from final " + "tree position -- never copied from a Marker id's trailing index " + "number, which was empirically confirmed non-monotonic with true " + "reading order." + ), + ) + section_path: list[str] = Field( + default_factory=list, + description=( + "The chain of governing SectionHeader Marker-block ids, ordered " + "outermost to innermost, derived from Marker's own " + "section_hierarchy map (Spec Section 3.5). Empty only for objects " + "outside any section (e.g. a journal wrapper page's Picture)." + ), + ) + + @model_validator(mode="after") + def _check_bbox_xor_contributing_bboxes(self) -> "StructuralProvenance": + if self.bbox is not None and self.contributing_bboxes is not None: + raise ValueError( + "StructuralProvenance may not set both 'bbox' and " + "'contributing_bboxes' -- exactly one geometric claim about " + "this object's origin is permitted, or neither when no " + "recoverable geometry exists." + ) + return self \ No newline at end of file diff --git a/src/betydb_extraction/document/reference.py b/src/betydb_extraction/document/reference.py new file mode 100644 index 0000000..4871410 --- /dev/null +++ b/src/betydb_extraction/document/reference.py @@ -0,0 +1,51 @@ +"""The Reference model. + +Implements the Document Schema Specification v1.1, Section 16 +("Reference (Bibliography Entry)"). +""" + +from __future__ import annotations + +from typing import Literal + +from pydantic import BaseModel, ConfigDict, Field, field_validator + +from betydb_extraction.document.enums import NodeKind +from betydb_extraction.document.identifiers import validate_object_id_shape +from betydb_extraction.document.provenance import StructuralProvenance + +__all__ = ["Reference"] + + +class Reference(BaseModel): + """One bibliography entry.""" + + model_config = ConfigDict( + frozen=True, + extra="forbid", + ) + + kind: Literal[NodeKind.REFERENCE] = Field( + default=NodeKind.REFERENCE, + description="Discriminator for Section.children.", + ) + + id: str = Field( + description="Deterministic identifier, per Spec Section 2." + ) + + provenance: StructuralProvenance = Field( + description="marker_block_ids = [the ListItem block's id]." + ) + + raw_text: str = Field( + description=( + "Verbatim reference entry text, including any inline markup " + "Marker preserved." + ) + ) + + @field_validator("id") + @classmethod + def _check_id_shape(cls, value: str) -> str: + return validate_object_id_shape(value) \ No newline at end of file diff --git a/src/betydb_extraction/document/section.py b/src/betydb_extraction/document/section.py new file mode 100644 index 0000000..7d6aa6e --- /dev/null +++ b/src/betydb_extraction/document/section.py @@ -0,0 +1,75 @@ +"""The Section model. + +Implements the Document Schema Specification, Section 9 ("Section") +""" + +from __future__ import annotations + +from typing import Annotated, Literal, Union + +from pydantic import BaseModel, ConfigDict, Field, field_validator + +from betydb_extraction.document.enums import NodeKind +from betydb_extraction.document.equation import Equation +from betydb_extraction.document.figure import Figure +from betydb_extraction.document.footnote import Footnote +from betydb_extraction.document.identifiers import validate_object_id_shape +from betydb_extraction.document.paragraph import Paragraph +from betydb_extraction.document.provenance import StructuralProvenance +from betydb_extraction.document.table import Table +from betydb_extraction.document.reference import Reference + +__all__ = ["Section", "SectionChild"] + + +class Section(BaseModel): + + model_config = ConfigDict(frozen=True, extra="forbid") + + kind: Literal[NodeKind.SECTION] = Field( + default=NodeKind.SECTION, + description="Discriminator for Page/Section children unions.", + ) + id: str = Field(description="Deterministic identifier, per Spec Section 2.") + heading_text: str = Field( + description="Verbatim text of the governing SectionHeader block." + ) + provenance: StructuralProvenance = Field( + description="marker_block_ids = [the SectionHeader block's id]." + ) + depth: int = Field( + ge=0, + description=( + "Position of this section's heading in the ordered section_path " + "list, zero-indexed from the outermost heading on the " + "page/document." + ), + ) + children: list["SectionChild"] = Field( + default_factory=list, + description=( + "Nested sub-sections and content governed by this heading, in " + "reading order." + ), + ) + + @field_validator("id") + @classmethod + def _check_id_shape(cls, value: str) -> str: + return validate_object_id_shape(value) + + +SectionChild = Annotated[ + Union[ + Section, + Paragraph, + Table, + Figure, + Equation, + Footnote, + Reference, + ], + Field(discriminator="kind"), +] + +Section.model_rebuild() \ No newline at end of file diff --git a/src/betydb_extraction/document/statistics.py b/src/betydb_extraction/document/statistics.py new file mode 100644 index 0000000..24dea04 --- /dev/null +++ b/src/betydb_extraction/document/statistics.py @@ -0,0 +1,47 @@ +from __future__ import annotations + +from pydantic import BaseModel, ConfigDict, Field, model_validator + +__all__ = ["Statistics"] + + +class Statistics(BaseModel): + + model_config = ConfigDict(frozen=True, extra="forbid") + + page_count: int = Field(ge=0, description="len(pages).") + section_count: int = Field( + ge=0, description="Total Section objects across the document." + ) + paragraph_count: int = Field(ge=0, description="Total Paragraph objects.") + table_count: int = Field(ge=0, description="Total Table objects.") + figure_count: int = Field(ge=0, description="Total Figure objects.") + equation_count: int = Field(ge=0, description="Total Equation objects.") + footnote_count: int = Field(ge=0, description="Total Footnote objects.") + reference_count: int = Field( + ge=0, + description=( + "Total Reference objects. As of Version 1.1, Reference objects " + "are reachable as Section.children members under a References " + "Section, so this is a true traversal count." + ), + ) + unresolved_footnote_count: int = Field( + ge=0, + description=( + "Footnotes whose attached_object_id is None after Normalizer " + "processing -- a direct, queryable signal of how much of the " + "geometric-attachment heuristic (empirical finding 3.2) " + "succeeded on this paper." + ), + ) + + @model_validator(mode="after") + def _check_unresolved_does_not_exceed_total(self) -> "Statistics": + if self.unresolved_footnote_count > self.footnote_count: + raise ValueError( + "Statistics.unresolved_footnote_count " + f"({self.unresolved_footnote_count}) cannot exceed " + f"footnote_count ({self.footnote_count})." + ) + return self \ No newline at end of file diff --git a/src/betydb_extraction/document/table.py b/src/betydb_extraction/document/table.py new file mode 100644 index 0000000..ddd7b8e --- /dev/null +++ b/src/betydb_extraction/document/table.py @@ -0,0 +1,128 @@ +from __future__ import annotations + +from typing import Literal + +from pydantic import BaseModel, ConfigDict, Field, field_validator + +from betydb_extraction.document.caption import Caption +from betydb_extraction.document.enums import NodeKind +from betydb_extraction.document.identifiers import validate_object_id_shape +from betydb_extraction.document.provenance import ( + BoundingBox, + Polygon, + StructuralProvenance, +) + +__all__ = ["Table", "TableCell", "TableRow", "TableRowCell"] + + +class TableRowCell(BaseModel): + model_config = ConfigDict(frozen=True, extra="forbid") + + text: str = Field( + description=( + "Cell text content from the parsed / element, with any " + " wrapper tag stripped and its content treated as plain text." + ) + ) + is_header: bool = Field( + description="True if the source element was , false for ." + ) + structural_notes: str | None = Field( + default=None, + description=( + "A free-text slot reserved for a Normalizer-attached structural " + "annotation, most notably a suspected merged-cell placeholder " + "(Marker silently flattens merged header cells into duplicated " + "rows with an empty filler cell, with no flag distinguishing this " + "from a genuinely empty cell). The heuristic for populating this " + "field is explicitly not decided by the specification -- it is an " + "open slot reserved so that decision can be made later without a " + "schema change." + ), + ) + + +class TableRow(BaseModel): + model_config = ConfigDict(frozen=True, extra="forbid") + + cells: list[TableRowCell] = Field( + min_length=1, + description="Ordered left to right per the source
    element.", + ) + + +class TableCell(BaseModel): + + model_config = ConfigDict(frozen=True, extra="forbid") + + id: str = Field(description="Deterministic identifier, per Spec Section 2.") + text: str = Field(description="Verbatim Marker TableCell content.") + bbox: BoundingBox = Field(description="Per-cell geometry.") + polygon: Polygon = Field(description="Mirrors bbox.") + + @field_validator("id") + @classmethod + def _check_id_shape(cls, value: str) -> str: + return validate_object_id_shape(value) + + +class Table(BaseModel): + + model_config = ConfigDict(frozen=True, extra="forbid") + + kind: Literal[NodeKind.TABLE] = Field( + default=NodeKind.TABLE, + description="Discriminator for Page/Section children unions.", + ) + id: str = Field(description="Deterministic identifier, per Spec Section 2.") + provenance: StructuralProvenance = Field( + description=( + "marker_block_ids = [the Table block's id] (and the TableGroup " + "id too, if Pattern A)." + ) + ) + caption: Caption | None = Field( + default=None, + description=( + "None only if no caption-bearing blocks were found adjacent to " + "the table at all -- not empirically observed in the " + "representative paper, but not assumed impossible." + ), + ) + raw_html: str = Field( + description=( + "The Table block's own html field, verbatim -- the complete, " + "correctly-nested
    ...
    Marker produces. The source " + "of truth for logical structure." + ) + ) + rows: list[TableRow] = Field( + default_factory=list, + description=( + "A structured parse of raw_html's elements into row " + "objects, derived from raw_html, not an independent " + "reconstruction. May be empty." + ), + ) + cells: list[TableCell] = Field( + default_factory=list, + description=( + "The flat list of Marker TableCell child blocks, retained only " + "as evidence/geometry data. May be empty." + ), + ) + footnote_ids: list[str] = Field( + default_factory=list, + description=( + "Ids of Footnote objects geometrically attached to this table. " + "Empty until the Normalizer's bbox-proximity heuristic runs; the " + "field exists now so that heuristic's output has a defined home " + "without a later schema change." + ), + ) + + @field_validator("id") + @classmethod + def _check_id_shape(cls, value: str) -> str: + return validate_object_id_shape(value) \ No newline at end of file diff --git a/src/betydb_extraction/marker_adapter/__init__.py b/src/betydb_extraction/marker_adapter/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/betydb_extraction/marker_adapter/raw_model.py b/src/betydb_extraction/marker_adapter/raw_model.py new file mode 100644 index 0000000..c748ed6 --- /dev/null +++ b/src/betydb_extraction/marker_adapter/raw_model.py @@ -0,0 +1,221 @@ +from __future__ import annotations + +from typing import Any, Optional + +from pydantic import BaseModel, ConfigDict, Field, field_validator + + +class MarkerPolygonPoint(BaseModel): + model_config = ConfigDict(frozen=True, extra="forbid") + + x: float + y: float + + @classmethod + def from_pair(cls, pair: "list[float] | tuple[float, float]") -> "MarkerPolygonPoint": + """Construct from Marker's raw ``[x, y]`` list/tuple representation.""" + x, y = pair + return cls(x=x, y=y) + + def to_pair(self) -> list[float]: + """Serialize back to Marker's raw ``[x, y]`` list representation.""" + return [self.x, self.y] + + +class MarkerBBox(BaseModel): + model_config = ConfigDict(frozen=True, extra="forbid") + + x0: float + y0: float + x1: float + y1: float + + @classmethod + def from_list(cls, values: "list[float]") -> "MarkerBBox": + """Construct from Marker's raw ``[x0, y0, x1, y1]`` list representation.""" + x0, y0, x1, y1 = values + return cls(x0=x0, y0=y0, x1=x1, y1=y1) + + def to_list(self) -> list[float]: + """Serialize back to Marker's raw ``[x0, y0, x1, y1]`` list representation.""" + return [self.x0, self.y0, self.x1, self.y1] + + +class MarkerBlock(BaseModel): + + model_config = ConfigDict( + frozen=True, + # Marker's schema may evolve. Unknown fields are preserved rather + # than silently dropped, so that upgrading Marker never causes + # silent data loss even before this model is updated to formally + # recognize a new field. See module docstring, point 4. + extra="allow", + ) + + id: Optional[str] = Field( + default=None, + description=( + "Marker's own block identifier, e.g. '/page/7/Table/2'. This is " + "a path-like string encoding page index, block_type, and a " + "local positional index. It is preserved verbatim and is NOT " + "guaranteed to be globally stable across different Marker " + "versions or runs -- treat it as provenance to the specific " + "Marker invocation that produced this tree, not as a permanent " + "cross-run identifier. Permanent identifiers are derived later, " + "in the Document Object layer. Modeled as Optional because the " + "root 'Document' block in observed Marker output omits this " + "field entirely (along with html, polygon, bbox, and " + "section_hierarchy) -- it is structurally a bare wrapper " + "around the page children and carries no positional identity " + "of its own." + ), + ) + + block_type: str = Field( + ..., + description=( + "Marker's block type tag, e.g. 'Page', 'Table', 'TableCell', " + "'Text', 'SectionHeader', 'Footnote', 'Caption', 'Figure', " + "'FigureGroup', 'TableGroup', 'Picture', 'ListGroup', " + "'ListItem', 'PageHeader', 'PageFooter', 'Equation', " + "'Document'. Modeled as a plain ``str`` rather than an ``Enum`` " + "or discriminator so that an unrecognized block_type from a " + "future Marker version still parses successfully." + ), + ) + + html: str = Field( + default="", + description=( + "For leaf blocks: the actual inline HTML content of this block " + "(e.g. '

    ...

    '). For container blocks: " + "a manifest of '' " + "pointers to this block's children, in reading order -- in " + "that case this field is redundant with `children` and should " + "be treated as a reading-order hint only, not primary content. " + "Distinguishing these two cases is a Normalizer responsibility " + "(in practice: `children is None` implies the html is real " + "leaf content; `children is not None` implies it is a " + "content-ref manifest), not something this model decides." + ), + ) + + polygon: Optional[list[MarkerPolygonPoint]] = Field( + default=None, + description=( + "The block's bounding polygon as a list of corner points, in " + "Marker's native page coordinate space. Observed in practice as " + "4 points (a rectangle) but modeled as a list of arbitrary " + "length since Marker's polygon format is not contractually " + "limited to 4 points." + ), + ) + + bbox: Optional[MarkerBBox] = Field( + default=None, + description=( + "The block's axis-aligned bounding box [x0, y0, x1, y1] in " + "Marker's native page coordinate space." + ), + ) + + children: Optional[list["MarkerBlock"]] = Field( + default=None, + description=( + "Nested child blocks, in reading order, or `None` for a true " + "leaf block. `None` and `[]` are deliberately NOT collapsed " + "into one representation -- in observed Marker output, leaf " + "blocks have `children: None`, never `children: []`; preserving " + "this distinction exactly as Marker emits it is part of this " + "layer's lossless mandate." + ), + ) + + section_hierarchy: dict[str, str] = Field( + default_factory=dict, + description=( + "A mapping from depth-index string (e.g. '1', '4') to the " + "Marker block id of the governing SectionHeader at that depth, " + "as emitted by Marker for this specific block. This is a live " + "breadcrumb of the heading path above this block at the time " + "Marker produced the tree." + ), + ) + + images: Optional[dict[str, str]] = Field( + default=None, + description=( + "A mapping from (typically this block's own) Marker id to a " + "base64-encoded image payload. Populated for blocks that embed " + "raster image data (observed: 'Picture' blocks); `{}` for the " + "large majority of blocks that carry no image data. Modeled as " + "Optional rather than defaulting to `{}` because Marker's own " + "output for this field was observed to vary between `{}` and " + "(for Page-level container blocks) `None` is not ruled out by " + "the schema even though `{}` was the only empty case directly " + "observed in this paper's output -- see point 3 in the module " + "docstring on not collapsing absent vs. empty." + ), + ) + + @field_validator("polygon", mode="before") + @classmethod + def _coerce_polygon(cls, value: Any) -> Any: + if value is None: + return value + coerced = [] + for point in value: + if isinstance(point, MarkerPolygonPoint): + coerced.append(point) + elif isinstance(point, dict): + coerced.append(point) + else: + # Raw [x, y] list/tuple as emitted by Marker. + coerced.append(MarkerPolygonPoint.from_pair(point)) + return coerced + + @field_validator("bbox", mode="before") + @classmethod + def _coerce_bbox(cls, value: Any) -> Any: + if value is None: + return value + if isinstance(value, MarkerBBox) or isinstance(value, dict): + return value + return MarkerBBox.from_list(value) + + def is_leaf(self) -> bool: + return self.children is None + + def iter_descendants(self) -> "list[MarkerBlock]": + result: list[MarkerBlock] = [] + for child in self.children or []: + result.append(child) + result.extend(child.iter_descendants()) + return result + + +class MarkerDocument(BaseModel): + + model_config = ConfigDict(frozen=True, extra="allow") + + root: MarkerBlock = Field( + ..., + description=( + "The root block of Marker's output tree for this document " + "(observed block_type: 'Document')." + ), + ) + + source_marker_json_path: Optional[str] = Field( + default=None, + description=( + "Optional filesystem path or identifier of the raw Marker JSON " + "file this object was parsed from, retained purely for " + "debugging and provenance traceability. Not part of Marker's " + "own output -- populated by the adapter at parse time." + ), + ) + + @property + def pages(self) -> list[MarkerBlock]: + return self.root.children or [] diff --git a/src/betydb_extraction/normalizer/__init__.py b/src/betydb_extraction/normalizer/__init__.py new file mode 100644 index 0000000..6bedc11 --- /dev/null +++ b/src/betydb_extraction/normalizer/__init__.py @@ -0,0 +1,3 @@ +from .api import normalize + +__all__ = ["normalize"] \ No newline at end of file diff --git a/src/betydb_extraction/normalizer/api.py b/src/betydb_extraction/normalizer/api.py new file mode 100644 index 0000000..0929009 --- /dev/null +++ b/src/betydb_extraction/normalizer/api.py @@ -0,0 +1,113 @@ +""" +Public API for the Normalizer. +""" +from __future__ import annotations + +from betydb_extraction.document.document import Document +from betydb_extraction.marker_adapter.raw_model import MarkerDocument +from betydb_extraction.normalizer.context import NormalizerProcessingContext +from betydb_extraction.normalizer.internal.stage0 import detect_front_matter +from betydb_extraction.normalizer.internal.stage1 import build_page_shells +from betydb_extraction.normalizer.internal.stage1_5 import unwrap_page +from betydb_extraction.normalizer.internal.stage2 import classify_blocks +from betydb_extraction.normalizer.internal.stage3 import build_leaf_builders +from betydb_extraction.normalizer.internal.stage4 import resolve_captions +from betydb_extraction.normalizer.internal.stage5 import build_table_structure +from betydb_extraction.normalizer.internal.stage6 import attach_footnotes +from betydb_extraction.normalizer.internal.stage7 import assemble_section_tree +from betydb_extraction.normalizer.internal.stage8 import assign_reading_order +from betydb_extraction.normalizer.internal.stage9 import compute_canonical_paths +from betydb_extraction.normalizer.internal.stage10 import materialize +from betydb_extraction.normalizer.internal.stage11 import validate_whole_tree +from betydb_extraction.normalizer.internal.stage10 import ( + _compute_document_id, + _compute_object_id, +) + + +def normalize( + marker_document: MarkerDocument, + source_pdf_identifier: str, + processing_context: NormalizerProcessingContext, +) -> Document: + page_blocks = marker_document.pages # list[MarkerBlock], one per page + front_matter_flags: dict[int, bool] = detect_front_matter(page_blocks) + page_builders = build_page_shells(page_blocks, front_matter_flags) + classified_sequences = [] + flat_builders_per_page = [] + shared_heading_registry: dict = {} + + for page_index, page_block in enumerate(page_blocks): + page_builder = page_builders[page_index] + + # Stage 1.5 — Wrapper unwrapping + unwrapped = unwrap_page(page_block.children or []) + + # Stage 2 — Block classification + classified = classify_blocks(unwrapped, page_index, shared_heading_registry) + classified_sequences.append(classified) + + # Stage 3 — Leaf builder construction + flat_builders = build_leaf_builders( + classified, page_number=page_index + ) + flat_builders_per_page.append(flat_builders) + + # Stage 4 — Caption resolution + resolve_captions(flat_builders, classified, page_index) + + # Stage 5 — Table internal structure + build_table_structure(flat_builders, classified) + + # Stage 6 — Footnote attachment + attach_footnotes(flat_builders, classified, page_index) + page_builder.children = flat_builders + + # Stage 7 — Section tree assembly (whole document) + + assemble_section_tree( + page_builders=page_builders, + classified_pages=classified_sequences, + ) + + # Stage 8 — Global reading-order assignment (whole document) + assign_reading_order(page_builders) + + # Stage 9 — Canonical path computation (whole document) + compute_canonical_paths(page_builders) + + # Stage 10 discards the builder tree. + stage3_expected_ids: set[str] = _collect_stage3_expected_ids( + flat_builders_per_page, source_pdf_identifier + ) + + # Stage 10 — Materialization + document: Document = materialize( + page_builders=page_builders, + source_pdf_identifier=source_pdf_identifier, + processing_context=processing_context, + classified_pages=classified_sequences, + ) + + # Stage 11 — Whole-tree validation + validate_whole_tree( + document=document, + stage3_expected_ids=stage3_expected_ids, + ) + + return document + + +# Internal helper — not part of the public contract +def _collect_stage3_expected_ids( + flat_builders_per_page: list[list], + source_pdf_identifier: str, +) -> set[str]: + document_id = _compute_document_id(source_pdf_identifier) + ids: set[str] = set() + for page_flat in flat_builders_per_page: + for builder in page_flat: + cp = getattr(builder, "canonical_path", None) + if cp is not None: + ids.add(_compute_object_id(document_id, cp)) + return ids \ No newline at end of file diff --git a/src/betydb_extraction/normalizer/builders/__init__.py b/src/betydb_extraction/normalizer/builders/__init__.py new file mode 100644 index 0000000..4b39d6d --- /dev/null +++ b/src/betydb_extraction/normalizer/builders/__init__.py @@ -0,0 +1,56 @@ +""" +Builder layer public surface. +""" +from betydb_extraction.normalizer.builders.base import ( + ClassifiedBlock, + Disposition, + UnwrappedBlock, + WrapperContext, +) +from betydb_extraction.normalizer.builders.caption import CaptionBuilder +from betydb_extraction.normalizer.builders.equation import EquationBuilder +from betydb_extraction.normalizer.builders.figure import FigureBuilder +from betydb_extraction.normalizer.builders.footnote import FootnoteBuilder +from betydb_extraction.normalizer.builders.page import PageBuilder +from betydb_extraction.normalizer.builders.page_footer import PageFooterBuilder +from betydb_extraction.normalizer.builders.page_header import PageHeaderBuilder +from betydb_extraction.normalizer.builders.paragraph import ParagraphBuilder +from betydb_extraction.normalizer.builders.provenance import ProvenanceBuilder +from betydb_extraction.normalizer.builders.reference import ReferenceBuilder +from betydb_extraction.normalizer.builders.section import SectionBuilder +from betydb_extraction.normalizer.builders.table import ( + TableBuilder, + TableCellBuilder, + TableRowBuilder, + TableRowCellBuilder, +) + +__all__ = [ + # base + "WrapperContext", + "UnwrappedBlock", + "Disposition", + "ClassifiedBlock", + # provenance + "ProvenanceBuilder", + # page + "PageBuilder", + # section + "SectionBuilder", + # content leaves + "ParagraphBuilder", + "EquationBuilder", + "FootnoteBuilder", + "ReferenceBuilder", + "PageHeaderBuilder", + "PageFooterBuilder", + # caption + "CaptionBuilder", + # table + "TableBuilder", + "TableRowBuilder", + "TableRowCellBuilder", + "TableCellBuilder", + # figure + "FigureBuilder", +] \ No newline at end of file diff --git a/src/betydb_extraction/normalizer/builders/base.py b/src/betydb_extraction/normalizer/builders/base.py new file mode 100644 index 0000000..e7cb315 --- /dev/null +++ b/src/betydb_extraction/normalizer/builders/base.py @@ -0,0 +1,59 @@ +from __future__ import annotations + +from dataclasses import dataclass +from enum import Enum +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from betydb_extraction.marker_adapter.raw_model import MarkerBlock # adjust path + +# ── Wrapper unwrapping types (Stage 1.5) ───────────────────────────────────── + +@dataclass(frozen=True) +class WrapperContext: + wrapper_type: str + block_id: str + +@dataclass(frozen=True) +class UnwrappedBlock: + """ + A single Marker block after Stage 1.5 flattening, paired with its + wrapper provenance (None if it was a direct page child). + """ + block: "MarkerBlock" + wrapper_context: WrapperContext | None + + +# ── Disposition enum (Stage 2) ──────────────────────────────────────────────── + +class Disposition(str, Enum): + """ + Classification tags assigned by Stage 2. + + Every value maps to exactly one processing path in Stages 3–7. + The complete mapping is the Stage 2 classification table in spec §5. + """ + GENUINE_SECTION_HEADER = "GENUINE_SECTION_HEADER" + CAPTION_LABEL = "CAPTION_LABEL" + BODY_PARAGRAPH = "BODY_PARAGRAPH" + REFERENCE_ENTRY = "REFERENCE_ENTRY" + TABLE_SHELL = "TABLE_SHELL" + FIGURE_SHELL = "FIGURE_SHELL" + CAPTION_TEXT = "CAPTION_TEXT" + TABLE_CELL_EVIDENCE = "TABLE_CELL_EVIDENCE" + EQUATION = "EQUATION" + FOOTNOTE = "FOOTNOTE" + PAGE_HEADER = "PAGE_HEADER" + PAGE_FOOTER = "PAGE_FOOTER" + PICTURE = "PICTURE" + + + +@dataclass(frozen=True) +class ClassifiedBlock: + """ + An UnwrappedBlock paired with its Stage 2 disposition tag. + All stages after Stage 2 work against lists of ClassifiedBlocks. + """ + unwrapped: UnwrappedBlock + disposition: Disposition \ No newline at end of file diff --git a/src/betydb_extraction/normalizer/builders/caption.py b/src/betydb_extraction/normalizer/builders/caption.py new file mode 100644 index 0000000..d358217 --- /dev/null +++ b/src/betydb_extraction/normalizer/builders/caption.py @@ -0,0 +1,17 @@ +"""CaptionBuilder — mutable accumulator for Schema v1.1 Caption.""" +from __future__ import annotations + +from dataclasses import dataclass, field + +from betydb_extraction.normalizer.builders.provenance import ProvenanceBuilder + + +@dataclass +class CaptionBuilder: + # kind is set at Stage 4 (not Stage 3), per spec §4.1 kind field rule. + kind: str | None = None + label: str | None = None + text: str | None = None + trailing_notes: str | None = None + provenance: ProvenanceBuilder = field(default_factory=ProvenanceBuilder) + canonical_path: str | None = None \ No newline at end of file diff --git a/src/betydb_extraction/normalizer/builders/equation.py b/src/betydb_extraction/normalizer/builders/equation.py new file mode 100644 index 0000000..b6da45b --- /dev/null +++ b/src/betydb_extraction/normalizer/builders/equation.py @@ -0,0 +1,15 @@ +"""EquationBuilder — mutable accumulator for Schema v1.1 Equation.""" +from __future__ import annotations + +from dataclasses import dataclass, field + +from betydb_extraction.normalizer.builders.provenance import ProvenanceBuilder + + +@dataclass +class EquationBuilder: + kind: str | None = None + raw_math: str | None = None + equation_number: str | None = None # Always None per spec §17 + provenance: ProvenanceBuilder = field(default_factory=ProvenanceBuilder) + canonical_path: str | None = None \ No newline at end of file diff --git a/src/betydb_extraction/normalizer/builders/figure.py b/src/betydb_extraction/normalizer/builders/figure.py new file mode 100644 index 0000000..a8e07cf --- /dev/null +++ b/src/betydb_extraction/normalizer/builders/figure.py @@ -0,0 +1,17 @@ +"""FigureBuilder — mutable accumulator for Schema v1.1 Figure.""" +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import Any + +from betydb_extraction.normalizer.builders.provenance import ProvenanceBuilder + + +@dataclass +class FigureBuilder: + kind: str | None = None + image_data: Any | None = None # bytes | None; from source block images + caption: Any | None = None # CaptionBuilder | None; Stage 4 + footnote_ids: list[str] = field(default_factory=list) # Stage 6 + provenance: ProvenanceBuilder = field(default_factory=ProvenanceBuilder) + canonical_path: str | None = None diff --git a/src/betydb_extraction/normalizer/builders/footnote.py b/src/betydb_extraction/normalizer/builders/footnote.py new file mode 100644 index 0000000..fc0e729 --- /dev/null +++ b/src/betydb_extraction/normalizer/builders/footnote.py @@ -0,0 +1,17 @@ +"""FootnoteBuilder — mutable accumulator for Schema v1.1 Footnote.""" +from __future__ import annotations + +from dataclasses import dataclass, field + +from betydb_extraction.normalizer.builders.provenance import ProvenanceBuilder + + +@dataclass +class FootnoteBuilder: + kind: str | None = None + raw_text: str | None = None + # Set by Stage 6; left None if no candidate table/figure found. + attached_object_id: str | None = None + provenance: ProvenanceBuilder = field(default_factory=ProvenanceBuilder) + canonical_path: str | None = None + \ No newline at end of file diff --git a/src/betydb_extraction/normalizer/builders/page.py b/src/betydb_extraction/normalizer/builders/page.py new file mode 100644 index 0000000..cb4b341 --- /dev/null +++ b/src/betydb_extraction/normalizer/builders/page.py @@ -0,0 +1,20 @@ +""" +PageBuilder — mutable accumulator for Schema v1.1 Page. +""" +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import Any + +from betydb_extraction.normalizer.builders.provenance import ProvenanceBuilder + + +@dataclass +class PageBuilder: + kind: str | None = None + page_number: int | None = None + is_front_matter: bool | None = None + provenance: ProvenanceBuilder = field(default_factory=ProvenanceBuilder) + children: list[Any] = field(default_factory=list) + canonical_path: str | None = None + \ No newline at end of file diff --git a/src/betydb_extraction/normalizer/builders/page_footer.py b/src/betydb_extraction/normalizer/builders/page_footer.py new file mode 100644 index 0000000..f3cf900 --- /dev/null +++ b/src/betydb_extraction/normalizer/builders/page_footer.py @@ -0,0 +1,15 @@ +"""PageFooterBuilder — mutable accumulator for Schema v1.1 PageFooter.""" +from __future__ import annotations + +from dataclasses import dataclass, field + +from betydb_extraction.normalizer.builders.provenance import ProvenanceBuilder + + +@dataclass +class PageFooterBuilder: + kind: str | None = None + raw_text: str | None = None + provenance: ProvenanceBuilder = field(default_factory=ProvenanceBuilder) + canonical_path: str | None = None + \ No newline at end of file diff --git a/src/betydb_extraction/normalizer/builders/page_header.py b/src/betydb_extraction/normalizer/builders/page_header.py new file mode 100644 index 0000000..311264e --- /dev/null +++ b/src/betydb_extraction/normalizer/builders/page_header.py @@ -0,0 +1,15 @@ +"""PageHeaderBuilder — mutable accumulator for Schema v1.1 PageHeader.""" +from __future__ import annotations + +from dataclasses import dataclass, field + +from betydb_extraction.normalizer.builders.provenance import ProvenanceBuilder + + +@dataclass +class PageHeaderBuilder: + kind: str | None = None + raw_text: str | None = None + provenance: ProvenanceBuilder = field(default_factory=ProvenanceBuilder) + canonical_path: str | None = None + \ No newline at end of file diff --git a/src/betydb_extraction/normalizer/builders/paragraph.py b/src/betydb_extraction/normalizer/builders/paragraph.py new file mode 100644 index 0000000..154f56f --- /dev/null +++ b/src/betydb_extraction/normalizer/builders/paragraph.py @@ -0,0 +1,18 @@ +"""ParagraphBuilder — mutable accumulator for Schema v1.1 Paragraph.""" +from __future__ import annotations + +from dataclasses import dataclass, field + +from betydb_extraction.normalizer.builders.provenance import ProvenanceBuilder + + +@dataclass +class ParagraphBuilder: + # Schema v1.1 discriminated-union literal — set at Stage 3 construction. + kind: str | None = None + text: str | None = None + provenance: ProvenanceBuilder = field(default_factory=ProvenanceBuilder) + # Stage 9 only — not present in Schema v1.1 model. + canonical_path: str | None = None + # Stage 10 only — final frozen id. + \ No newline at end of file diff --git a/src/betydb_extraction/normalizer/builders/provenance.py b/src/betydb_extraction/normalizer/builders/provenance.py new file mode 100644 index 0000000..80c511f --- /dev/null +++ b/src/betydb_extraction/normalizer/builders/provenance.py @@ -0,0 +1,20 @@ +""" +ProvenanceBuilder — mutable accumulator for StructuralProvenance. +""" +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import Any + + +@dataclass +class ProvenanceBuilder: + marker_block_ids: list[str] = field(default_factory=list) + page_number: int | None = None + # BoundingBox and Polygon are frozen leaf value types from the Schema; + # they are stored here directly once constructed (Stage 3 / Stage 4). + bbox: Any | None = None # BoundingBox | None + contributing_bboxes: list[Any] | None = None # list[BoundingBox] | None + polygon: Any | None = None # Polygon | None + section_path: list[str] = field(default_factory=list) + reading_order_index: int | None = None \ No newline at end of file diff --git a/src/betydb_extraction/normalizer/builders/reference.py b/src/betydb_extraction/normalizer/builders/reference.py new file mode 100644 index 0000000..8de0b8d --- /dev/null +++ b/src/betydb_extraction/normalizer/builders/reference.py @@ -0,0 +1,15 @@ +"""ReferenceBuilder — mutable accumulator for Schema v1.1 Reference.""" +from __future__ import annotations + +from dataclasses import dataclass, field + +from betydb_extraction.normalizer.builders.provenance import ProvenanceBuilder + + +@dataclass +class ReferenceBuilder: + kind: str | None = None + raw_text: str | None = None + provenance: ProvenanceBuilder = field(default_factory=ProvenanceBuilder) + canonical_path: str | None = None + \ No newline at end of file diff --git a/src/betydb_extraction/normalizer/builders/section.py b/src/betydb_extraction/normalizer/builders/section.py new file mode 100644 index 0000000..cc4230e --- /dev/null +++ b/src/betydb_extraction/normalizer/builders/section.py @@ -0,0 +1,28 @@ +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import Any + +from betydb_extraction.normalizer.builders.provenance import ProvenanceBuilder + + +@dataclass +class SectionBuilder: + kind: str | None = None + heading_text: str | None = None + # depth and parent are internal assembly aids; not in Schema v1.1. + depth: int | None = None + parent_section_builder: "SectionBuilder | None" = field( + default=None, repr=False, compare=False + ) + children: list[Any] = field(default_factory=list) + # INVARIANT: heading_marker_block_id must always equal + # provenance.marker_block_ids[0]. Both are set together, exactly once, + # at the single call site in Stage 7 Step 1 that constructs this + # SectionBuilder. Never set one without the other, and never mutate + # either independently afterward — Stage 9's canonical_path + # ({page_canonical_path}/section/{heading_marker_block_id}) and this + # builder's provenance must stay derived from the same source id. + heading_marker_block_id: str | None = None + provenance: ProvenanceBuilder = field(default_factory=ProvenanceBuilder) + canonical_path: str | None = None \ No newline at end of file diff --git a/src/betydb_extraction/normalizer/builders/table.py b/src/betydb_extraction/normalizer/builders/table.py new file mode 100644 index 0000000..6356d40 --- /dev/null +++ b/src/betydb_extraction/normalizer/builders/table.py @@ -0,0 +1,54 @@ +""" +Table-related builders. +""" +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import Any + +from betydb_extraction.normalizer.builders.provenance import ProvenanceBuilder + + +@dataclass +class TableRowCellBuilder: + """One or element parsed from raw_html.""" + kind: str | None = None + text: str | None = None + is_header: bool | None = None + structural_notes: str | None = None # Always None per spec §5 Stage 5 + canonical_path: str | None = None + + +@dataclass +class TableRowBuilder: + """One element parsed from raw_html.""" + kind: str | None = None + cells: list[TableRowCellBuilder] = field(default_factory=list) + canonical_path: str | None = None + + +@dataclass +class TableCellBuilder: + kind: str | None = None + marker_block_id: str | None = None + text: str | None = None + bbox: Any | None = None + polygon: Any | None = None + canonical_path: str | None = None + + +@dataclass +class TableBuilder: + kind: str | None = None + raw_html: str | None = None + caption: Any | None = None + rows: list[TableRowBuilder] = field(default_factory=list) + cells: list[TableCellBuilder] = field(default_factory=list) + footnote_ids: list[str] = field(default_factory=list) + provenance: ProvenanceBuilder = field(default_factory=ProvenanceBuilder) + canonical_path: str | None = None + + + + + diff --git a/src/betydb_extraction/normalizer/context.py b/src/betydb_extraction/normalizer/context.py new file mode 100644 index 0000000..3947d25 --- /dev/null +++ b/src/betydb_extraction/normalizer/context.py @@ -0,0 +1,19 @@ +from __future__ import annotations + +from dataclasses import dataclass +from datetime import datetime + + +@dataclass(frozen=True) +class NormalizerProcessingContext: + marker_version: str + """Version string of the Marker run that produced the MarkerDocument.""" + + normalizer_version: str + """Semantic version of this Normalizer code.""" + + source_marker_artifact_ref: str + """Path or content hash identifying the Raw Marker Model JSON file.""" + + processed_at: datetime + """Wall-clock UTC datetime of this materialization. Must be timezone-aware UTC.""" \ No newline at end of file diff --git a/src/betydb_extraction/normalizer/errors.py b/src/betydb_extraction/normalizer/errors.py new file mode 100644 index 0000000..9a61d7c --- /dev/null +++ b/src/betydb_extraction/normalizer/errors.py @@ -0,0 +1,34 @@ +""" +Normalizer exception hierarchy. +""" +from __future__ import annotations + + +class NormalizerError(Exception): + """Base class for all normalizer exceptions.""" + +class UnrecognizedBlockTypeError(NormalizerError): + def __init__(self, block_type: str, marker_block_id: str, page_index: int) -> None: + self.block_type = block_type + self.marker_block_id = marker_block_id + self.page_index = page_index + super().__init__( + f"Unrecognized block_type={block_type!r} " + f"(marker_block_id={marker_block_id!r}, page_index={page_index})" + ) + + +class WholeTreeInvariantViolationError(NormalizerError): + def __init__( + self, + invariant_number: int, + description: str, + offending_object_ids: list[str] | None = None, + ) -> None: + self.invariant_number = invariant_number + self.description = description + self.offending_object_ids: list[str] = offending_object_ids or [] + super().__init__( + f"Invariant {invariant_number} violated: {description} " + f"(offending ids: {self.offending_object_ids})" + ) \ No newline at end of file diff --git a/src/betydb_extraction/normalizer/internal/stage0.py b/src/betydb_extraction/normalizer/internal/stage0.py new file mode 100644 index 0000000..ed41b3f --- /dev/null +++ b/src/betydb_extraction/normalizer/internal/stage0.py @@ -0,0 +1,102 @@ +""" +Stage 0: Front-Matter Detection. +""" +from __future__ import annotations + +import re +from typing import Any + +from betydb_extraction.normalizer.logging_util import log_front_matter_detection + +# ── Signal patterns ─────────────────────────────────────────────────────────── + +_S1_STRING = "Submit your article" + +_S2_RE = re.compile(r"\bISSN\s*[\d\-]{4,10}") +_S3_RE = re.compile(r"Article views:\s*\d+") + + +# ── Helpers ─────────────────────────────────────────────────────────────────── + +def _concatenate_text_blocks(page_block: Any) -> str: + """ + Return the concatenated plain text of all Text-typed blocks in a page's + raw children array (one level deep, since S1/S2/S3 are content signals). + + Uses .html as the text source, which matches how all other stages access + block text content. + """ + parts: list[str] = [] + for child in page_block.children: + if child.block_type == "Text": + parts.append(getattr(child, "html", "") or "") + return " ".join(parts) + + +def _has_section_header(page_block: Any) -> bool: + """ + Return True if any block in the page's raw children array has + block_type == "SectionHeader". + + Checked before Stage 1.5 unwrapping — operates on the raw children array. + Wrapper children are NOT inspected (S4 is a one-level check on direct + page children, per the structural nature of the signal). + """ + for child in page_block.children: + if child.block_type == "SectionHeader": + return True + return False + + +# ── Public entry point ──────────────────────────────────────────────────────── + +def detect_front_matter(page_blocks: list[Any]) -> dict[int, bool]: + """ + Run front-matter detection on every page and return a flags mapping. + + Parameters + ---------- + page_blocks: + marker_document.children — the ordered list of top-level page blocks. + + Returns + ------- + dict[int, bool] + page_index (0-based) → is_front_matter. + Every page index present in the input has an entry in the output. + """ + flags: dict[int, bool] = {} + + for page_index, page_block in enumerate(page_blocks): + text = _concatenate_text_blocks(page_block) + signals_fired: list[str] = [] + + # S1 — fixed string, case-sensitive + if _S1_STRING in text: + signals_fired.append("S1") + + # S2 — ISSN regex + if _S2_RE.search(text): + signals_fired.append("S2") + + # S3 — article views regex + if _S3_RE.search(text): + signals_fired.append("S3") + + # S4 — structural: no SectionHeader present on the page + if not _has_section_header(page_block): + signals_fired.append("S4") + + # Threshold: at least two signals must fire (S4 alone is not enough, + # but two signals including S4 are sufficient). + is_front_matter = len(signals_fired) >= 2 + + log_front_matter_detection( + page_index=page_index, + signals_fired=signals_fired, + is_front_matter=is_front_matter, + ) + + flags[page_index] = is_front_matter + + return flags \ No newline at end of file diff --git a/src/betydb_extraction/normalizer/internal/stage1.py b/src/betydb_extraction/normalizer/internal/stage1.py new file mode 100644 index 0000000..00b9680 --- /dev/null +++ b/src/betydb_extraction/normalizer/internal/stage1.py @@ -0,0 +1,41 @@ +""" +Stage 1: Page Builder Construction. +""" +from __future__ import annotations + +from typing import Any + +from betydb_extraction.normalizer.builders.page import PageBuilder +from betydb_extraction.normalizer.builders.provenance import ProvenanceBuilder + + +def build_page_shells( + page_blocks: list[Any], # list[MarkerBlock] — top-level page nodes + front_matter_flags: dict[int, bool], # Stage 0 output: page_index → bool +) -> list[PageBuilder]: + shells: list[PageBuilder] = [] + + for page_index, page_block in enumerate(page_blocks): + # Spec: bbox from page_block.bbox if present, else None. + bbox = getattr(page_block, "bbox", None) + + provenance = ProvenanceBuilder( + marker_block_ids=[page_block.id], + page_number=page_index, + bbox=bbox, + # polygon: MarkerDocument root page blocks carry no polygon + # (not confirmed empirically — left None per conservative rule). + polygon=None, + ) + + shell = PageBuilder( + page_number=page_index, + is_front_matter=front_matter_flags.get(page_index, False), + provenance=provenance, + # children: empty — Stage 7 populates. + children=[], + ) + + shells.append(shell) + + return shells \ No newline at end of file diff --git a/src/betydb_extraction/normalizer/internal/stage10.py b/src/betydb_extraction/normalizer/internal/stage10.py new file mode 100644 index 0000000..cd32011 --- /dev/null +++ b/src/betydb_extraction/normalizer/internal/stage10.py @@ -0,0 +1,481 @@ +""" +Stage 10: Materialization. +""" + +from __future__ import annotations + +import hashlib +import logging +from typing import Any + +from betydb_extraction.normalizer.builders.page import PageBuilder +from betydb_extraction.normalizer.builders.section import SectionBuilder +from betydb_extraction.normalizer.builders.table import TableBuilder +from betydb_extraction.normalizer.builders.figure import FigureBuilder +from betydb_extraction.normalizer.builders.footnote import FootnoteBuilder +from betydb_extraction.normalizer.context import NormalizerProcessingContext +from betydb_extraction.normalizer.builders.base import Disposition +from betydb_extraction.normalizer.logging_util import log_materialization_complete + +# Schema v1.1 frozen models — imported here only (all other stages work with +# builders exclusively). +from betydb_extraction.document.document import Document +from betydb_extraction.document.page import Page +from betydb_extraction.document.section import Section +from betydb_extraction.document.paragraph import Paragraph +from betydb_extraction.document.table import Table, TableRow, TableRowCell, TableCell +from betydb_extraction.document.figure import Figure +from betydb_extraction.document.caption import Caption +from betydb_extraction.document.equation import Equation +from betydb_extraction.document.footnote import Footnote +from betydb_extraction.document.reference import Reference +from betydb_extraction.document.page_furniture import PageHeader, PageFooter +from betydb_extraction.document.provenance import BoundingBox, Polygon, StructuralProvenance +from betydb_extraction.document.metadata import Metadata +from betydb_extraction.document.statistics import Statistics +from betydb_extraction.document.processing_metadata import ProcessingMetadata + +logger = logging.getLogger(__name__) + +def _convert_bbox(marker_bbox: Any) -> BoundingBox | None: + + if marker_bbox is None: + return None + return BoundingBox( + x0=marker_bbox.x0, y0=marker_bbox.y0, + x1=marker_bbox.x1, y1=marker_bbox.y1, + ) + + +def _convert_polygon(marker_polygon: Any) -> Polygon | None: + + if marker_polygon is None: + return None + points = tuple((p.x, p.y) for p in marker_polygon) + return Polygon(points=points) + +def _sha256_prefix(text: str, length: int = 16) -> str: + return hashlib.sha256(text.encode()).hexdigest()[:length] + + +def _compute_document_id(source_pdf_identifier: str) -> str: + return "betydoc:" + _sha256_prefix(source_pdf_identifier) + + +def _compute_object_id(document_id: str, canonical_path: str) -> str: + return "doc:" + _sha256_prefix(document_id + "|" + canonical_path) + +_REFERENCE_TARGET_TYPES = (SectionBuilder, TableBuilder, FigureBuilder, FootnoteBuilder) + + +def _own_marker_block_id(builder: Any) -> str | None: + if isinstance(builder, SectionBuilder): + return builder.heading_marker_block_id + + mids = getattr(getattr(builder, "provenance", None), "marker_block_ids", None) + if mids: + return mids[0] + return None + + +def _build_marker_id_to_final_id_map( + page_builders: list[PageBuilder], + document_id: str, +) -> dict[str, str]: + mapping: dict[str, str] = {} + + def _register(builder: Any) -> None: + if not isinstance(builder, _REFERENCE_TARGET_TYPES): + return + canonical_path = getattr(builder, "canonical_path", None) + if canonical_path is None: + raise ValueError( + f"Stage 10: {type(builder).__name__} has no canonical_path — " + "Stage 9 must run before Stage 10." + ) + own_id = _own_marker_block_id(builder) + if own_id is None: + raise ValueError( + f"Stage 10: {type(builder).__name__} has no resolvable " + "Marker block id — cannot register as a translation target." + ) + mapping[own_id] = _compute_object_id(document_id, canonical_path) + + def _walk(items: list[Any]) -> None: + for item in items: + _register(item) + if isinstance(item, SectionBuilder): + _walk(item.children) + + for pb in page_builders: + _walk(pb.children) + + return mapping + + +def _translate_reference( + marker_block_id: str, + id_map: dict[str, str], + *, + context: str, +) -> str: + final_id = id_map.get(marker_block_id) + if final_id is None: + raise ValueError( + f"Stage 10: dangling interim reference in {context} — " + f"marker_block_id={marker_block_id!r} does not resolve to any " + "materialized Section/Table/Figure/Footnote in the tree." + ) + return final_id + +def _materialize_provenance( + prov_builder: Any, + document_id: str, + id_map: dict[str, str], +) -> StructuralProvenance: + contributing = prov_builder.contributing_bboxes + translated_section_path = [ + _translate_reference( + heading_marker_id, id_map, context="StructuralProvenance.section_path" + ) + for heading_marker_id in prov_builder.section_path + ] + return StructuralProvenance( + marker_block_ids=list(prov_builder.marker_block_ids), + page_number=prov_builder.page_number, + bbox=_convert_bbox(prov_builder.bbox), + contributing_bboxes=( + [_convert_bbox(b) for b in contributing] if contributing else None + ), + polygon=_convert_polygon(prov_builder.polygon), + section_path=translated_section_path, + reading_order_index=prov_builder.reading_order_index, + ) + + +def _mat_table_row_cell(b: Any, document_id: str) -> TableRowCell: + return TableRowCell( + text=b.text or "", + is_header=b.is_header, + structural_notes=b.structural_notes, + ) + + +def _mat_table_row(b: Any, document_id: str) -> TableRow: + cells = [_mat_table_row_cell(c, document_id) for c in (b.cells or [])] + return TableRow( + cells=cells, + ) + + +def _mat_table_cell(b: Any, document_id: str) -> TableCell: + return TableCell( + id=_compute_object_id(document_id, b.canonical_path), + text=b.text, + bbox=_convert_bbox(b.bbox), + polygon=_convert_polygon(b.polygon), + ) + + +def _mat_caption(b: Any, document_id: str, id_map: dict[str, str]) -> Caption: + return Caption( + label=b.label, + text=b.text, + trailing_notes=b.trailing_notes, + provenance=_materialize_provenance(b.provenance, document_id, id_map), + ) + + +def _mat_paragraph(b: Any, document_id: str, id_map: dict[str, str]) -> Paragraph: + return Paragraph( + kind=b.kind, + id=_compute_object_id(document_id, b.canonical_path), + text=b.text, + provenance=_materialize_provenance(b.provenance, document_id, id_map), + ) + + +def _mat_equation(b: Any, document_id: str, id_map: dict[str, str]) -> Equation: + return Equation( + kind=b.kind, + id=_compute_object_id(document_id, b.canonical_path), + raw_math=b.raw_math, + equation_number=b.equation_number, + provenance=_materialize_provenance(b.provenance, document_id, id_map), + ) + + +def _mat_footnote(b: Any, document_id: str, id_map: dict[str, str]) -> Footnote: + attached_final_id = None + if b.attached_object_id is not None: + attached_final_id = _translate_reference( + b.attached_object_id, id_map, context="Footnote.attached_object_id" + ) + + return Footnote( + kind=b.kind, + id=_compute_object_id(document_id, b.canonical_path), + raw_text=b.raw_text, + attached_object_id=attached_final_id, + provenance=_materialize_provenance(b.provenance, document_id, id_map), + ) + + +def _mat_reference(b: Any, document_id: str, id_map: dict[str, str]) -> Reference: + return Reference( + kind=b.kind, + id=_compute_object_id(document_id, b.canonical_path), + raw_text=b.raw_text, + provenance=_materialize_provenance(b.provenance, document_id, id_map), + ) + + +def _mat_page_header(b: Any, document_id: str, id_map: dict[str, str]) -> PageHeader: + return PageHeader( + kind=b.kind, + id=_compute_object_id(document_id, b.canonical_path), + raw_text=b.raw_text, + provenance=_materialize_provenance(b.provenance, document_id, id_map), + ) + + +def _mat_page_footer(b: Any, document_id: str, id_map: dict[str, str]) -> PageFooter: + return PageFooter( + kind=b.kind, + id=_compute_object_id(document_id, b.canonical_path), + raw_text=b.raw_text, + provenance=_materialize_provenance(b.provenance, document_id, id_map), + ) + + +def _mat_table(b: Any, document_id: str, id_map: dict[str, str]) -> Table: + rows = [_mat_table_row(r, document_id) for r in (b.rows or [])] + cells = [_mat_table_cell(c, document_id) for c in (b.cells or [])] + caption = ( + _mat_caption(b.caption, document_id, id_map) if b.caption is not None else None + ) + footnote_ids = [ + _translate_reference(fid, id_map, context="Table.footnote_ids") + for fid in (b.footnote_ids or []) + ] + return Table( + kind=b.kind, + id=_compute_object_id(document_id, b.canonical_path), + raw_html=b.raw_html, + rows=rows, + cells=cells, + caption=caption, + footnote_ids=footnote_ids, + provenance=_materialize_provenance(b.provenance, document_id, id_map), + ) + + +def _mat_figure(b: Any, document_id: str, id_map: dict[str, str]) -> Figure: + caption = _mat_caption(b.caption, document_id, id_map) if b.caption is not None else None + return Figure( + id=_compute_object_id(document_id, b.canonical_path), + image_data=b.image_data, + caption=caption, + provenance=_materialize_provenance(b.provenance, document_id, id_map), + ) + + +_LEAF_MATERIALIZERS: dict[str, Any] = { + "ParagraphBuilder": _mat_paragraph, + "EquationBuilder": _mat_equation, + "FootnoteBuilder": _mat_footnote, + "ReferenceBuilder": _mat_reference, + "PageHeaderBuilder": _mat_page_header, + "PageFooterBuilder": _mat_page_footer, + "TableBuilder": _mat_table, + "FigureBuilder": _mat_figure, +} + + +def _materialize_leaf(builder: Any, document_id: str, id_map: dict[str, str]) -> Any: + name = type(builder).__name__ + fn = _LEAF_MATERIALIZERS.get(name) + if fn is None: + raise TypeError( + f"stage10: no materializer registered for builder type {name!r}" + ) + return fn(builder, document_id, id_map) + + +def _mat_section_postorder( + sb: SectionBuilder, document_id: str, id_map: dict[str, str] +) -> Section: + """ + Recursively materialize a SectionBuilder post-order (children before + parent). Returns the frozen Section model. + """ + materialized_children: list[Any] = [] + for child in sb.children: + if isinstance(child, SectionBuilder): + materialized_children.append( + _mat_section_postorder(child, document_id, id_map) + ) + else: + materialized_children.append( + _materialize_leaf(child, document_id, id_map) + ) + + return Section( + kind=sb.kind, + id=_compute_object_id(document_id, sb.canonical_path), + heading_text=sb.heading_text, + depth=sb.depth, + children=materialized_children, + provenance=_materialize_provenance(sb.provenance, document_id, id_map), + ) + + +def _count_objects(pages: list[Page]) -> dict[str, int]: + """ + Recursive type-counting traversal of the materialized page list. + Returns a dict of field-name → count matching Statistics field names. + """ + counts: dict[str, int] = { + "page_count": len(pages), + "section_count": 0, + "paragraph_count": 0, + "table_count": 0, + "figure_count": 0, + "equation_count": 0, + "footnote_count": 0, + "unresolved_footnote_count": 0, + "reference_count": 0, + "page_header_count": 0, + "page_footer_count": 0, + } + + def _walk(items: list[Any]) -> None: + for item in items: + t = type(item).__name__ + if t == "Section": + counts["section_count"] += 1 + _walk(item.children) + elif t == "Paragraph": + counts["paragraph_count"] += 1 + elif t == "Table": + counts["table_count"] += 1 + elif t == "Figure": + counts["figure_count"] += 1 + elif t == "Equation": + counts["equation_count"] += 1 + elif t == "Footnote": + counts["footnote_count"] += 1 + if item.attached_object_id is None: + counts["unresolved_footnote_count"] += 1 + elif t == "Reference": + counts["reference_count"] += 1 + elif t == "PageHeader": + counts["page_header_count"] += 1 + elif t == "PageFooter": + counts["page_footer_count"] += 1 + + for page in pages: + _walk(page.children) + + return counts + + +def _extract_title( + page_builders: list[PageBuilder], + classified_pages: list[list[Any]], +) -> str | None: + """ + Return the html of the first GENUINE_SECTION_HEADER-classified block on + the first non-front-matter page. None if no such block exists. + """ + for page_index, page_builder in enumerate( + sorted(page_builders, key=lambda pb: pb.page_number or 0) + ): + if page_builder.is_front_matter: + continue + if page_index >= len(classified_pages): + continue + for cb in classified_pages[page_index]: + if cb.disposition == Disposition.GENUINE_SECTION_HEADER: + return getattr(cb.unwrapped.block, "html", None) + return None + +def materialize( + page_builders: list[PageBuilder], + source_pdf_identifier: str, + processing_context: NormalizerProcessingContext, + classified_pages: list[list[Any]], +) -> Document: + #document_id (computed once) + document_id = _compute_document_id(source_pdf_identifier) + + sorted_page_builders = sorted(page_builders, key=lambda pb: pb.page_number or 0) + + id_map = _build_marker_id_to_final_id_map(sorted_page_builders, document_id) + + materialized_pages: list[Page] = [] + + for pb in sorted_page_builders: + page_children: list[Any] = [] + for child in pb.children: + if isinstance(child, SectionBuilder): + page_children.append( + _mat_section_postorder(child, document_id, id_map) + ) + else: + page_children.append( + _materialize_leaf(child, document_id, id_map) + ) + + page = Page( + id=_compute_object_id(document_id, pb.canonical_path), + page_number=pb.page_number, + is_front_matter=pb.is_front_matter, + children=page_children, + provenance=_materialize_provenance(pb.provenance, document_id, id_map), + ) + materialized_pages.append(page) + + processing_metadata = ProcessingMetadata( + marker_version=processing_context.marker_version, + normalizer_version=processing_context.normalizer_version, + source_marker_artifact_ref=processing_context.source_marker_artifact_ref, + processed_at=processing_context.processed_at, + ) + + title = _extract_title(sorted_page_builders, classified_pages) + + metadata = Metadata( + page_count=len(materialized_pages), + has_front_matter_page=any(p.is_front_matter for p in materialized_pages), + title=title, + ) + + counts = _count_objects(materialized_pages) + statistics = Statistics( + page_count=counts["page_count"], + section_count=counts["section_count"], + paragraph_count=counts["paragraph_count"], + table_count=counts["table_count"], + figure_count=counts["figure_count"], + equation_count=counts["equation_count"], + footnote_count=counts["footnote_count"], + unresolved_footnote_count=counts["unresolved_footnote_count"], + reference_count=counts["reference_count"], + ) + + document = Document( + id=document_id, + pages=materialized_pages, + metadata=metadata, + statistics=statistics, + processing_metadata=processing_metadata, + source_pdf_identifier=source_pdf_identifier, + ) + + log_materialization_complete( + statistics=counts, + normalizer_version=processing_context.normalizer_version, + ) + + return document \ No newline at end of file diff --git a/src/betydb_extraction/normalizer/internal/stage11.py b/src/betydb_extraction/normalizer/internal/stage11.py new file mode 100644 index 0000000..bcbf4fa --- /dev/null +++ b/src/betydb_extraction/normalizer/internal/stage11.py @@ -0,0 +1,321 @@ +""" +Stage 11: Whole-Tree Validation. +""" + +from __future__ import annotations + +import logging +from typing import Any + +from betydb_extraction.normalizer.errors import WholeTreeInvariantViolationError + +logger = logging.getLogger(__name__) + + +def _iter_all_objects(document: Any) -> list[Any]: + result: list[Any] = [] + + def _walk(items: list[Any]) -> None: + for item in items: + result.append(item) + t = type(item).__name__ + if t == "Section": + _walk(item.children) + elif t == "Table": + if item.caption is not None: + result.append(item.caption) + for row in (item.rows or []): + result.append(row) + for cell in (row.cells or []): + result.append(cell) + for tc in (item.cells or []): + result.append(tc) + elif t == "Figure": + if item.caption is not None: + result.append(item.caption) + + for page in document.pages: + result.append(page) + _walk(page.children) + + return result + + +def _iter_leaf_objects(document: Any) -> list[Any]: + result: list[Any] = [] + + def _walk(items: list[Any]) -> None: + for item in items: + result.append(item) + t = type(item).__name__ + if t == "Section": + _walk(item.children) + + for page in document.pages: + result.append(page) + _walk(page.children) + + return result + + +def _iter_objects_with_ancestor_chains(document: Any) -> list[tuple[Any, list[str]]]: + result: list[tuple[Any, list[str]]] = [] + + def _walk(items: list[Any], ancestor_chain: list[str]) -> None: + for item in items: + t = type(item).__name__ + if t == "Section": + result.append((item, list(ancestor_chain))) + _walk(item.children, ancestor_chain + [item.id]) + else: + result.append((item, list(ancestor_chain))) + if t == "Table" and item.caption is not None: + result.append((item.caption, list(ancestor_chain))) + elif t == "Figure" and item.caption is not None: + result.append((item.caption, list(ancestor_chain))) + + for page in document.pages: + _walk(page.children, []) + + return result + + +def _check_invariant_1(document: Any) -> None: + items = _iter_leaf_objects(document) + prev = -1 + offending: list[str] = [] + for item in items: + roi = getattr(getattr(item, "provenance", None), "reading_order_index", None) + if roi is None: + offending.append(getattr(item, "id", "")) + continue + if roi <= prev: + offending.append(getattr(item, "id", "")) + prev = roi + + if offending: + raise WholeTreeInvariantViolationError( + invariant_number=1, + description="reading_order_index is not strictly increasing", + offending_object_ids=offending, + ) + + +def _check_invariant_2(document: Any, all_objects: list[Any]) -> None: + table_figure_ids: set[str] = { + obj.id + for obj in all_objects + if type(obj).__name__ in {"Table", "Figure"} + } + offending: list[str] = [] + for obj in all_objects: + if type(obj).__name__ != "Footnote": + continue + aid = obj.attached_object_id + if aid is not None and aid not in table_figure_ids: + offending.append(obj.id) + + if offending: + raise WholeTreeInvariantViolationError( + invariant_number=2, + description=( + "Footnote.attached_object_id does not match any Table or Figure id" + ), + offending_object_ids=offending, + ) + + +def _check_invariant_3(document: Any, all_objects: list[Any]) -> None: + footnote_by_id: dict[str, Any] = { + obj.id: obj + for obj in all_objects + if type(obj).__name__ == "Footnote" + } + offending: list[str] = [] + for obj in all_objects: + if type(obj).__name__ != "Table": + continue + for fid in (obj.footnote_ids or []): + fn = footnote_by_id.get(fid) + if fn is None or fn.attached_object_id != obj.id: + offending.append(obj.id) + break + + if offending: + raise WholeTreeInvariantViolationError( + invariant_number=3, + description="Footnote ↔ Table symmetry violated", + offending_object_ids=offending, + ) + + +def _check_invariant_4(document: Any) -> None: + pairs = _iter_objects_with_ancestor_chains(document) + offending: list[str] = [] + + for obj, real_ancestor_chain in pairs: + prov = getattr(obj, "provenance", None) + if prov is None: + continue + + stored_path: list[str] = list( + getattr(prov, "section_path", []) or [] + ) + + if stored_path == real_ancestor_chain: + continue + + # TEMP DIAGNOSTIC — remove after root cause found + print(f"MISMATCH obj={type(obj).__name__} id={getattr(obj, 'id', '')}") + print(f" stored_path = {stored_path}") + print(f" real_chain = {real_ancestor_chain}") + + offending.append(getattr(obj, "id", "")) + + if offending: + raise WholeTreeInvariantViolationError( + invariant_number=4, + description=( + "section_path does not exactly match the object's real " + "ancestor Section chain (id sequence, order, and length " + "must match exactly)" + ), + offending_object_ids=offending, + ) + + +def _check_invariant_5(all_objects: list[Any]) -> None: + seen: dict[str, str] = {} + offending: list[str] = [] + for obj in all_objects: + oid = getattr(obj, "id", None) + if oid is None: + continue + if oid in seen: + offending.append(oid) + else: + seen[oid] = type(obj).__name__ + + if offending: + raise WholeTreeInvariantViolationError( + invariant_number=5, + description="Duplicate id values found in the tree", + offending_object_ids=offending, + ) + + +def _check_invariant_6(document: Any) -> None: + page_numbers = [p.page_number for p in document.pages] + offending: list[str] = [] + for i in range(1, len(page_numbers)): + if page_numbers[i] <= page_numbers[i - 1]: + offending.append(str(page_numbers[i])) + + if offending: + raise WholeTreeInvariantViolationError( + invariant_number=6, + description="Document.pages is not strictly ascending by page_number", + offending_object_ids=offending, + ) + + +def _check_invariant_7(document: Any) -> None: + counts: dict[str, int] = { + "page_count": len(document.pages), + "section_count": 0, + "paragraph_count": 0, + "table_count": 0, + "figure_count": 0, + "equation_count": 0, + "footnote_count": 0, + "unresolved_footnote_count": 0, + "reference_count": 0, + } + + def _walk(items: list[Any]) -> None: + for item in items: + t = type(item).__name__ + if t == "Section": + counts["section_count"] += 1 + _walk(item.children) + elif t == "Paragraph": + counts["paragraph_count"] += 1 + elif t == "Table": + counts["table_count"] += 1 + elif t == "Figure": + counts["figure_count"] += 1 + elif t == "Equation": + counts["equation_count"] += 1 + elif t == "Footnote": + counts["footnote_count"] += 1 + if item.attached_object_id is None: + counts["unresolved_footnote_count"] += 1 + elif t == "Reference": + counts["reference_count"] += 1 + + for page in document.pages: + _walk(page.children) + + stats = document.statistics + offending: list[str] = [] + for field_name, expected in counts.items(): + actual = getattr(stats, field_name, None) + if actual != expected: + offending.append( + f"{field_name}: expected={expected} actual={actual}" + ) + + if offending: + raise WholeTreeInvariantViolationError( + invariant_number=7, + description="Statistics counts do not match independent re-traversal: " + + "; ".join(offending), + offending_object_ids=[], + ) + + +def _check_invariant_8( + document: Any, + all_objects: list[Any], + stage3_expected_ids: set[str], +) -> None: + tree_ids: dict[str, int] = {} + for obj in all_objects: + oid = getattr(obj, "id", None) + if oid is not None: + tree_ids[oid] = tree_ids.get(oid, 0) + 1 + + missing = stage3_expected_ids - set(tree_ids) + duplicated = { + oid for oid, count in tree_ids.items() + if oid in stage3_expected_ids and count > 1 + } + offending = list(missing) + list(duplicated) + + if offending: + raise WholeTreeInvariantViolationError( + invariant_number=8, + description=( + "Stage-3 builders missing from tree or duplicated: " + f"missing={len(missing)} duplicated={len(duplicated)}" + ), + offending_object_ids=offending, + ) + + +def validate_whole_tree( + document: Any, + stage3_expected_ids: set[str], +) -> None: + all_objects = _iter_all_objects(document) + + _check_invariant_1(document) + _check_invariant_2(document, all_objects) + _check_invariant_3(document, all_objects) + _check_invariant_4(document) + _check_invariant_5(all_objects) + _check_invariant_6(document) + _check_invariant_7(document) + _check_invariant_8(document, all_objects, stage3_expected_ids) + + logger.debug("stage11 complete: all 8 whole-tree invariants passed") \ No newline at end of file diff --git a/src/betydb_extraction/normalizer/internal/stage1_5.py b/src/betydb_extraction/normalizer/internal/stage1_5.py new file mode 100644 index 0000000..6057abc --- /dev/null +++ b/src/betydb_extraction/normalizer/internal/stage1_5.py @@ -0,0 +1,34 @@ +""" +Stage 1.5: Wrapper Unwrapping. +""" +from __future__ import annotations + +from typing import Any + +from betydb_extraction.normalizer.builders.base import UnwrappedBlock, WrapperContext + +# The three Marker block types that Stage 1.5 unwraps. +_WRAPPER_BLOCK_TYPES: frozenset[str] = frozenset( + {"TableGroup", "FigureGroup", "ListGroup"} +) + + +def unwrap_page(page_block_children: list[Any]) -> list[UnwrappedBlock]: + result: list[UnwrappedBlock] = [] + + for block in page_block_children: + block_type: str = block.block_type + + if block_type in _WRAPPER_BLOCK_TYPES: + # Unwrap exactly one level: each child gets the wrapper's context. + ctx = WrapperContext(wrapper_type=block_type, block_id=block.id) + for child in block.children: + # Inner wrappers are NOT recursively expanded — they land here + # tagged with the outer wrapper's ctx and are treated as + # ordinary blocks by Stage 2. + result.append(UnwrappedBlock(block=child, wrapper_context=ctx)) + else: + # Direct page child — no wrapper context. + result.append(UnwrappedBlock(block=block, wrapper_context=None)) + + return result \ No newline at end of file diff --git a/src/betydb_extraction/normalizer/internal/stage2.py b/src/betydb_extraction/normalizer/internal/stage2.py new file mode 100644 index 0000000..931ec91 --- /dev/null +++ b/src/betydb_extraction/normalizer/internal/stage2.py @@ -0,0 +1,179 @@ +""" +Stage 2: Block Classification. +""" +from __future__ import annotations + +import re +from typing import Any + +from betydb_extraction.normalizer.builders.base import ClassifiedBlock, Disposition, UnwrappedBlock +from betydb_extraction.normalizer.errors import UnrecognizedBlockTypeError +from betydb_extraction.normalizer.logging_util import log_picture_discarded + +# ── Constants ───────────────────────────────────────────────────────────────── + +# Regex for CAPTION_LABEL override condition (a): +# Matches "Table 3", "Figure 12.", "TABLE 1:" etc. +_CAPTION_LABEL_RE = re.compile( + r"^(Table|Figure)\s+\d+[\.:]?\s*$", + re.IGNORECASE, +) + +# block_types that trigger the CAPTION_LABEL lookahead condition (b): +_CAPTION_TARGET_BLOCK_TYPES: frozenset[str] = frozenset({"Table", "Figure"}) + +# How many subsequent blocks to scan for a Table/Figure in condition (b): +_CAPTION_LOOKAHEAD_WINDOW = 3 + +# block_types that are wrapper types — must have been consumed by Stage 1.5. +# If one appears here, it was inside another wrapper (inner-wrapper case) and +# is classified normally; its block_type will not be in _DISPATCH, so +# UnrecognizedBlockTypeError fires — intentionally loud per spec §4.2. +_WRAPPER_BLOCK_TYPES: frozenset[str] = frozenset( + {"TableGroup", "FigureGroup", "ListGroup"} +) + +# References section heading vocabulary (spec §5 Stage 2, ListItem rule): +_REFERENCES_VOCABULARY: frozenset[str] = frozenset( + {"references", "bibliography", "works cited", "literature cited"} +) + +# Static dispatch table: block_type → default Disposition. +# Does NOT include SectionHeader or ListItem — those have override logic. +_STATIC_DISPATCH: dict[str, Disposition] = { + "Text": Disposition.BODY_PARAGRAPH, + "Table": Disposition.TABLE_SHELL, + "Figure": Disposition.FIGURE_SHELL, + "Caption": Disposition.CAPTION_TEXT, + "TableCell": Disposition.TABLE_CELL_EVIDENCE, + "Equation": Disposition.EQUATION, + "Footnote": Disposition.FOOTNOTE, + "PageHeader": Disposition.PAGE_HEADER, + "PageFooter": Disposition.PAGE_FOOTER, + "Picture": Disposition.PICTURE, +} + + +# ── Helpers ─────────────────────────────────────────────────────────────────── + +def _html_text(block: Any) -> str: + """Return block.html stripped of leading/trailing whitespace.""" + return (getattr(block, "html", "") or "").strip() + + +def _has_table_or_figure_within( + unwrapped_seq: list[UnwrappedBlock], + start_index: int, + window: int, +) -> bool: + end = min(start_index + window, len(unwrapped_seq)) + for i in range(start_index, end): + if unwrapped_seq[i].block.block_type in _CAPTION_TARGET_BLOCK_TYPES: + return True + return False + + +def _resolve_section_header_disposition( + unwrapped: UnwrappedBlock, + seq: list[UnwrappedBlock], + current_index: int, +) -> Disposition: + text = _html_text(unwrapped.block) + + if _CAPTION_LABEL_RE.match(text): + next_start = current_index + 1 + if _has_table_or_figure_within(seq, next_start, _CAPTION_LOOKAHEAD_WINDOW): + return Disposition.CAPTION_LABEL + + return Disposition.GENUINE_SECTION_HEADER + + +def _resolve_list_item_disposition( + block: Any, + heading_registry: dict[str, Any], # marker_block_id → MarkerBlock + classified_headings: dict[str, Disposition], # id → disposition so far +) -> Disposition: + section_hierarchy: dict[str, str] = getattr( + block, "section_hierarchy", {} + ) or {} + + if not section_hierarchy: + return Disposition.BODY_PARAGRAPH + + # Sort depth keys numerically to find the deepest governing section. + try: + sorted_keys = sorted(section_hierarchy.keys(), key=lambda k: int(k)) + except (ValueError, TypeError): + # Non-numeric keys: fall back to lexicographic sort (conservative). + sorted_keys = sorted(section_hierarchy.keys()) + + if not sorted_keys: + return Disposition.BODY_PARAGRAPH + + deepest_key = sorted_keys[-1] + deepest_heading_id: str = section_hierarchy[deepest_key] + + heading_block = heading_registry.get(deepest_heading_id) + if heading_block is None: + # Heading not found in prior blocks — no governing section resolved. + return Disposition.BODY_PARAGRAPH + + heading_text = _html_text(heading_block).lower() + if heading_text in _REFERENCES_VOCABULARY: + return Disposition.REFERENCE_ENTRY + + return Disposition.BODY_PARAGRAPH + + +# ── Public entry point ──────────────────────────────────────────────────────── + +def classify_blocks( + unwrapped_seq: list[UnwrappedBlock], + page_index: int, + heading_registry: dict[str, Any] | None = None, +) -> list[ClassifiedBlock]: + result: list[ClassifiedBlock] = [] + + if heading_registry is None: + heading_registry = {} + + for i, unwrapped in enumerate(unwrapped_seq): + block = unwrapped.block + block_type: str = block.block_type + + # ── SectionHeader (has override logic) ─────────────────────────────── + if block_type == "SectionHeader": + disposition = _resolve_section_header_disposition(unwrapped, unwrapped_seq, i) + if disposition == Disposition.GENUINE_SECTION_HEADER: + # Register immediately so ListItems later in the pass can find it. + heading_registry[block.id] = block + + # ── ListItem (has override logic) ───────────────────────────────────── + elif block_type == "ListItem": + disposition = _resolve_list_item_disposition( + block, heading_registry, {} + ) + + # ── Picture (static, but requires a log entry) ──────────────────────── + elif block_type == "Picture": + disposition = Disposition.PICTURE + log_picture_discarded( + marker_block_id=block.id, + page_index=page_index, + ) + + # ── Static dispatch ─────────────────────────────────────────────────── + elif block_type in _STATIC_DISPATCH: + disposition = _STATIC_DISPATCH[block_type] + + # ── Unrecognised ────────────────────────────────────────────────────── + else: + raise UnrecognizedBlockTypeError( + block_type=block_type, + marker_block_id=block.id, + page_index=page_index, + ) + + result.append(ClassifiedBlock(unwrapped=unwrapped, disposition=disposition)) + + return result \ No newline at end of file diff --git a/src/betydb_extraction/normalizer/internal/stage3.py b/src/betydb_extraction/normalizer/internal/stage3.py new file mode 100644 index 0000000..0737294 --- /dev/null +++ b/src/betydb_extraction/normalizer/internal/stage3.py @@ -0,0 +1,228 @@ +""" +Stage 3: Leaf Builder Construction. +""" +from __future__ import annotations + +from typing import Any + +from betydb_extraction.normalizer.builders.base import ClassifiedBlock, Disposition +from betydb_extraction.normalizer.builders.caption import CaptionBuilder +from betydb_extraction.normalizer.builders.equation import EquationBuilder +from betydb_extraction.normalizer.builders.figure import FigureBuilder +from betydb_extraction.normalizer.builders.footnote import FootnoteBuilder +from betydb_extraction.normalizer.builders.page_footer import PageFooterBuilder +from betydb_extraction.normalizer.builders.page_header import PageHeaderBuilder +from betydb_extraction.normalizer.builders.paragraph import ParagraphBuilder +from betydb_extraction.normalizer.builders.provenance import ProvenanceBuilder +from betydb_extraction.normalizer.builders.reference import ReferenceBuilder +from betydb_extraction.normalizer.builders.table import TableBuilder + +# Union type alias for all builders Stage 3 can produce. +# (Section builders are excluded — they are produced by Stage 7.) +ObjectBuilder = ( + ParagraphBuilder + | EquationBuilder + | FootnoteBuilder + | ReferenceBuilder + | PageHeaderBuilder + | PageFooterBuilder + | TableBuilder + | FigureBuilder +) + +# ── kind literal constants ──────────────────────────────────────────────────── +# These are the Schema v1.1 discriminated-union literal values for each type. +# Read once from the schema; not computed at runtime. +_KIND_PARAGRAPH = "paragraph" +_KIND_EQUATION = "equation" +_KIND_FOOTNOTE = "footnote" +_KIND_REFERENCE = "reference" +_KIND_PAGE_HEADER = "page_header" +_KIND_PAGE_FOOTER = "page_footer" +_KIND_TABLE = "table" +_KIND_FIGURE = "figure" + +# Dispositions that produce NO builder in Stage 3: +_NO_BUILDER_DISPOSITIONS: frozenset[Disposition] = frozenset({ + Disposition.CAPTION_TEXT, + Disposition.TABLE_CELL_EVIDENCE, + Disposition.GENUINE_SECTION_HEADER, + Disposition.CAPTION_LABEL, + Disposition.PICTURE, +}) + + +# ── Helpers ─────────────────────────────────────────────────────────────────── + +def _html(block: Any) -> str: + """Return block.html, defaulting to empty string if absent or None.""" + return getattr(block, "html", "") or "" + + +def _make_provenance(block: Any, page_number: int) -> ProvenanceBuilder: + return ProvenanceBuilder( + marker_block_ids=[block.id], + page_number=page_number, + bbox=getattr(block, "bbox", None), + polygon=getattr(block, "polygon", None), + # contributing_bboxes: not set at Stage 3 (only Pattern-B captions use it) + contributing_bboxes=None, + # reading_order_index: Stage 8 + reading_order_index=None, + # section_path: Stage 7 + section_path=[], + ) + + +def _image_data(block: Any) -> bytes | None: + images: dict | None = getattr(block, "images", None) + if not images: + return None + for value in images.values(): + if value: + if isinstance(value, bytes): + return value + if isinstance(value, str): + import base64 + try: + return base64.b64decode(value) + except Exception: + return value.encode() + return None + + +# ── Builder constructors ────────────────────────────────────────────────────── + +def _build_paragraph(block: Any, page_number: int) -> ParagraphBuilder: + return ParagraphBuilder( + kind=_KIND_PARAGRAPH, + text=_html(block), + provenance=_make_provenance(block, page_number), + # deferred + canonical_path=None, + + ) + + +def _build_equation(block: Any, page_number: int) -> EquationBuilder: + return EquationBuilder( + kind=_KIND_EQUATION, + raw_math=_html(block), + equation_number=None, # deferred-population slot + provenance=_make_provenance(block, page_number), + canonical_path=None, + + ) + + +def _build_footnote(block: Any, page_number: int) -> FootnoteBuilder: + return FootnoteBuilder( + kind=_KIND_FOOTNOTE, + raw_text=_html(block), + attached_object_id=None, # Stage 6 + provenance=_make_provenance(block, page_number), + canonical_path=None, + + ) + + +def _build_reference(block: Any, page_number: int) -> ReferenceBuilder: + return ReferenceBuilder( + kind=_KIND_REFERENCE, + raw_text=_html(block), + provenance=_make_provenance(block, page_number), + canonical_path=None, + + ) + + +def _build_page_header(block: Any, page_number: int) -> PageHeaderBuilder: + return PageHeaderBuilder( + kind=_KIND_PAGE_HEADER, + raw_text=_html(block), + provenance=_make_provenance(block, page_number), + canonical_path=None, + + ) + + +def _build_page_footer(block: Any, page_number: int) -> PageFooterBuilder: + return PageFooterBuilder( + kind=_KIND_PAGE_FOOTER, + raw_text=_html(block), + provenance=_make_provenance(block, page_number), + canonical_path=None, + + ) + + +def _build_table(block: Any, page_number: int) -> TableBuilder: + return TableBuilder( + kind=_KIND_TABLE, + raw_html=_html(block), + caption=None, # Stage 4 + rows=[], # Stage 5 + cells=[], # Stage 5 (bare tables keep []) + footnote_ids=[], # Stage 6 + provenance=_make_provenance(block, page_number), + canonical_path=None, + + ) + + +def _build_figure(block: Any, page_number: int) -> FigureBuilder: + return FigureBuilder( + kind=_KIND_FIGURE, + image_data=_image_data(block), + caption=None, # Stage 4 + footnote_ids=[], # Stage 6 + provenance=_make_provenance(block, page_number), + canonical_path=None, + + ) + + +# ── Dispatch table ──────────────────────────────────────────────────────────── + +_BUILDER_DISPATCH: dict[Disposition, Any] = { + Disposition.BODY_PARAGRAPH: _build_paragraph, + Disposition.EQUATION: _build_equation, + Disposition.FOOTNOTE: _build_footnote, + Disposition.REFERENCE_ENTRY: _build_reference, + Disposition.PAGE_HEADER: _build_page_header, + Disposition.PAGE_FOOTER: _build_page_footer, + Disposition.TABLE_SHELL: _build_table, + Disposition.FIGURE_SHELL: _build_figure, +} + + +# ── Public entry point ──────────────────────────────────────────────────────── + +def build_leaf_builders( + classified_seq: list[ClassifiedBlock], + page_number: int, +) -> list[ObjectBuilder]: + builders: list[ObjectBuilder] = [] + + for cb in classified_seq: + disposition = cb.disposition + + if disposition in _NO_BUILDER_DISPOSITIONS: + # Intentionally skipped — consumed by Stages 4, 5, or 7, or + # discarded (PICTURE, already logged in Stage 2). + continue + + constructor = _BUILDER_DISPATCH.get(disposition) + if constructor is None: + # Should never happen if Stage 2's classification table is + # complete — but fail loudly rather than silently dropping. + raise RuntimeError( + f"Stage 3: no builder constructor for disposition " + f"{disposition!r} (block id={cb.unwrapped.block.id!r}, " + f"page_index={page_number})" + ) + + builder = constructor(cb.unwrapped.block, page_number) + builders.append(builder) + + return builders \ No newline at end of file diff --git a/src/betydb_extraction/normalizer/internal/stage4.py b/src/betydb_extraction/normalizer/internal/stage4.py new file mode 100644 index 0000000..8b12663 --- /dev/null +++ b/src/betydb_extraction/normalizer/internal/stage4.py @@ -0,0 +1,310 @@ +""" +Stage 4: Caption Resolution. +""" +from __future__ import annotations + +import re +from typing import Any + +from betydb_extraction.normalizer.builders.base import ClassifiedBlock, Disposition +from betydb_extraction.normalizer.builders.caption import CaptionBuilder +from betydb_extraction.normalizer.builders.figure import FigureBuilder +from betydb_extraction.normalizer.builders.provenance import ProvenanceBuilder +from betydb_extraction.normalizer.builders.table import TableBuilder +from betydb_extraction.normalizer.logging_util import ( + log_caption_not_resolved, + log_caption_resolved, +) + +# Schema v1.1 discriminated-union literal for Caption. +_KIND_CAPTION = "caption" +_LABEL_PREFIX_RE = re.compile( + r"^((?:Table|Figure)\s+\d+[\.:]?\s*)", + re.IGNORECASE, +) + +# Trailing-notes trigger (case-sensitive, per spec): +_TRAILING_NOTE_PREFIX = "Note:" + +# Dispositions that are transparent to Pattern B adjacency scans: +_TRANSPARENT_DISPOSITIONS: frozenset[Disposition] = frozenset({ + Disposition.PICTURE, +}) + + +# ── Internal helpers ────────────────────────────────────────────────────────── + +def _html(block: Any) -> str: + return getattr(block, "html", "") or "" + + +def _make_caption_provenance_pattern_a( + caption_block: Any, + wrapper_block_id: str, + page_number: int, +) -> ProvenanceBuilder: + return ProvenanceBuilder( + marker_block_ids=[caption_block.id, wrapper_block_id], + page_number=page_number, + bbox=getattr(caption_block, "bbox", None), + contributing_bboxes=None, + polygon=getattr(caption_block, "polygon", None), + reading_order_index=None, # Stage 8 + section_path=[], # Stage 7 + ) + + +def _make_caption_provenance_pattern_b( + label_block: Any, + caption_text_block: Any, + trailing_notes_block: Any | None, + page_number: int, +) -> ProvenanceBuilder: + ids = [label_block.id, caption_text_block.id] + bboxes = [] + + lb_bbox = getattr(label_block, "bbox", None) + if lb_bbox is not None: + bboxes.append(lb_bbox) + + ct_bbox = getattr(caption_text_block, "bbox", None) + if ct_bbox is not None: + bboxes.append(ct_bbox) + + if trailing_notes_block is not None: + ids.append(trailing_notes_block.id) + tn_bbox = getattr(trailing_notes_block, "bbox", None) + if tn_bbox is not None: + bboxes.append(tn_bbox) + + return ProvenanceBuilder( + marker_block_ids=ids, + page_number=page_number, + bbox=None, + contributing_bboxes=bboxes if bboxes else None, + polygon=None, + reading_order_index=None, # Stage 8 + section_path=[], # Stage 7 + ) + + +def _split_label_and_text_pattern_a(caption_html: str) -> tuple[str | None, str | None]: + m = _LABEL_PREFIX_RE.match(caption_html) + if m: + raw_label = m.group(1).rstrip(". :") + remainder = caption_html[m.end():].lstrip(". :") + return (raw_label or None, remainder.strip() or None) + return (None, caption_html.strip() or None) + + +# ── Pattern A resolution ────────────────────────────────────────────────────── + +def _resolve_pattern_a( + wrapper_block_id: str, + classified_seq: list[ClassifiedBlock], + page_number: int, +) -> CaptionBuilder | None: + for cb in classified_seq: + if ( + cb.disposition == Disposition.CAPTION_TEXT + and cb.unwrapped.wrapper_context is not None + and cb.unwrapped.wrapper_context.block_id == wrapper_block_id + ): + caption_block = cb.unwrapped.block + caption_html = _html(caption_block) + label, text = _split_label_and_text_pattern_a(caption_html) + provenance = _make_caption_provenance_pattern_a( + caption_block, wrapper_block_id, page_number + ) + return CaptionBuilder( + kind=_KIND_CAPTION, + label=label, + text=text, + trailing_notes=None, + provenance=provenance, + canonical_path=None, + ) + + return None + + +# ── Pattern B resolution ────────────────────────────────────────────────────── + +def _non_picture_blocks_before( + classified_seq: list[ClassifiedBlock], + index: int, +) -> list[tuple[int, ClassifiedBlock]]: + return [ + (i, cb) + for i, cb in enumerate(classified_seq[:index]) + if cb.disposition not in _TRANSPARENT_DISPOSITIONS + ] + + +def _non_picture_blocks_after( + classified_seq: list[ClassifiedBlock], + index: int, +) -> list[tuple[int, ClassifiedBlock]]: + return [ + (i, cb) + for i, cb in enumerate(classified_seq[index + 1:], start=index + 1) + if cb.disposition not in _TRANSPARENT_DISPOSITIONS + ] + + +def _resolve_pattern_b( + shell_index: int, + classified_seq: list[ClassifiedBlock], + page_number: int, + object_kind: str, + marker_block_id: str, +) -> CaptionBuilder | None: + # 1. Find immediately preceding non-PICTURE block — must be the caption + # text (BODY_PARAGRAPH), the block adjacent to the shell. + preceding = _non_picture_blocks_before(classified_seq, shell_index) + if not preceding: + log_caption_not_resolved( + object_kind=object_kind, + marker_block_id=marker_block_id, + reason="pattern_b: no preceding non-PICTURE block", + ) + return None + + text_index, text_cb = preceding[-1] + if text_cb.disposition != Disposition.BODY_PARAGRAPH: + log_caption_not_resolved( + object_kind=object_kind, + marker_block_id=marker_block_id, + reason=( + f"pattern_b: immediately preceding non-PICTURE block has " + f"disposition={text_cb.disposition!r}, expected BODY_PARAGRAPH" + ), + ) + return None + + caption_text_block = text_cb.unwrapped.block + + # 2. Find the non-PICTURE block immediately preceding the caption text — + # must be CAPTION_LABEL. + preceding_label = _non_picture_blocks_before(classified_seq, text_index) + if not preceding_label: + log_caption_not_resolved( + object_kind=object_kind, + marker_block_id=marker_block_id, + reason="pattern_b: no preceding non-PICTURE block before caption text", + ) + return None + + label_index, label_cb = preceding_label[-1] + if label_cb.disposition != Disposition.CAPTION_LABEL: + log_caption_not_resolved( + object_kind=object_kind, + marker_block_id=marker_block_id, + reason=( + f"pattern_b: block immediately preceding caption text has " + f"disposition={label_cb.disposition!r}, expected CAPTION_LABEL" + ), + ) + return None + + label_block = label_cb.unwrapped.block + + # 3. Check for optional trailing-notes block immediately after the shell. + following = _non_picture_blocks_after(classified_seq, shell_index) + trailing_notes_block: Any | None = None + if following: + _, next_cb = following[0] + next_html = _html(next_cb.unwrapped.block) + if next_html.startswith(_TRAILING_NOTE_PREFIX): + trailing_notes_block = next_cb.unwrapped.block + + # 4. Build the CaptionBuilder. + provenance = _make_caption_provenance_pattern_b( + label_block=label_block, + caption_text_block=caption_text_block, + trailing_notes_block=trailing_notes_block, + page_number=page_number, + ) + return CaptionBuilder( + kind=_KIND_CAPTION, + label=_html(label_block) or None, + text=_html(caption_text_block) or None, + trailing_notes=_html(trailing_notes_block) if trailing_notes_block else None, + provenance=provenance, + canonical_path=None, + ) + + +# ── Public entry point ──────────────────────────────────────────────────────── + +def resolve_captions( + object_builders: list[Any], + classified_seq: list[ClassifiedBlock], + page_number: int, +) -> None: + # Build a lookup: Marker block id → index in classified_seq. + # Used to find the shell's position for Pattern B adjacency scan, and to + # recover wrapper_context for Pattern A. + block_id_to_index: dict[str, int] = { + cb.unwrapped.block.id: i + for i, cb in enumerate(classified_seq) + } + + for builder in object_builders: + if not isinstance(builder, (TableBuilder, FigureBuilder)): + continue + + # The shell's own Marker id is always marker_block_ids[0] at this + # point in the pipeline (Stage 3 sets it to exactly [block.id]). + marker_block_id = builder.provenance.marker_block_ids[0] + shell_index = block_id_to_index.get(marker_block_id) + if shell_index is None: + log_caption_not_resolved( + object_kind=builder.kind or "unknown", + marker_block_id=marker_block_id, + reason="shell block not found in classified_seq", + ) + builder.caption = None + continue + + shell_cb = classified_seq[shell_index] + wrapper_ctx = shell_cb.unwrapped.wrapper_context + object_kind = builder.kind or "unknown" + + if wrapper_ctx is not None: + # Pattern A: wrapped (TableGroup or FigureGroup). + caption = _resolve_pattern_a( + wrapper_block_id=wrapper_ctx.block_id, + classified_seq=classified_seq, + page_number=page_number, + ) + if caption is not None: + log_caption_resolved( + object_kind=object_kind, + marker_block_id=marker_block_id, + pattern="A", + ) + else: + log_caption_not_resolved( + object_kind=object_kind, + marker_block_id=marker_block_id, + reason="pattern_a: no CAPTION_TEXT sibling found for wrapper_id", + ) + else: + # Pattern B: bare (wrapper_context is None). + caption = _resolve_pattern_b( + shell_index=shell_index, + classified_seq=classified_seq, + page_number=page_number, + object_kind=object_kind, + marker_block_id=marker_block_id, + ) + # Pattern B logs its own failure reason internally; log success here. + if caption is not None: + log_caption_resolved( + object_kind=object_kind, + marker_block_id=marker_block_id, + pattern="B", + ) + + builder.caption = caption \ No newline at end of file diff --git a/src/betydb_extraction/normalizer/internal/stage5.py b/src/betydb_extraction/normalizer/internal/stage5.py new file mode 100644 index 0000000..15763e7 --- /dev/null +++ b/src/betydb_extraction/normalizer/internal/stage5.py @@ -0,0 +1,153 @@ +""" +Stage 5: Table Internal Structure. +""" +from __future__ import annotations + +import re +from typing import Any + +from bs4 import BeautifulSoup +from bs4.element import Tag + +from betydb_extraction.normalizer.builders.base import ClassifiedBlock, Disposition +from betydb_extraction.normalizer.builders.table import ( + TableBuilder, + TableCellBuilder, + TableRowBuilder, + TableRowCellBuilder, +) + +_KIND_TABLE_ROW = "table_row" +_KIND_TABLE_ROW_CELL = "table_row_cell" +_KIND_TABLE_CELL = "table_cell" + +# Matches a ... wrapper tag (case-insensitive, DOTALL so +# it spans multiple lines), capturing its inner content in group 1. +_MATH_WRAPPER_RE = re.compile( + r"]*>(.*?)", + re.IGNORECASE | re.DOTALL, +) + +# Header vs. data cell tag names. +_HEADER_CELL_TAGS = {"th"} +_DATA_CELL_TAGS = {"td"} +_CELL_TAGS = _HEADER_CELL_TAGS | _DATA_CELL_TAGS + + +# ── HTML parsing helpers ────────────────────────────────────────────────────── + +def _strip_math_wrapper(html_fragment: str) -> str: + return _MATH_WRAPPER_RE.sub(lambda m: m.group(1), html_fragment) + + +def _cell_inner_html(cell_tag: Tag) -> str: + return cell_tag.decode_contents() + + +def _parse_rows(raw_html: str) -> list[TableRowBuilder]: + + if not raw_html or not raw_html.strip(): + return [] + + soup = BeautifulSoup(raw_html, "html.parser") + row_builders: list[TableRowBuilder] = [] + + for tr_tag in soup.find_all("tr"): + cell_builders: list[TableRowCellBuilder] = [] + + for cell_tag in tr_tag.find_all(list(_CELL_TAGS), recursive=False): + raw_inner_html = _cell_inner_html(cell_tag) + text = _strip_math_wrapper(raw_inner_html).strip() + is_header = cell_tag.name.lower() in _HEADER_CELL_TAGS + + cell_builders.append( + TableRowCellBuilder( + kind=_KIND_TABLE_ROW_CELL, + text=text or None, + is_header=is_header, + structural_notes=None, # Always None per spec. + canonical_path=None, + + ) + ) + + row_builders.append( + TableRowBuilder( + kind=_KIND_TABLE_ROW, + cells=cell_builders, + canonical_path=None, + + ) + ) + + return row_builders + + +# ── TABLE_CELL_EVIDENCE (flat cells) helpers ────────────────────────────────── + +def _html(block: Any) -> str: + return getattr(block, "html", "") or "" + + +def _build_flat_cells_for_wrapper( + wrapper_block_id: str, + classified_seq: list[ClassifiedBlock], +) -> list[TableCellBuilder]: + cell_builders: list[TableCellBuilder] = [] + + for cb in classified_seq: + if cb.disposition != Disposition.TABLE_CELL_EVIDENCE: + continue + wrapper_ctx = cb.unwrapped.wrapper_context + if wrapper_ctx is None or wrapper_ctx.block_id != wrapper_block_id: + continue + + block = cb.unwrapped.block + cell_builders.append( + TableCellBuilder( + kind=_KIND_TABLE_CELL, + marker_block_id=block.id, + text=_html(block) or None, + bbox=getattr(block, "bbox", None), + polygon=getattr(block, "polygon", None), + canonical_path=None, + + ) + ) + + return cell_builders + + +# ── Public entry point ──────────────────────────────────────────────────────── + +def build_table_structure( + object_builders: list[Any], + classified_seq: list[ClassifiedBlock], +) -> None: + block_id_to_index: dict[str, int] = { + cb.unwrapped.block.id: i + for i, cb in enumerate(classified_seq) + } + + for builder in object_builders: + if not isinstance(builder, TableBuilder): + continue + + # rows: parsed from raw_html unconditionally (wrapped or bare). + builder.rows = _parse_rows(builder.raw_html or "") + + # cells: only for wrapped tables; [] for bare tables (resolves MAJOR-4). + marker_block_id = builder.provenance.marker_block_ids[0] + shell_index = block_id_to_index.get(marker_block_id) + + wrapper_ctx = None + if shell_index is not None: + wrapper_ctx = classified_seq[shell_index].unwrapped.wrapper_context + + if wrapper_ctx is not None: + builder.cells = _build_flat_cells_for_wrapper( + wrapper_block_id=wrapper_ctx.block_id, + classified_seq=classified_seq, + ) + else: + builder.cells = [] \ No newline at end of file diff --git a/src/betydb_extraction/normalizer/internal/stage6.py b/src/betydb_extraction/normalizer/internal/stage6.py new file mode 100644 index 0000000..6c65b8f --- /dev/null +++ b/src/betydb_extraction/normalizer/internal/stage6.py @@ -0,0 +1,156 @@ +""" +Stage 6: Footnote Attachment. +""" +from __future__ import annotations + +from typing import Any + +from betydb_extraction.normalizer.builders.base import ClassifiedBlock +from betydb_extraction.normalizer.builders.figure import FigureBuilder +from betydb_extraction.normalizer.builders.footnote import FootnoteBuilder +from betydb_extraction.normalizer.builders.table import TableBuilder +from betydb_extraction.normalizer.logging_util import ( + log_footnote_attached, + log_footnote_missing_bbox, + log_footnote_not_attached, +) + +# Candidate target builder types for footnote attachment. +_TargetBuilder = TableBuilder | FigureBuilder + + +# ── Internal helpers ────────────────────────────────────────────────────────── + +def _bbox_y0(bbox: Any) -> float | None: + if bbox is None: + return None + return getattr(bbox, "y0", None) + + +def _bbox_y1(bbox: Any) -> float | None: + if bbox is None: + return None + return getattr(bbox, "y1", None) + + +def _marker_block_id(builder: Any) -> str: + mids = builder.provenance.marker_block_ids + if not mids: + raise ValueError( + f"Stage 6: builder {type(builder).__name__!r} has empty " + "provenance.marker_block_ids — cannot use as an interim " + "cross-reference id." + ) + return mids[0] + + +def _build_block_id_to_seq_index( + classified_seq: list[ClassifiedBlock], +) -> dict[str, int]: + return { + cb.unwrapped.block.id: i + for i, cb in enumerate(classified_seq) + } + + +def _select_best_candidate( + footnote_bbox_y0: float, + candidates: list[_TargetBuilder], + block_id_to_seq_index: dict[str, int], +) -> _TargetBuilder | None: + best: _TargetBuilder | None = None + best_y1: float | None = None + best_seq_index: int | None = None + + for candidate in candidates: + candidate_y1 = _bbox_y1(candidate.provenance.bbox) + if candidate_y1 is None: + # No bbox on this candidate — cannot be compared; skip it. + continue + if not (candidate_y1 < footnote_bbox_y0): + continue + + candidate_mbid = _marker_block_id(candidate) + candidate_seq_index = block_id_to_seq_index.get(candidate_mbid) + if candidate_seq_index is None: + # A candidate must originate from this page's classified + # sequence — if it doesn't, that's an internal inconsistency + # between the object_builders list and classified_seq passed + # to this call. Fail loudly rather than silently mis-ordering + # the tie-break (spec §22 fail-loud philosophy). + raise ValueError( + f"Stage 6: candidate {type(candidate).__name__!r} with " + f"marker_block_id={candidate_mbid!r} was not found in the " + "classified_seq passed to this call — object_builders and " + "classified_seq are out of sync for this page." + ) + + if ( + best is None + or candidate_y1 > best_y1 + or (candidate_y1 == best_y1 and candidate_seq_index < best_seq_index) + ): + best = candidate + best_y1 = candidate_y1 + best_seq_index = candidate_seq_index + + return best + + +# ── Public entry point ──────────────────────────────────────────────────────── + +def attach_footnotes( + object_builders: list[Any], + classified_seq: list[ClassifiedBlock], + page_number: int, +) -> None: + footnotes: list[FootnoteBuilder] = [ + b for b in object_builders if isinstance(b, FootnoteBuilder) + ] + candidates: list[_TargetBuilder] = [ + b for b in object_builders if isinstance(b, (TableBuilder, FigureBuilder)) + ] + + if not footnotes: + return + + block_id_to_seq_index = _build_block_id_to_seq_index(classified_seq) + + for footnote in footnotes: + footnote_mbid = _marker_block_id(footnote) + footnote_bbox_y0 = _bbox_y0(footnote.provenance.bbox) + + if footnote_bbox_y0 is None: + # Missing bbox — attached_object_id stays None; log per spec §23. + footnote.attached_object_id = None + log_footnote_missing_bbox( + marker_block_id=footnote_mbid, + page_index=page_number, + ) + continue + + target = _select_best_candidate( + footnote_bbox_y0=footnote_bbox_y0, + candidates=candidates, + block_id_to_seq_index=block_id_to_seq_index, + ) + + if target is None: + footnote.attached_object_id = None + log_footnote_not_attached( + marker_block_id=footnote_mbid, + page_index=page_number, + ) + continue + + # Atomic both-sides update, from this single matching result. + target_mbid = _marker_block_id(target) + footnote.attached_object_id = target_mbid + target.footnote_ids = list(target.footnote_ids or []) + [footnote_mbid] + + log_footnote_attached( + footnote_marker_block_id=footnote_mbid, + target_marker_block_id=target_mbid, + target_kind=type(target).__name__, + page_index=page_number, + ) \ No newline at end of file diff --git a/src/betydb_extraction/normalizer/internal/stage7.py b/src/betydb_extraction/normalizer/internal/stage7.py new file mode 100644 index 0000000..b3714a3 --- /dev/null +++ b/src/betydb_extraction/normalizer/internal/stage7.py @@ -0,0 +1,328 @@ +""" +Stage 7: Section Tree Assembly. +""" + +from __future__ import annotations + +import logging +from collections import defaultdict +from typing import Any + +from betydb_extraction.normalizer.builders.page import PageBuilder +from betydb_extraction.normalizer.builders.section import SectionBuilder +from betydb_extraction.normalizer.builders.provenance import ProvenanceBuilder +from betydb_extraction.normalizer.builders.base import Disposition + +logger = logging.getLogger(__name__) + + +def _numeric_sort_key(depth_key: str) -> int: + try: + return int(depth_key) + except ValueError: + return 10_000 + + +def _get_section_hierarchy(marker_block: Any) -> dict[str, str]: + raw = getattr(marker_block, "section_hierarchy", None) + if raw is None: + return {} + return dict(raw) # defensive copy + + +def _sorted_hierarchy_entries( + section_hierarchy: dict[str, str], +) -> list[tuple[int, str]]: + return sorted( + ((int(k), v) for k, v in section_hierarchy.items() if k.isdigit()), + key=lambda pair: pair[0], + ) + +def assemble_section_tree( + page_builders: list[PageBuilder], + classified_pages: list[list[Any]], +) -> None: + """ + Stage 7 entry point. + """ + #Step 1 + + # heading_registry[marker_block_id] = SectionBuilder + heading_registry: dict[str, SectionBuilder] = {} + + # heading_page_index[marker_block_id] = page_index + heading_page_index: dict[str, int] = {} + + # heading_seq_pos[marker_block_id] = position in that page's classified seq + heading_seq_pos: dict[str, int] = {} + + for page_index, classified_blocks in enumerate(classified_pages): + for seq_pos, cb in enumerate(classified_blocks): + if cb.disposition != Disposition.GENUINE_SECTION_HEADER: + continue + + block = cb.unwrapped.block + heading_id: str = block.id + heading_html: str = getattr(block, "html", "") or "" + + # Build provenance for the SectionBuilder heading. + from betydb_extraction.normalizer.builders.provenance import ProvenanceBuilder + prov = ProvenanceBuilder( + marker_block_ids=[heading_id], + page_number=page_index, + bbox=getattr(block, "bbox", None), + polygon=getattr(block, "polygon", None), + ) + + sb = SectionBuilder( + heading_text=heading_html, + heading_marker_block_id=heading_id, + provenance=prov, + children=[], + ) + + heading_registry[heading_id] = sb + heading_page_index[heading_id] = page_index + heading_seq_pos[heading_id] = seq_pos + + logger.debug( + "stage7 step1 registered heading marker_block_id=%s page=%d seq_pos=%d", + heading_id, + page_index, + seq_pos, + ) + + if not heading_registry: + logger.debug("stage7 no GENUINE_SECTION_HEADER blocks found; skipping tree assembly") + return + +#Step 2 + heading_depth: dict[str, int] = {} + + heading_parent_id: dict[str, str] = {} + + heading_has_content: set[str] = set() + + for page_index, classified_blocks in enumerate(classified_pages): + for cb in classified_blocks: + if cb.disposition == Disposition.GENUINE_SECTION_HEADER: + continue + + block = cb.unwrapped.block + sh: dict[str, str] = _get_section_hierarchy(block) + if not sh: + continue + + sorted_entries = _sorted_hierarchy_entries(sh) + for list_pos, (depth_int, ref_heading_id) in enumerate(sorted_entries): + if ref_heading_id not in heading_registry: + + continue + + heading_has_content.add(ref_heading_id) + + if ref_heading_id not in heading_depth: + heading_depth[ref_heading_id] = depth_int + + if depth_int > 0 and ref_heading_id not in heading_parent_id: + if list_pos > 0: + parent_heading_id = sorted_entries[list_pos - 1][1] + if parent_heading_id in heading_registry: + heading_parent_id[ref_heading_id] = parent_heading_id + + + omitted_heading_ids: set[str] = set() + for heading_id, sb in heading_registry.items(): + if heading_id not in heading_has_content: + omitted_heading_ids.add(heading_id) + logger.debug( + "stage7 step2 omitting heading marker_block_id=%s " + "(no governed content found)", + heading_id, + ) + continue + + sb.depth = heading_depth.get(heading_id, 0) + sb.kind = "section" # Schema v1.1 discriminated-union literal + + parent_id = heading_parent_id.get(heading_id) + if parent_id and parent_id not in omitted_heading_ids: + sb.parent_section_builder = heading_registry.get(parent_id) + else: + sb.parent_section_builder = None + + logger.debug( + "stage7 step2 heading marker_block_id=%s depth=%d parent=%s", + heading_id, + sb.depth, + parent_id, + ) + + + non_omitted = [ + (heading_id, sb) + for heading_id, sb in heading_registry.items() + if heading_id not in omitted_heading_ids + ] + for heading_id, sb in sorted(non_omitted, key=lambda pair: pair[1].depth or 0): + parent = sb.parent_section_builder + if parent is not None: + sb.provenance.section_path = list(parent.provenance.section_path) + [ + parent.heading_marker_block_id + ] + else: + sb.provenance.section_path = [] + + logger.debug( + "stage7 step2 complete: section_path set on %d non-omitted SectionBuilders", + len(non_omitted), + ) + +# Step 3 + mbid_to_builder: dict[str, Any] = {} + for pb in page_builders: + for builder in pb.children: + if isinstance(builder, SectionBuilder): + continue + mids = getattr(getattr(builder, "provenance", None), "marker_block_ids", []) + if mids: + mbid_to_builder[mids[0]] = builder + + # Now resolve section_path for every leaf builder. + # builder_governing_section[builder_python_id] = innermost SectionBuilder | None + builder_governing_section: dict[int, SectionBuilder | None] = {} + + for page_index, classified_blocks in enumerate(classified_pages): + for cb in classified_blocks: + if cb.disposition == Disposition.GENUINE_SECTION_HEADER: + continue + + block = cb.unwrapped.block + block_id: str = getattr(block, "id", None) + if block_id is None: + continue + + builder = mbid_to_builder.get(block_id) + if builder is None: + continue + + sh: dict[str, str] = _get_section_hierarchy(block) + if not sh: + builder.provenance.section_path = [] + builder_governing_section[id(builder)] = None + continue + + sorted_entries = _sorted_hierarchy_entries(sh) + + # Filter to heading ids actually present in our registry and not omitted. + resolved_ids: list[str] = [] + innermost_sb: SectionBuilder | None = None + for _depth, heading_id in sorted_entries: + if heading_id in heading_registry and heading_id not in omitted_heading_ids: + resolved_ids.append(heading_id) + innermost_sb = heading_registry[heading_id] + + builder.provenance.section_path = resolved_ids + builder_governing_section[id(builder)] = innermost_sb + + logger.debug("stage7 step3 complete: section_path populated on all leaf builders") + +#step 4 + for pb in page_builders: + pb.children = [] + + builder_page_index: dict[int, int] = {} + for page_index, classified_blocks in enumerate(classified_pages): + for cb in classified_blocks: + if cb.disposition == Disposition.GENUINE_SECTION_HEADER: + continue + block = cb.unwrapped.block + block_id = getattr(block, "id", None) + if block_id is None: + continue + builder = mbid_to_builder.get(block_id) + if builder is None: + continue + builder_page_index[id(builder)] = page_index + + # Place each leaf builder into its governing SectionBuilder or its page. + for py_id, governing_sb in builder_governing_section.items(): + pass # handled in the loop below + + # Rebuild: python id → builder object (needed to retrieve from builder_governing_section) + pyid_to_builder: dict[int, Any] = { + id(b): b for b in mbid_to_builder.values() + } + + for py_id, governing_sb in builder_governing_section.items(): + builder = pyid_to_builder.get(py_id) + if builder is None: + continue # safety guard + is_page_furniture = type(builder).__name__ in ( + "PageHeaderBuilder", "PageFooterBuilder" + ) + + if governing_sb is not None and not is_page_furniture: + governing_sb.children.append(builder) + else: + if is_page_furniture: + builder.provenance.section_path = [] + page_idx = builder_page_index.get(py_id, 0) + page_builders[page_idx].children.append(builder) + + logger.debug("stage7 step4 complete: leaf builders placed into parents") + + #Step 5 + + for heading_id, sb in heading_registry.items(): + if heading_id in omitted_heading_ids: + continue + + if sb.parent_section_builder is not None: + sb.parent_section_builder.children.append(sb) + else: + # Top-level section (depth 0): attach to the PageBuilder of the + # page where the heading block appears. + page_idx = heading_page_index.get(heading_id, 0) + page_builders[page_idx].children.append(sb) + + logger.debug("stage7 step5 complete: SectionBuilders nested") + + # ── Step 6 + mbid_to_seq: dict[str, tuple[int, int]] = {} + for page_index, classified_blocks in enumerate(classified_pages): + for seq_pos, cb in enumerate(classified_blocks): + block_id = getattr(cb.unwrapped.block, "id", None) + if block_id is not None: + mbid_to_seq[block_id] = (page_index, seq_pos) + + def _sort_key(item: Any) -> tuple[int, int]: + if isinstance(item, SectionBuilder): + hid = item.heading_marker_block_id + return mbid_to_seq.get(hid, (0, 0)) + else: + # Leaf builder: use its first Marker block id. + mids = getattr(getattr(item, "provenance", None), "marker_block_ids", []) + if mids: + return mbid_to_seq.get(mids[0], (0, 0)) + return (0, 0) + + def _sort_children(children: list[Any]) -> None: + children.sort(key=_sort_key) + + # Sort each PageBuilder.children. + for pb in page_builders: + _sort_children(pb.children) + + # Sort each SectionBuilder.children (all non-omitted sections). + for heading_id, sb in heading_registry.items(): + if heading_id in omitted_heading_ids: + continue + _sort_children(sb.children) + + logger.debug("stage7 step6 complete: all children lists sorted by classified-sequence position") + logger.debug( + "stage7 complete: %d sections registered, %d omitted", + len(heading_registry) - len(omitted_heading_ids), + len(omitted_heading_ids), + ) \ No newline at end of file diff --git a/src/betydb_extraction/normalizer/internal/stage8.py b/src/betydb_extraction/normalizer/internal/stage8.py new file mode 100644 index 0000000..e066994 --- /dev/null +++ b/src/betydb_extraction/normalizer/internal/stage8.py @@ -0,0 +1,51 @@ +""" +Stage 8: Global Reading-Order Assignment. +""" + +from __future__ import annotations + +import logging +from typing import Any + +from betydb_extraction.normalizer.builders.page import PageBuilder +from betydb_extraction.normalizer.builders.section import SectionBuilder + +logger = logging.getLogger(__name__) + + +def _assign_index(builder: Any, counter: list[int]) -> None: + builder.provenance.reading_order_index = counter[0] + counter[0] += 1 + + +def _traverse(item: Any, counter: list[int]) -> None: + _assign_index(item, counter) + + if isinstance(item, SectionBuilder): + for child in item.children: + _traverse(child, counter) + else: + caption = getattr(item, "caption", None) + if caption is not None: + _assign_index(caption, counter) + + +def assign_reading_order(page_builders: list[PageBuilder]) -> None: + # Single shared counter across the entire document. + # Wrapped in a list for mutability inside nested helper functions. + counter: list[int] = [0] + + for page_builder in sorted(page_builders, key=lambda pb: pb.page_number or 0): + # Assign the PageBuilder itself first (pre-order). + _assign_index(page_builder, counter) + + # Traverse children in the order Stage 7 established. + for child in page_builder.children: + _traverse(child, counter) + + total_assigned = counter[0] + logger.debug( + "stage8 complete: reading_order_index assigned to %d builders", + total_assigned, + ) + \ No newline at end of file diff --git a/src/betydb_extraction/normalizer/internal/stage9.py b/src/betydb_extraction/normalizer/internal/stage9.py new file mode 100644 index 0000000..dba2376 --- /dev/null +++ b/src/betydb_extraction/normalizer/internal/stage9.py @@ -0,0 +1,162 @@ +""" +Stage 9: Canonical Path Computation. +""" + +from __future__ import annotations + +import logging +from typing import Any + +from betydb_extraction.normalizer.builders.page import PageBuilder +from betydb_extraction.normalizer.builders.section import SectionBuilder + +logger = logging.getLogger(__name__) + + +# --------------------------------------------------------------------------- +# Internal helpers +# --------------------------------------------------------------------------- + +def _class_name(obj: Any) -> str: + return type(obj).__name__ + + +def _first_marker_block_id(builder: Any) -> str: + mids = getattr(getattr(builder, "provenance", None), "marker_block_ids", []) + if not mids: + raise ValueError( + f"Builder {_class_name(builder)} has no marker_block_ids — " + "cannot compute canonical_path." + ) + return mids[0] + + +def _reading_order_index(builder: Any) -> int: + roi = getattr(getattr(builder, "provenance", None), "reading_order_index", None) + if roi is None: + raise ValueError( + f"Builder {_class_name(builder)} has no reading_order_index — " + "Stage 8 must run before Stage 9." + ) + return roi + + +def _set_sub_object_paths(table_builder: Any, table_path: str) -> None: + # TableRow and TableRowCell + for row_ordinal, row_builder in enumerate(getattr(table_builder, "rows", []) or []): + row_path = f"{table_path}/row/{row_ordinal}" + row_builder.canonical_path = row_path + + for cell_ordinal, cell_builder in enumerate( + getattr(row_builder, "cells", []) or [] + ): + cell_builder.canonical_path = f"{row_path}/cell/{cell_ordinal}" + + # Flat TableCell evidence (bare cell list) + for tc_builder in getattr(table_builder, "cells", []) or []: + mbid = tc_builder.marker_block_id + if mbid is None: + raise ValueError( + "Stage 9: TableCellBuilder has no marker_block_id — " + "cannot compute canonical_path." + ) + tc_builder.canonical_path = f"{table_path}/cell_evidence/{mbid}" + + # Caption + caption = getattr(table_builder, "caption", None) + if caption is not None: + caption.canonical_path = f"{table_path}/caption" + caption.provenance.section_path = list(table_builder.provenance.section_path) + + +def _set_figure_sub_paths(figure_builder: Any, figure_path: str) -> None: + caption = getattr(figure_builder, "caption", None) + if caption is not None: + caption.canonical_path = f"{figure_path}/caption" + caption.provenance.section_path = list(figure_builder.provenance.section_path) + + +def _page_path_for(builder: Any) -> str: + page_number = getattr(getattr(builder, "provenance", None), "page_number", None) + if page_number is None: + raise ValueError( + f"Stage 9: {_class_name(builder)} has no provenance.page_number — " + "cannot compute canonical_path." + ) + return f"/page/{page_number}" + + +def _set_leaf_path(builder: Any) -> None: + page_path = _page_path_for(builder) + name = _class_name(builder) + + if name == "ParagraphBuilder": + builder.canonical_path = ( + f"{page_path}/paragraph/{_reading_order_index(builder)}" + ) + + elif name == "EquationBuilder": + builder.canonical_path = ( + f"{page_path}/equation/{_reading_order_index(builder)}" + ) + + elif name == "FootnoteBuilder": + builder.canonical_path = ( + f"{page_path}/footnote/{_first_marker_block_id(builder)}" + ) + + elif name == "ReferenceBuilder": + builder.canonical_path = ( + f"{page_path}/reference/{_reading_order_index(builder)}" + ) + + elif name == "PageHeaderBuilder": + builder.canonical_path = ( + f"{page_path}/page_header/{_first_marker_block_id(builder)}" + ) + + elif name == "PageFooterBuilder": + builder.canonical_path = ( + f"{page_path}/page_footer/{_first_marker_block_id(builder)}" + ) + + elif name == "TableBuilder": + table_path = f"{page_path}/table/{_first_marker_block_id(builder)}" + builder.canonical_path = table_path + _set_sub_object_paths(builder, table_path) + + elif name == "FigureBuilder": + figure_path = f"{page_path}/figure/{_first_marker_block_id(builder)}" + builder.canonical_path = figure_path + _set_figure_sub_paths(builder, figure_path) + + else: + logger.warning( + "stage9: unrecognised builder class %r; canonical_path not set", name + ) + + +def _traverse_section(section_builder: SectionBuilder) -> None: + page_path = _page_path_for(section_builder) + heading_id = section_builder.heading_marker_block_id or "" + section_builder.canonical_path = f"{page_path}/section/{heading_id}" + + for child in section_builder.children: + if isinstance(child, SectionBuilder): + _traverse_section(child) + else: + _set_leaf_path(child) + +def compute_canonical_paths(page_builders: list[PageBuilder]) -> None: + for page_builder in sorted(page_builders, key=lambda pb: pb.page_number or 0): + page_num = page_builder.page_number + page_path = f"/page/{page_num}" + page_builder.canonical_path = page_path + + for child in page_builder.children: + if isinstance(child, SectionBuilder): + _traverse_section(child) + else: + _set_leaf_path(child) + + logger.debug("stage9 complete: canonical_path set on all builders") \ No newline at end of file diff --git a/src/betydb_extraction/normalizer/logging_util.py b/src/betydb_extraction/normalizer/logging_util.py new file mode 100644 index 0000000..7ac636b --- /dev/null +++ b/src/betydb_extraction/normalizer/logging_util.py @@ -0,0 +1,138 @@ +""" +Structured logging helpers for the Normalizer. +""" +from __future__ import annotations + +import logging +from typing import Any + +logger = logging.getLogger("normalizer") + + +def _emit(level: int, event: str, **fields: Any) -> None: + logger.log(level, event, extra={"normalizer_fields": fields}) + + +#Stage 0 + +def log_front_matter_detection( + page_index: int, + signals_fired: list[str], + is_front_matter: bool, +) -> None: + _emit( + logging.DEBUG, + "stage0.front_matter_detection", + page_index=page_index, + signals_fired=signals_fired, + is_front_matter=is_front_matter, + ) + + +#Stage 2 + +def log_picture_discarded(marker_block_id: str, page_index: int) -> None: + _emit( + logging.INFO, + "stage2.picture_discarded", + marker_block_id=marker_block_id, + page_index=page_index, + ) + + +#Stage 4 + +def log_caption_resolved( + object_kind: str, + marker_block_id: str, + pattern: str, +) -> None: + _emit( + logging.DEBUG, + "stage4.caption_resolved", + object_kind=object_kind, + marker_block_id=marker_block_id, + pattern=pattern, + ) + + +def log_caption_not_resolved( + object_kind: str, + marker_block_id: str, + reason: str, +) -> None: + _emit( + logging.INFO, + "stage4.caption_not_resolved", + object_kind=object_kind, + marker_block_id=marker_block_id, + reason=reason, + ) + + + +def log_footnote_attached( + footnote_marker_block_id: str, + target_marker_block_id: str, + target_kind: str, + page_index: int, +) -> None: + _emit( + logging.DEBUG, + "stage6.footnote_attached", + footnote_marker_block_id=footnote_marker_block_id, + target_marker_block_id=target_marker_block_id, + target_kind=target_kind, + page_index=page_index, + ) + + +def log_footnote_not_attached( + marker_block_id: str, + page_index: int, +) -> None: + _emit( + logging.INFO, + "stage6.footnote_not_attached", + marker_block_id=marker_block_id, + page_index=page_index, + ) + + +def log_footnote_missing_bbox( + marker_block_id: str, + page_index: int, +) -> None: + _emit( + logging.INFO, + "stage6.footnote_missing_bbox", + marker_block_id=marker_block_id, + page_index=page_index, + ) + + +def log_materialization_complete( + statistics: dict[str, Any], + normalizer_version: str, +) -> None: + _emit( + logging.INFO, + "stage10.materialization_complete", + statistics=statistics, + normalizer_version=normalizer_version, + ) + +def log_error_context( + stage: str, + marker_block_id: str | None, + page_index: int | None, + message: str, +) -> None: + _emit( + logging.ERROR, + "normalizer.error", + stage=stage, + marker_block_id=marker_block_id, + page_index=page_index, + message=message, + ) \ No newline at end of file diff --git a/tests/document/__init__.py b/tests/document/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/document/conftest.py b/tests/document/conftest.py new file mode 100644 index 0000000..93fd3a4 --- /dev/null +++ b/tests/document/conftest.py @@ -0,0 +1,221 @@ +""" +Shared fixtures and small construction helpers for the Document Object test suite. +""" +from __future__ import annotations + +from datetime import datetime, timezone + +import pytest + +from betydb_extraction.document import ( + BoundingBox, + Caption, + Document, + Equation, + Figure, + Footnote, + Metadata, + NodeKind, + Page, + PageFooter, + PageHeader, + Paragraph, + Polygon, + ProcessingMetadata, + Reference, + Section, + Statistics, + StructuralProvenance, + Table, + TableCell, + TableRow, + TableRowCell, + compute_document_id, + compute_object_id, +) + +TEST_DOCUMENT_ID = compute_document_id("10.1000/test-doi") + + +def make_provenance( + canonical_path: str = "/page/0/Text/0", + *, + reading_order_index: int = 0, + page_number: int = 0, + bbox: BoundingBox | None = None, + section_path: list[str] | None = None, +) -> StructuralProvenance: + return StructuralProvenance( + marker_block_ids=[canonical_path], + page_number=page_number, + bbox=bbox, + reading_order_index=reading_order_index, + section_path=section_path or [], + ) + + +def make_id(canonical_path: str) -> str: + """Compute a deterministic object id rooted at TEST_DOCUMENT_ID.""" + return compute_object_id(TEST_DOCUMENT_ID, canonical_path) + + +def make_paragraph(path: str = "/page/0/Text/0", text: str = "Body text.") -> Paragraph: + return Paragraph( + id=make_id(path), + text=text, + provenance=make_provenance(path), + ) + + +def make_footnote(path: str = "/page/0/Footnote/0") -> Footnote: + return Footnote( + id=make_id(path), + provenance=make_provenance(path), + raw_text="1. See methods for details.", + ) + + +def make_reference(path: str = "/page/9/ListItem/0") -> Reference: + return Reference( + id=make_id(path), + provenance=make_provenance(path), + raw_text="Smukler, S. et al. 2012. Nutrient cycling. J. Agron.", + ) + + +def make_equation(path: str = "/page/3/Equation/0") -> Equation: + return Equation( + id=make_id(path), + provenance=make_provenance(path), + raw_math="y = mx + b ... (1)", + ) + + +def make_caption(path: str = "/page/6/Caption/0") -> Caption: + return Caption( + label="Table 3", + text="Nutrient flux by treatment.", + provenance=make_provenance(path), + ) + + +def make_table(path: str = "/page/6/Table/0") -> Table: + return Table( + id=make_id(path), + provenance=make_provenance(path), + caption=make_caption(), + raw_html="
    1.2
    ", + rows=[TableRow(cells=[TableRowCell(text="1.2", is_header=False)])], + cells=[ + TableCell( + id=make_id(path + "/cell/0"), + text="1.2", + bbox=BoundingBox(x0=0, y0=0, x1=10, y1=10), + polygon=Polygon( + points=((0, 0), (10, 0), (10, 10), (0, 10)) + ), + ) + ], + ) + + +def make_figure(path: str = "/page/7/Figure/0") -> Figure: + return Figure( + id=make_id(path), + provenance=make_provenance(path), + caption=make_caption(path + "/Caption/0"), + ) + + +def make_page_header(path: str = "/page/0/PageHeader/0") -> PageHeader: + return PageHeader( + id=make_id(path), + provenance=make_provenance(path), + raw_text="J. Agronomic Studies", + ) + + +def make_page_footer(path: str = "/page/0/PageFooter/0") -> PageFooter: + return PageFooter( + id=make_id(path), + provenance=make_provenance(path), + raw_text="Page 1 of 12", + ) + + +def make_section( + path: str = "/page/0/SectionHeader/0", + *, + heading_text: str = "Methods", + depth: int = 0, + children: list | None = None, +) -> Section: + return Section( + id=make_id(path), + heading_text=heading_text, + provenance=make_provenance(path), + depth=depth, + children=children or [], + ) + + +def make_page( + page_number: int = 0, + *, + children: list | None = None, + is_front_matter: bool = False, +) -> Page: + path = f"/page/{page_number}" + return Page( + id=make_id(path), + page_number=page_number, + provenance=make_provenance(path, page_number=page_number), + children=children or [], + is_front_matter=is_front_matter, + ) + + +def make_statistics(**overrides) -> Statistics: + base = dict( + page_count=1, + section_count=1, + paragraph_count=1, + table_count=0, + figure_count=0, + equation_count=0, + footnote_count=0, + reference_count=0, + unresolved_footnote_count=0, + ) + base.update(overrides) + return Statistics(**base) + + +def make_metadata(**overrides) -> Metadata: + base = dict(title="A Paper", page_count=1, has_front_matter_page=False) + base.update(overrides) + return Metadata(**base) + + +def make_processing_metadata(**overrides) -> ProcessingMetadata: + base = dict( + marker_version="1.2.3", + normalizer_version="0.1.0", + processed_at=datetime(2026, 6, 17, 12, 0, 0, tzinfo=timezone.utc), + source_marker_artifact_ref="artifacts/smukler_2012.marker.json", + ) + base.update(overrides) + return ProcessingMetadata(**base) + + +def make_document(**overrides) -> Document: + base = dict( + id=TEST_DOCUMENT_ID, + source_pdf_identifier="10.1000/test-doi", + metadata=make_metadata(), + processing_metadata=make_processing_metadata(), + statistics=make_statistics(), + pages=[make_page(0, children=[make_paragraph()])], + ) + base.update(overrides) + return Document(**base) \ No newline at end of file diff --git a/tests/document/test_document.py b/tests/document/test_document.py new file mode 100644 index 0000000..f6ef4f7 --- /dev/null +++ b/tests/document/test_document.py @@ -0,0 +1,83 @@ +"""Tests for Document (Spec Section 4): the root container.""" +from __future__ import annotations + +import pytest +from pydantic import ValidationError + +from betydb_extraction.document import Document, StructuralProvenance + +from .conftest import make_document, make_page, make_paragraph + + +class TestDocument: + def test_minimal_valid_construction(self): + doc = make_document() + assert len(doc.pages) == 1 + + def test_pages_must_be_non_empty(self): + with pytest.raises(ValidationError): + make_document(pages=[]) + + def test_duplicate_page_numbers_rejected(self): + with pytest.raises(ValidationError): + make_document( + pages=[ + make_page(0, children=[make_paragraph()]), + make_page(0, children=[make_paragraph("/page/0/Text/1")]), + ] + ) + + def test_out_of_order_pages_rejected(self): + with pytest.raises(ValidationError): + make_document( + pages=[ + make_page(1, children=[make_paragraph("/page/1/Text/0")]), + make_page(0, children=[make_paragraph("/page/0/Text/0")]), + ] + ) + + def test_ascending_pages_with_gap_is_valid(self): + # Spec: a Marker-side page omission is preserved, not silently + # re-numbered -- a gap in page_number is allowed as long as the + # list itself is still ascending. + doc = make_document( + pages=[ + make_page(0, children=[make_paragraph("/page/0/Text/0")]), + make_page(2, children=[make_paragraph("/page/2/Text/0")]), + ] + ) + assert [p.page_number for p in doc.pages] == [0, 2] + + def test_multiple_ascending_pages_valid(self): + doc = make_document( + pages=[ + make_page(0, children=[make_paragraph("/page/0/Text/0")]), + make_page(1, children=[make_paragraph("/page/1/Text/0")]), + make_page(2, children=[make_paragraph("/page/2/Text/0")]), + ] + ) + assert len(doc.pages) == 3 + + def test_document_has_no_structural_provenance_field(self): + # Spec 4 invariant: Document is the only object with no + # StructuralProvenance of its own. + assert "provenance" not in Document.model_fields + for field in Document.model_fields.values(): + assert field.annotation is not StructuralProvenance + + def test_rejects_malformed_document_id(self): + with pytest.raises(ValidationError): + make_document(id="not-a-document-id") + + def test_rejects_object_id_shape_as_document_id(self): + # A doc: id (object-shaped) must not validate as a Document id. + from betydb_extraction.document.identifiers import compute_object_id + + bad_id = compute_object_id("betydoc:" + "a" * 16, "/page/0") + with pytest.raises(ValidationError): + make_document(id=bad_id) + + def test_is_frozen(self): + doc = make_document() + with pytest.raises(ValidationError): + doc.source_pdf_identifier = "different" \ No newline at end of file diff --git a/tests/document/test_identifiers.py b/tests/document/test_identifiers.py new file mode 100644 index 0000000..f89dffc --- /dev/null +++ b/tests/document/test_identifiers.py @@ -0,0 +1,108 @@ +"""Tests for betydb_extraction.document.identifiers. +""" +from __future__ import annotations + +import pytest + +from betydb_extraction.document.identifiers import ( + compute_document_id, + compute_object_id, + is_valid_document_id, + is_valid_object_id, + validate_document_id_shape, + validate_object_id_shape, +) + + +class TestDeterminism: + def test_document_id_is_deterministic(self): + assert compute_document_id("10.1000/x") == compute_document_id("10.1000/x") + + def test_document_id_differs_for_different_input(self): + assert compute_document_id("10.1000/x") != compute_document_id("10.1000/y") + + def test_object_id_is_deterministic(self): + doc_id = compute_document_id("10.1000/x") + assert compute_object_id(doc_id, "/page/0/Text/0") == compute_object_id( + doc_id, "/page/0/Text/0" + ) + + def test_object_id_differs_by_canonical_path(self): + doc_id = compute_document_id("10.1000/x") + assert compute_object_id(doc_id, "/page/0/Text/0") != compute_object_id( + doc_id, "/page/0/Text/1" + ) + + def test_object_id_differs_by_document_id(self): + doc_a = compute_document_id("10.1000/x") + doc_b = compute_document_id("10.1000/y") + assert compute_object_id(doc_a, "/page/0/Text/0") != compute_object_id( + doc_b, "/page/0/Text/0" + ) + + +class TestShape: + def test_document_id_has_expected_prefix_and_length(self): + value = compute_document_id("10.1000/x") + assert value.startswith("betydoc:") + assert len(value) == len("betydoc:") + 16 + + def test_object_id_has_expected_prefix_and_length(self): + value = compute_object_id(compute_document_id("10.1000/x"), "/page/0") + assert value.startswith("doc:") + assert len(value) == len("doc:") + 16 + + def test_is_valid_document_id_accepts_well_formed(self): + assert is_valid_document_id(compute_document_id("10.1000/x")) + + def test_is_valid_document_id_rejects_object_id(self): + doc_id = compute_document_id("10.1000/x") + obj_id = compute_object_id(doc_id, "/page/0") + assert not is_valid_document_id(obj_id) + + def test_is_valid_object_id_rejects_document_id(self): + doc_id = compute_document_id("10.1000/x") + assert not is_valid_object_id(doc_id) + + @pytest.mark.parametrize( + "bad_value", + [ + "betydoc:short", + "betydoc:" + "g" * 16, # non-hex char + "wrongprefix:" + "a" * 16, + "", + ], + ) + def test_is_valid_document_id_rejects_malformed(self, bad_value): + assert not is_valid_document_id(bad_value) + + @pytest.mark.parametrize( + "bad_value", + [ + "doc:short", + "doc:" + "G" * 16, # uppercase not allowed + "wrongprefix:" + "a" * 16, + "", + ], + ) + def test_is_valid_object_id_rejects_malformed(self, bad_value): + assert not is_valid_object_id(bad_value) + + +class TestRaisingValidators: + def test_validate_document_id_shape_returns_value_when_valid(self): + value = compute_document_id("10.1000/x") + assert validate_document_id_shape(value) == value + + def test_validate_document_id_shape_raises_when_invalid(self): + with pytest.raises(ValueError): + validate_document_id_shape("not-a-valid-id") + + def test_validate_object_id_shape_returns_value_when_valid(self): + doc_id = compute_document_id("10.1000/x") + value = compute_object_id(doc_id, "/page/0") + assert validate_object_id_shape(value) == value + + def test_validate_object_id_shape_raises_when_invalid(self): + with pytest.raises(ValueError): + validate_object_id_shape("not-a-valid-id") \ No newline at end of file diff --git a/tests/document/test_invariants.py b/tests/document/test_invariants.py new file mode 100644 index 0000000..3612978 --- /dev/null +++ b/tests/document/test_invariants.py @@ -0,0 +1,98 @@ +from __future__ import annotations + +import pytest +from pydantic import ValidationError + +from betydb_extraction.document import Section + +from .conftest import ( + make_document, + make_page, + make_paragraph, + make_reference, + make_section, + make_table, +) + + +class TestEndToEndDeterminism: + def test_identical_construction_produces_identical_ids(self): + doc_a = make_document() + doc_b = make_document() + assert doc_a.id == doc_b.id + assert doc_a.pages[0].id == doc_b.pages[0].id + + def test_identical_construction_produces_byte_identical_json(self): + doc_a = make_document() + doc_b = make_document() + assert doc_a.model_dump_json() == doc_b.model_dump_json() + + def test_full_tree_with_reference_is_deterministic(self): + def build(): + return make_document( + pages=[ + make_page( + 0, + children=[ + make_section( + "/page/0/SectionHeader/0", + heading_text="References", + children=[make_reference(), make_reference("/page/0/ListItem/1")], + ) + ], + ) + ] + ) + + doc_a, doc_b = build(), build() + assert doc_a.model_dump_json() == doc_b.model_dump_json() + + +class TestImmutabilityDepth: + + def test_top_level_assignment_blocked(self): + section = make_section() + with pytest.raises(ValidationError): + section.depth = 5 + + def test_section_children_list_itself_is_not_swappable(self): + section = make_section(children=[make_paragraph()]) + with pytest.raises(ValidationError): + section.children = [] + + def test_nested_child_object_is_independently_frozen(self): + para = make_paragraph() + section = make_section(children=[para]) + # The child retrieved from the parent is the same frozen model; + # mutating it must still fail. + with pytest.raises(ValidationError): + section.children[0].text = "mutated" + + def test_document_pages_list_itself_is_not_swappable(self): + doc = make_document() + with pytest.raises(ValidationError): + doc.pages = [] + + +class TestExtraFieldsForbidden: + + def test_section_rejects_unknown_field(self): + with pytest.raises(ValidationError): + Section( + **{ + **make_section().model_dump(), + "unexpected_field": "should not be allowed", + } + ) + + def test_table_rejects_unknown_field(self): + with pytest.raises(ValidationError): + type(make_table())( + **{**make_table().model_dump(), "unexpected_field": "x"} + ) + + def test_document_rejects_unknown_field(self): + with pytest.raises(ValidationError): + type(make_document())( + **{**make_document().model_dump(), "unexpected_field": "x"} + ) \ No newline at end of file diff --git a/tests/document/test_leaf_models.py b/tests/document/test_leaf_models.py new file mode 100644 index 0000000..d33130f --- /dev/null +++ b/tests/document/test_leaf_models.py @@ -0,0 +1,219 @@ +"""Tests for the leaf and supporting content models: Paragraph, Caption, +Table (+ TableRow/TableRowCell/TableCell), Figure, Equation, Footnote, +Reference, PageHeader, PageFooter. +""" +from __future__ import annotations + +import pytest +from pydantic import ValidationError + +from betydb_extraction.document import ( + Caption, + Equation, + Figure, + Footnote, + NodeKind, + PageFooter, + PageHeader, + Paragraph, + Reference, + Table, + TableRow, + TableRowCell, +) + +from .conftest import ( + make_caption, + make_equation, + make_footnote, + make_id, + make_page_footer, + make_page_header, + make_paragraph, + make_provenance, + make_reference, + make_table, +) + + +class TestParagraph: + def test_constructs_with_kind_default(self): + p = make_paragraph() + assert p.kind == NodeKind.PARAGRAPH + + def test_preserves_inline_html_verbatim(self): + p = make_paragraph(text="See Table 3 for details.") + assert p.text == "See Table 3 for details." + + def test_rejects_malformed_id(self): + with pytest.raises(ValidationError): + Paragraph(id="not-an-id", text="x", provenance=make_provenance()) + + def test_is_frozen(self): + p = make_paragraph() + with pytest.raises(ValidationError): + p.text = "different" + + +class TestCaption: + def test_all_text_fields_optional(self): + cap = Caption(provenance=make_provenance()) + assert cap.label is None + assert cap.text is None + assert cap.trailing_notes is None + + def test_pattern_a_shape(self): + cap = make_caption() + assert cap.label == "Table 3" + assert cap.text is not None + + def test_has_no_kind_discriminator(self): + # Caption is embedded only, never a children-union member. + assert not hasattr(Caption, "model_fields") or "kind" not in Caption.model_fields + + +class TestTable: + def test_constructs_with_kind_default(self): + t = make_table() + assert t.kind == NodeKind.TABLE + + def test_row_with_zero_cells_rejected(self): + with pytest.raises(ValidationError): + TableRow(cells=[]) + + def test_rows_may_be_empty_list(self): + t = Table( + id=make_id("/page/6/Table/1"), + provenance=make_provenance("/page/6/Table/1"), + raw_html="
    ", + ) + assert t.rows == [] + + def test_caption_optional(self): + t = Table( + id=make_id("/page/6/Table/2"), + provenance=make_provenance("/page/6/Table/2"), + raw_html="
    ", + ) + assert t.caption is None + + def test_table_row_cell_math_stripped_text_is_plain_field(self): + cell = TableRowCell(text="1.2 +/- 0.3", is_header=False) + assert cell.text == "1.2 +/- 0.3" + + def test_table_row_cell_has_no_row_col_index_fields(self): + assert "row_index" not in TableRowCell.model_fields + assert "col_index" not in TableRowCell.model_fields + + def test_table_row_cell_has_no_span_fields(self): + assert "rowspan" not in TableRowCell.model_fields + assert "colspan" not in TableRowCell.model_fields + + +class TestFigure: + def test_constructs_with_kind_default(self): + from .conftest import make_figure + + fig = make_figure() + assert fig.kind == NodeKind.FIGURE + + def test_image_data_optional(self): + fig = Figure( + id=make_id("/page/7/Figure/1"), + provenance=make_provenance("/page/7/Figure/1"), + ) + assert fig.image_data is None + + +class TestEquation: + def test_constructs_with_kind_default(self): + eq = make_equation() + assert eq.kind == NodeKind.EQUATION + + def test_equation_number_is_separate_optional_slot(self): + eq = Equation( + id=make_id("/page/3/Equation/1"), + provenance=make_provenance("/page/3/Equation/1"), + raw_math="E = mc^2", + equation_number="2", + ) + assert eq.equation_number == "2" + assert "(2)" not in eq.raw_math # number is a separate field here + + def test_raw_math_can_embed_number_inline(self): + # Per spec: Marker provides no separate field, so raw_math may + # contain the number embedded in the string itself. + eq = Equation( + id=make_id("/page/3/Equation/2"), + provenance=make_provenance("/page/3/Equation/2"), + raw_math="y = mx + b ... (1)", + ) + assert "(1)" in eq.raw_math + assert eq.equation_number is None + + +class TestFootnote: + def test_constructs_with_kind_default(self): + fn = make_footnote() + assert fn.kind == NodeKind.FOOTNOTE + + def test_attached_object_id_defaults_to_none(self): + fn = make_footnote() + assert fn.attached_object_id is None + + def test_unresolved_attachment_does_not_fail_validation(self): + # Spec 20: no validation forces attached_object_id to be set. + fn = Footnote( + id=make_id("/page/0/Footnote/1"), + provenance=make_provenance("/page/0/Footnote/1"), + raw_text="2. Another note.", + attached_object_id=None, + ) + assert fn.attached_object_id is None + + def test_attached_object_id_can_be_set(self): + fn = Footnote( + id=make_id("/page/0/Footnote/2"), + provenance=make_provenance("/page/0/Footnote/2"), + raw_text="3. Yet another.", + attached_object_id=make_id("/page/0/Table/0"), + ) + assert fn.attached_object_id is not None + + +class TestReference: + """Reference per Spec Section 16, Version 1.1 (kind discriminator added).""" + + def test_constructs_with_kind_default(self): + ref = make_reference() + assert ref.kind == NodeKind.REFERENCE + + def test_kind_is_required_in_field_set(self): + assert "kind" in Reference.model_fields + + def test_raw_text_preserved_verbatim(self): + ref = make_reference() + assert "Smukler" in ref.raw_text + + def test_is_frozen(self): + ref = make_reference() + with pytest.raises(ValidationError): + ref.raw_text = "different" + + def test_rejects_malformed_id(self): + with pytest.raises(ValidationError): + Reference(id="bad-id", provenance=make_provenance(), raw_text="x") + + +class TestPageHeaderFooter: + def test_page_header_constructs_with_kind_default(self): + h = make_page_header() + assert h.kind == NodeKind.PAGE_HEADER + + def test_page_footer_constructs_with_kind_default(self): + f = make_page_footer() + assert f.kind == NodeKind.PAGE_FOOTER + + def test_distinct_types_not_merged(self): + assert PageHeader is not PageFooter + assert PageHeader.model_fields["kind"].default != PageFooter.model_fields["kind"].default \ No newline at end of file diff --git a/tests/document/test_metadata_and_statistics.py b/tests/document/test_metadata_and_statistics.py new file mode 100644 index 0000000..fb9af6a --- /dev/null +++ b/tests/document/test_metadata_and_statistics.py @@ -0,0 +1,117 @@ +"""Tests for Statistics (Spec Section 7), Metadata (Spec Section 5), and +ProcessingMetadata (Spec Section 6). +""" +from __future__ import annotations + +from datetime import datetime, timedelta, timezone + +import pytest +from pydantic import ValidationError + +from betydb_extraction.document import Metadata, ProcessingMetadata, Statistics + +from .conftest import make_metadata, make_processing_metadata, make_statistics + + +class TestStatistics: + def test_minimal_valid_construction(self): + stats = make_statistics() + assert stats.page_count == 1 + + def test_all_counts_must_be_non_negative(self): + for field in [ + "page_count", + "section_count", + "paragraph_count", + "table_count", + "figure_count", + "equation_count", + "footnote_count", + "reference_count", + "unresolved_footnote_count", + ]: + with pytest.raises(ValidationError): + make_statistics(**{field: -1}) + + def test_unresolved_cannot_exceed_total_footnotes(self): + with pytest.raises(ValidationError): + make_statistics(footnote_count=2, unresolved_footnote_count=3) + + def test_unresolved_equal_to_total_is_valid(self): + stats = make_statistics(footnote_count=2, unresolved_footnote_count=2) + assert stats.unresolved_footnote_count == 2 + + def test_unresolved_less_than_total_is_valid(self): + stats = make_statistics(footnote_count=5, unresolved_footnote_count=2) + assert stats.unresolved_footnote_count == 2 + + def test_reference_count_field_exists(self): + # Direct regression guard for the original v1.0 omission this + # whole correction was about: Statistics always had this field, + # and it must still be present and independently settable. + stats = make_statistics(reference_count=7) + assert stats.reference_count == 7 + + def test_is_frozen(self): + stats = make_statistics() + with pytest.raises(ValidationError): + stats.page_count = 99 + + +class TestMetadata: + def test_minimal_valid_construction(self): + meta = make_metadata() + assert meta.page_count == 1 + + def test_title_optional(self): + meta = Metadata(page_count=1, has_front_matter_page=False) + assert meta.title is None + + def test_page_count_non_negative(self): + with pytest.raises(ValidationError): + make_metadata(page_count=-1) + + def test_has_no_author_journal_year_doi_fields(self): + # Spec 5's boundary note: these are explicitly NOT modeled here. + for forbidden_field in ["author", "authors", "journal", "year", "doi"]: + assert forbidden_field not in Metadata.model_fields + + def test_is_frozen(self): + meta = make_metadata() + with pytest.raises(ValidationError): + meta.title = "Different Title" + + +class TestProcessingMetadata: + def test_minimal_valid_construction(self): + pm = make_processing_metadata() + assert pm.marker_version == "1.2.3" + + def test_naive_datetime_rejected(self): + with pytest.raises(ValidationError): + make_processing_metadata(processed_at=datetime(2026, 6, 17, 12, 0, 0)) + + def test_non_utc_offset_rejected(self): + offset_tz = timezone(timedelta(hours=5)) + with pytest.raises(ValidationError): + make_processing_metadata( + processed_at=datetime(2026, 6, 17, 12, 0, 0, tzinfo=offset_tz) + ) + + def test_utc_datetime_accepted(self): + pm = make_processing_metadata( + processed_at=datetime(2026, 6, 17, 12, 0, 0, tzinfo=timezone.utc) + ) + assert pm.processed_at.tzinfo is not None + + def test_processed_at_not_part_of_any_id(self): + # Spec 6: processed_at is explicitly excluded from id computation. + # This is really a Document/identifiers-level guarantee, but we + # confirm here that ProcessingMetadata itself exposes no id field + # derived from processed_at. + assert "id" not in ProcessingMetadata.model_fields + + def test_is_frozen(self): + pm = make_processing_metadata() + with pytest.raises(ValidationError): + pm.marker_version = "9.9.9" \ No newline at end of file diff --git a/tests/document/test_provenance.py b/tests/document/test_provenance.py new file mode 100644 index 0000000..891e226 --- /dev/null +++ b/tests/document/test_provenance.py @@ -0,0 +1,147 @@ +"""Tests for betydb_extraction.document.provenance. +""" +from __future__ import annotations + +import pytest +from pydantic import ValidationError + +from betydb_extraction.document.provenance import ( + BoundingBox, + Polygon, + StructuralProvenance, +) + + +class TestBoundingBox: + def test_valid_box_constructs(self): + box = BoundingBox(x0=0, y0=0, x1=10, y1=20) + assert (box.x0, box.y0, box.x1, box.y1) == (0, 0, 10, 20) + + def test_zero_area_box_is_allowed(self): + # Spec 20 only requires x1 >= x0 and y1 >= y0, not strict >. + box = BoundingBox(x0=5, y0=5, x1=5, y1=5) + assert box.x1 == box.x0 + + def test_inverted_x_axis_rejected(self): + with pytest.raises(ValidationError): + BoundingBox(x0=10, y0=0, x1=0, y1=10) + + def test_inverted_y_axis_rejected(self): + with pytest.raises(ValidationError): + BoundingBox(x0=0, y0=10, x1=10, y1=0) + + def test_is_frozen(self): + box = BoundingBox(x0=0, y0=0, x1=1, y1=1) + with pytest.raises(ValidationError): + box.x0 = 5 + + def test_extra_fields_forbidden(self): + with pytest.raises(ValidationError): + BoundingBox(x0=0, y0=0, x1=1, y1=1, z0=0) + + +class TestPolygon: + def test_valid_polygon_constructs(self): + poly = Polygon(points=((0, 0), (10, 0), (10, 10), (0, 10))) + assert len(poly.points) == 4 + + def test_wrong_point_count_rejected(self): + with pytest.raises(ValidationError): + Polygon(points=((0, 0), (10, 0), (10, 10))) + + def test_is_frozen(self): + poly = Polygon(points=((0, 0), (10, 0), (10, 10), (0, 10))) + with pytest.raises(ValidationError): + poly.points = ((0, 0), (1, 0), (1, 1), (0, 1)) + + +class TestStructuralProvenance: + def test_minimal_valid_construction(self): + prov = StructuralProvenance( + marker_block_ids=["/page/0/Text/0"], + page_number=0, + reading_order_index=0, + ) + assert prov.bbox is None + assert prov.contributing_bboxes is None + assert prov.section_path == [] + + def test_empty_marker_block_ids_rejected(self): + with pytest.raises(ValidationError): + StructuralProvenance( + marker_block_ids=[], + page_number=0, + reading_order_index=0, + ) + + def test_negative_reading_order_index_rejected(self): + with pytest.raises(ValidationError): + StructuralProvenance( + marker_block_ids=["/page/0/Text/0"], + page_number=0, + reading_order_index=-1, + ) + + def test_bbox_and_contributing_bboxes_mutually_exclusive(self): + box = BoundingBox(x0=0, y0=0, x1=1, y1=1) + with pytest.raises(ValidationError): + StructuralProvenance( + marker_block_ids=["/page/0/Text/0"], + page_number=0, + reading_order_index=0, + bbox=box, + contributing_bboxes=[box], + ) + + def test_bbox_alone_is_valid(self): + box = BoundingBox(x0=0, y0=0, x1=1, y1=1) + prov = StructuralProvenance( + marker_block_ids=["/page/0/Text/0"], + page_number=0, + reading_order_index=0, + bbox=box, + ) + assert prov.bbox == box + + def test_contributing_bboxes_alone_is_valid(self): + box = BoundingBox(x0=0, y0=0, x1=1, y1=1) + prov = StructuralProvenance( + marker_block_ids=["/page/0/SectionHeader/0", "/page/0/Text/1"], + page_number=0, + reading_order_index=0, + contributing_bboxes=[box, box], + ) + assert len(prov.contributing_bboxes) == 2 + + def test_neither_bbox_field_is_valid(self): + # No recoverable geometry is a legitimate outcome (Section 3.3). + prov = StructuralProvenance( + marker_block_ids=["/page/0/Text/0"], + page_number=0, + reading_order_index=0, + ) + assert prov.bbox is None and prov.contributing_bboxes is None + + def test_section_path_preserves_order(self): + prov = StructuralProvenance( + marker_block_ids=["/page/7/TableCell/3"], + page_number=7, + reading_order_index=42, + section_path=[ + "/page/1/SectionHeader/1", + "/page/7/SectionHeader/0", + ], + ) + assert prov.section_path == [ + "/page/1/SectionHeader/1", + "/page/7/SectionHeader/0", + ] + + def test_is_frozen(self): + prov = StructuralProvenance( + marker_block_ids=["/page/0/Text/0"], + page_number=0, + reading_order_index=0, + ) + with pytest.raises(ValidationError): + prov.page_number = 1 \ No newline at end of file diff --git a/tests/document/test_section_and_page.py b/tests/document/test_section_and_page.py new file mode 100644 index 0000000..b8399c6 --- /dev/null +++ b/tests/document/test_section_and_page.py @@ -0,0 +1,192 @@ +"""Tests for Section (Spec Section 9) and Page (Spec Section 8), with +specific focus on the Version 1.1 correction: +""" +from __future__ import annotations + +import pytest +from pydantic import TypeAdapter, ValidationError + +from betydb_extraction.document import ( + NodeKind, + Page, + PageChild, + Paragraph, + Reference, + Section, + SectionChild, + Table, +) + +from .conftest import ( + make_equation, + make_figure, + make_footnote, + make_page, + make_page_footer, + make_page_header, + make_paragraph, + make_provenance, + make_reference, + make_section, + make_table, +) + + +class TestSectionChildUnionIncludesReference: + """The Version 1.1 correction, verified directly.""" + + def test_reference_is_a_valid_section_child_by_direct_construction(self): + ref = make_reference() + section = make_section( + heading_text="References", + children=[ref], + ) + assert len(section.children) == 1 + assert isinstance(section.children[0], Reference) + assert section.children[0].kind == NodeKind.REFERENCE + + def test_section_with_mixed_children_including_reference(self): + section = make_section( + heading_text="References", + children=[make_paragraph(), make_reference(), make_reference("/page/9/ListItem/1")], + ) + kinds = [child.kind for child in section.children] + assert kinds == [NodeKind.PARAGRAPH, NodeKind.REFERENCE, NodeKind.REFERENCE] + + def test_discriminated_union_resolves_reference_from_dict(self): + # Simulates what happens when a Section is built from raw dict/JSON + # data (e.g. round-tripped) -- the discriminator must correctly + # route a 'reference' kind to the Reference model rather than + # raising or silently coercing to a different type. + ref = make_reference() + section = Section( + id=make_section().id, + heading_text="References", + provenance=make_provenance("/page/9/SectionHeader/0"), + depth=0, + children=[ref.model_dump()], + ) + assert isinstance(section.children[0], Reference) + + def test_section_child_type_adapter_accepts_reference(self): + adapter = TypeAdapter(SectionChild) + ref = make_reference() + resolved = adapter.validate_python(ref.model_dump()) + assert isinstance(resolved, Reference) + + def test_nested_sections_still_work_alongside_reference(self): + inner = make_section( + "/page/9/SectionHeader/1", + heading_text="References", + depth=1, + children=[make_reference()], + ) + outer = make_section( + "/page/9/SectionHeader/0", + heading_text="Back Matter", + depth=0, + children=[inner], + ) + assert isinstance(outer.children[0], Section) + assert isinstance(outer.children[0].children[0], Reference) + + def test_all_pre_v1_1_child_types_still_valid(self): + # Guards against the v1.1 change accidentally narrowing the union + # instead of only widening it. + section = make_section( + children=[ + make_paragraph(), + make_table(), + make_figure(), + make_equation(), + make_footnote(), + ] + ) + kinds = {child.kind for child in section.children} + assert kinds == { + NodeKind.PARAGRAPH, + NodeKind.TABLE, + NodeKind.FIGURE, + NodeKind.EQUATION, + NodeKind.FOOTNOTE, + } + + +class TestPageChildUnionExcludesReference: + + def test_reference_is_not_in_page_child_union_members(self): + adapter = TypeAdapter(PageChild) + ref = make_reference() + with pytest.raises(ValidationError): + adapter.validate_python(ref.model_dump()) + + def test_page_rejects_reference_as_direct_child(self): + ref = make_reference() + with pytest.raises(ValidationError): + make_page(children=[ref]) + + def test_page_accepts_section_header_footer_and_all_pre_v1_1_types(self): + page = make_page( + children=[ + make_page_header(), + make_section(children=[make_paragraph()]), + make_table(), + make_figure(), + make_equation(), + make_footnote(), + make_page_footer(), + ] + ) + assert len(page.children) == 7 + + +class TestSection: + def test_constructs_with_kind_default(self): + section = make_section() + assert section.kind == NodeKind.SECTION + + def test_children_default_to_empty_list(self): + section = make_section() + assert section.children == [] + + def test_depth_must_be_non_negative(self): + with pytest.raises(ValidationError): + make_section(depth=-1) + + def test_is_frozen(self): + section = make_section() + with pytest.raises(ValidationError): + section.heading_text = "Different" + + def test_rejects_malformed_id(self): + with pytest.raises(ValidationError): + Section( + id="not-an-id", + heading_text="Methods", + provenance=make_provenance(), + depth=0, + ) + + +class TestPage: + def test_page_number_must_be_non_negative(self): + with pytest.raises(ValidationError): + make_page(page_number=-1) + + def test_children_default_to_empty_list(self): + page = make_page() + assert page.children == [] + + def test_is_front_matter_required(self): + with pytest.raises(ValidationError): + Page( + id=make_page().id, + page_number=0, + provenance=make_provenance("/page/0"), + children=[], + ) + + def test_is_frozen(self): + page = make_page() + with pytest.raises(ValidationError): + page.is_front_matter = True \ No newline at end of file diff --git a/tests/document/test_serialization.py b/tests/document/test_serialization.py new file mode 100644 index 0000000..3b6c3e4 --- /dev/null +++ b/tests/document/test_serialization.py @@ -0,0 +1,160 @@ +"""Serialization tests per Spec invariant 1.5 and Section 19: +""" +from __future__ import annotations + +import base64 + +from betydb_extraction.document import Figure + +from .conftest import ( + make_document, + make_equation, + make_footnote, + make_metadata, + make_page, + make_paragraph, + make_processing_metadata, + make_provenance, + make_reference, + make_section, + make_statistics, + make_table, +) + + +def _round_trip_dict(model): + cls = type(model) + dumped = model.model_dump() + rebuilt = cls.model_validate(dumped) + assert rebuilt == model + return dumped + + +def _round_trip_json(model): + cls = type(model) + dumped_json = model.model_dump_json() + rebuilt = cls.model_validate_json(dumped_json) + assert rebuilt == model + return dumped_json + + +class TestRoundTripDictAndJson: + def test_paragraph_round_trips(self): + p = make_paragraph() + _round_trip_dict(p) + _round_trip_json(p) + + def test_reference_round_trips(self): + ref = make_reference() + _round_trip_dict(ref) + _round_trip_json(ref) + + def test_table_round_trips(self): + t = make_table() + _round_trip_dict(t) + _round_trip_json(t) + + def test_equation_round_trips(self): + eq = make_equation() + _round_trip_dict(eq) + _round_trip_json(eq) + + def test_footnote_round_trips(self): + fn = make_footnote() + _round_trip_dict(fn) + _round_trip_json(fn) + + def test_section_with_reference_child_round_trips(self): + # Specifically exercises the v1.1-corrected union through a full + # JSON round trip, not just direct Python construction. + section = make_section( + heading_text="References", + children=[make_reference(), make_reference("/page/9/ListItem/1")], + ) + dumped = _round_trip_dict(section) + assert dumped["children"][0]["kind"] == "reference" + _round_trip_json(section) + + def test_page_round_trips(self): + page = make_page(children=[make_paragraph(), make_table()]) + _round_trip_dict(page) + _round_trip_json(page) + + def test_statistics_round_trips(self): + stats = make_statistics() + _round_trip_dict(stats) + _round_trip_json(stats) + + def test_metadata_round_trips(self): + meta = make_metadata() + _round_trip_dict(meta) + _round_trip_json(meta) + + def test_processing_metadata_round_trips_with_utc_datetime(self): + pm = make_processing_metadata() + dumped_json = _round_trip_json(pm) + assert "2026-06-17" in dumped_json + + def test_document_round_trips(self): + doc = make_document() + _round_trip_dict(doc) + _round_trip_json(doc) + + def test_document_with_full_tree_round_trips(self): + doc = make_document( + pages=[ + make_page( + 0, + children=[ + make_section( + "/page/0/SectionHeader/0", + heading_text="Methods", + children=[make_paragraph(), make_table()], + ), + make_section( + "/page/0/SectionHeader/1", + heading_text="References", + children=[make_reference()], + ), + ], + ) + ] + ) + _round_trip_dict(doc) + _round_trip_json(doc) + + +class TestFieldOrderingDeterminism: + def test_dump_json_field_order_matches_declaration_order(self): + p = make_paragraph() + dumped = p.model_dump_json() + # Declared order in Paragraph: kind, id, text, provenance. + assert dumped.index('"kind"') < dumped.index('"id"') < dumped.index( + '"text"' + ) < dumped.index('"provenance"') + + def test_repeated_dumps_are_byte_identical(self): + p = make_paragraph() + assert p.model_dump_json() == p.model_dump_json() + + +class TestBytesFieldSerialization: + def test_figure_image_data_serializes_as_base64_string(self): + raw_bytes = b"\x89PNG\r\n\x1a\nfake-bytes" + fig = Figure( + id=make_paragraph().id, # any well-formed id is fine here + provenance=make_provenance("/page/7/Figure/9"), + image_data=raw_bytes, + ) + dumped_json = fig.model_dump_json() + # Pydantic v2 base64-encodes bytes fields in JSON mode by default. + rebuilt = Figure.model_validate_json(dumped_json) + assert rebuilt.image_data == raw_bytes + + def test_figure_with_none_image_data_round_trips(self): + fig = Figure( + id=make_paragraph().id, + provenance=make_provenance("/page/7/Figure/10"), + ) + rebuilt = Figure.model_validate_json(fig.model_dump_json()) + assert rebuilt.image_data is None \ No newline at end of file diff --git a/tests/marker_adapter/__init__.py b/tests/marker_adapter/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/marker_adapter/test_raw_model.py b/tests/marker_adapter/test_raw_model.py new file mode 100644 index 0000000..3050d68 --- /dev/null +++ b/tests/marker_adapter/test_raw_model.py @@ -0,0 +1,397 @@ +from __future__ import annotations + +import json +from pathlib import Path + +import pytest + +from betydb_extraction.marker_adapter.raw_model import ( + MarkerBBox, + MarkerBlock, + MarkerDocument, + MarkerPolygonPoint, +) + +REAL_FIXTURE_PATH = Path( + "/mnt/user-data/uploads/1781681908897_Nutrient-cycling.json" +) + + +# --------------------------------------------------------------------------- +# Fixtures / helpers +# --------------------------------------------------------------------------- + + +def _minimal_leaf_block( + block_id: str = "/page/0/Text/0", + block_type: str = "Text", + html: str = "

    hello

    ", +) -> dict: + """A minimal, valid leaf block dict matching Marker's observed envelope.""" + return { + "id": block_id, + "block_type": block_type, + "html": html, + "polygon": [[0.0, 0.0], [100.0, 0.0], [100.0, 10.0], [0.0, 10.0]], + "bbox": [0.0, 0.0, 100.0, 10.0], + "children": None, + "section_hierarchy": {}, + "images": {}, + } + + +def _minimal_container_block( + block_id: str, + block_type: str, + children: list[dict], +) -> dict: + """A minimal, valid container block dict wrapping the given children.""" + return { + "id": block_id, + "block_type": block_type, + "html": "".join(f"" for c in children), + "polygon": [[0.0, 0.0], [200.0, 0.0], [200.0, 200.0], [0.0, 200.0]], + "bbox": [0.0, 0.0, 200.0, 200.0], + "children": children, + "section_hierarchy": {}, + "images": {}, + } + + +@pytest.fixture(scope="module") +def real_marker_json() -> dict: + if not REAL_FIXTURE_PATH.exists(): + pytest.skip(f"Real Marker fixture not present at {REAL_FIXTURE_PATH}") + with open(REAL_FIXTURE_PATH) as f: + return json.load(f) + + +# --------------------------------------------------------------------------- +# 1. Construction / validation -- synthetic inputs +# --------------------------------------------------------------------------- + + +class TestMarkerPolygonPoint: + def test_construct_from_pair(self): + point = MarkerPolygonPoint.from_pair([12.5, 34.0]) + assert point.x == 12.5 + assert point.y == 34.0 + + def test_to_pair_round_trip(self): + point = MarkerPolygonPoint(x=1.0, y=2.0) + assert point.to_pair() == [1.0, 2.0] + + def test_is_frozen(self): + point = MarkerPolygonPoint(x=1.0, y=2.0) + with pytest.raises(Exception): + point.x = 5.0 # type: ignore[misc] + + def test_rejects_extra_fields(self): + with pytest.raises(Exception): + MarkerPolygonPoint(x=1.0, y=2.0, z=3.0) # type: ignore[call-arg] + + +class TestMarkerBBox: + def test_construct_from_list(self): + bbox = MarkerBBox.from_list([0.0, 0.0, 10.0, 20.0]) + assert (bbox.x0, bbox.y0, bbox.x1, bbox.y1) == (0.0, 0.0, 10.0, 20.0) + + def test_to_list_round_trip(self): + bbox = MarkerBBox(x0=0.0, y0=1.0, x1=2.0, y1=3.0) + assert bbox.to_list() == [0.0, 1.0, 2.0, 3.0] + + def test_is_frozen(self): + bbox = MarkerBBox(x0=0.0, y0=0.0, x1=1.0, y1=1.0) + with pytest.raises(Exception): + bbox.x0 = 99.0 # type: ignore[misc] + + +class TestMarkerBlockConstruction: + def test_construct_minimal_leaf(self): + block = MarkerBlock.model_validate(_minimal_leaf_block()) + assert block.block_type == "Text" + assert block.is_leaf() + assert block.children is None + + def test_construct_container_with_children(self): + leaf = _minimal_leaf_block() + container = _minimal_container_block( + "/page/0/TableGroup/1", "TableGroup", [leaf] + ) + block = MarkerBlock.model_validate(container) + assert not block.is_leaf() + assert len(block.children) == 1 + assert block.children[0].block_type == "Text" + + def test_polygon_coerced_from_raw_pairs(self): + block = MarkerBlock.model_validate(_minimal_leaf_block()) + assert isinstance(block.polygon[0], MarkerPolygonPoint) + assert block.polygon[0].x == 0.0 + assert block.polygon[0].y == 0.0 + + def test_bbox_coerced_from_raw_list(self): + block = MarkerBlock.model_validate(_minimal_leaf_block()) + assert isinstance(block.bbox, MarkerBBox) + assert block.bbox.x1 == 100.0 + + def test_root_document_block_with_no_id_or_geometry(self): + data = { + "children": [_minimal_leaf_block()], + "block_type": "Document", + } + block = MarkerBlock.model_validate(data) + assert block.id is None + assert block.bbox is None + assert block.polygon is None + assert block.section_hierarchy == {} + assert len(block.children) == 1 + + def test_unknown_block_type_parses_successfully(self): + data = _minimal_leaf_block(block_type="SomeFutureBlockType") + block = MarkerBlock.model_validate(data) + assert block.block_type == "SomeFutureBlockType" + + def test_unknown_extra_field_is_preserved_not_dropped(self): + data = _minimal_leaf_block() + data["confidence_score"] = 0.987 # hypothetical future Marker field + block = MarkerBlock.model_validate(data) + dumped = block.model_dump(mode="json") + assert dumped["confidence_score"] == 0.987 + + def test_missing_required_block_type_raises(self): + data = _minimal_leaf_block() + del data["block_type"] + with pytest.raises(Exception): + MarkerBlock.model_validate(data) + + def test_is_frozen_immutable(self): + block = MarkerBlock.model_validate(_minimal_leaf_block()) + with pytest.raises(Exception): + block.html = "

    mutated

    " # type: ignore[misc] + + def test_nested_children_are_also_frozen(self): + leaf = _minimal_leaf_block() + container = _minimal_container_block( + "/page/0/TableGroup/1", "TableGroup", [leaf] + ) + block = MarkerBlock.model_validate(container) + with pytest.raises(Exception): + block.children[0].html = "

    mutated

    " # type: ignore[misc] + + def test_iter_descendants_depth_first_order(self): + grandchild = _minimal_leaf_block("/page/0/Text/2", "Text") + child_container = _minimal_container_block( + "/page/0/Caption/1", "Caption", [grandchild] + ) + root_container = _minimal_container_block( + "/page/0/TableGroup/0", "TableGroup", [child_container] + ) + block = MarkerBlock.model_validate(root_container) + descendants = block.iter_descendants() + assert [d.id for d in descendants] == [ + "/page/0/Caption/1", + "/page/0/Text/2", + ] + + def test_iter_descendants_empty_for_leaf(self): + block = MarkerBlock.model_validate(_minimal_leaf_block()) + assert block.iter_descendants() == [] + + def test_images_dict_preserved(self): + data = _minimal_leaf_block(block_type="Picture") + data["images"] = {"/page/0/Picture/0": "base64fakepayload=="} + block = MarkerBlock.model_validate(data) + assert block.images == {"/page/0/Picture/0": "base64fakepayload=="} + + def test_children_none_vs_empty_list_distinction_preserved(self): + leaf_data = _minimal_leaf_block() + leaf_data["children"] = None + leaf_block = MarkerBlock.model_validate(leaf_data) + assert leaf_block.children is None + + empty_list_data = _minimal_leaf_block() + empty_list_data["children"] = [] + empty_list_block = MarkerBlock.model_validate(empty_list_data) + assert empty_list_block.children == [] + # These are deliberately NOT equal in meaning -- is_leaf() should + # only be True for the None case. + assert leaf_block.is_leaf() is True + assert empty_list_block.is_leaf() is False + + +class TestMarkerDocument: + def test_wraps_root_block(self): + leaf = _minimal_leaf_block() + root_data = _minimal_container_block("doc-root", "Document", [leaf]) + root_block = MarkerBlock.model_validate(root_data) + doc = MarkerDocument(root=root_block) + assert doc.root.block_type == "Document" + + def test_pages_property_returns_root_children(self): + page_block = _minimal_container_block("/page/0/Page/0", "Page", []) + root_data = _minimal_container_block("doc-root", "Document", [page_block]) + root_block = MarkerBlock.model_validate(root_data) + doc = MarkerDocument(root=root_block) + assert len(doc.pages) == 1 + assert doc.pages[0].block_type == "Page" + + def test_pages_property_empty_when_no_children(self): + root_block = MarkerBlock.model_validate( + {"children": None, "block_type": "Document"} + ) + doc = MarkerDocument(root=root_block) + assert doc.pages == [] + + def test_source_path_optional_and_retained(self): + root_block = MarkerBlock.model_validate( + {"children": None, "block_type": "Document"} + ) + doc = MarkerDocument(root=root_block, source_marker_json_path="/tmp/x.json") + assert doc.source_marker_json_path == "/tmp/x.json" + + def test_is_frozen(self): + root_block = MarkerBlock.model_validate( + {"children": None, "block_type": "Document"} + ) + doc = MarkerDocument(root=root_block) + with pytest.raises(Exception): + doc.source_marker_json_path = "/tmp/other.json" # type: ignore[misc] + + +# --------------------------------------------------------------------------- +# 2. Construction / validation -- real Marker output (ground truth) +# --------------------------------------------------------------------------- + + +class TestRealMarkerDocument: + def test_parses_without_error(self, real_marker_json): + block = MarkerBlock.model_validate(real_marker_json) + assert block.block_type == "Document" + + def test_root_has_no_id(self, real_marker_json): + """Confirmed empirical fact: the real root block has no `id` field.""" + block = MarkerBlock.model_validate(real_marker_json) + assert block.id is None + + def test_page_count_matches_known_value(self, real_marker_json): + block = MarkerBlock.model_validate(real_marker_json) + doc = MarkerDocument(root=block) + assert len(doc.pages) == 17 + + def test_total_descendant_count_matches_known_census(self, real_marker_json): + block = MarkerBlock.model_validate(real_marker_json) + assert len(block.iter_descendants()) == 1085 + + def test_table_group_pattern_caption_then_table(self, real_marker_json): + block = MarkerBlock.model_validate(real_marker_json) + table_groups = [ + d for d in block.iter_descendants() if d.block_type == "TableGroup" + ] + assert len(table_groups) == 3 + for tg in table_groups: + assert len(tg.children) == 2 + assert tg.children[0].block_type == "Caption" + assert tg.children[1].block_type == "Table" + + def test_figure_group_pattern_figure_then_caption(self, real_marker_json): + block = MarkerBlock.model_validate(real_marker_json) + figure_groups = [ + d for d in block.iter_descendants() if d.block_type == "FigureGroup" + ] + assert len(figure_groups) == 1 + fg = figure_groups[0] + assert len(fg.children) == 2 + assert fg.children[0].block_type == "Figure" + assert fg.children[1].block_type == "Caption" + + def test_page_7_has_bare_tables_and_flat_footnotes(self, real_marker_json): + block = MarkerBlock.model_validate(real_marker_json) + doc = MarkerDocument(root=block) + page7 = doc.pages[7] + child_types = [c.block_type for c in page7.children] + assert child_types.count("Table") == 2 + assert child_types.count("Footnote") == 3 + assert "TableGroup" not in child_types + + def test_table_block_has_both_html_and_table_cell_children(self, real_marker_json): + block = MarkerBlock.model_validate(real_marker_json) + tables = [d for d in block.iter_descendants() if d.block_type == "Table"] + assert len(tables) == 7 + for table in tables: + assert "" in table.html + assert table.children is not None + assert all(c.block_type == "TableCell" for c in table.children) + + def test_picture_blocks_carry_image_payloads(self, real_marker_json): + block = MarkerBlock.model_validate(real_marker_json) + pictures = [d for d in block.iter_descendants() if d.block_type == "Picture"] + assert len(pictures) == 2 + for pic in pictures: + assert pic.images + assert len(next(iter(pic.images.values()))) > 100 # real base64 payload + + +# --------------------------------------------------------------------------- +# 3. Round-trip serialization losslessness +# --------------------------------------------------------------------------- + + +class TestRoundTripSerialization: + def test_synthetic_block_round_trip_via_dict(self): + leaf = _minimal_leaf_block() + container = _minimal_container_block( + "/page/0/TableGroup/0", "TableGroup", [leaf] + ) + block = MarkerBlock.model_validate(container) + dumped = block.model_dump(mode="json") + reparsed = MarkerBlock.model_validate(dumped) + assert reparsed.model_dump(mode="json") == dumped + + def test_synthetic_block_round_trip_via_json_string(self): + block = MarkerBlock.model_validate(_minimal_leaf_block()) + json_str = block.model_dump_json() + reparsed = MarkerBlock.model_validate_json(json_str) + assert reparsed.model_dump(mode="json") == block.model_dump(mode="json") + + def test_real_document_round_trip_via_dict_is_lossless(self, real_marker_json): + block = MarkerBlock.model_validate(real_marker_json) + dumped = block.model_dump(mode="json") + reparsed = MarkerBlock.model_validate(dumped) + assert reparsed.model_dump(mode="json") == dumped + + def test_real_document_round_trip_preserves_descendant_ids_in_order( + self, real_marker_json + ): + block = MarkerBlock.model_validate(real_marker_json) + dumped = block.model_dump(mode="json") + reparsed = MarkerBlock.model_validate(dumped) + orig_ids = [d.id for d in block.iter_descendants()] + reparsed_ids = [d.id for d in reparsed.iter_descendants()] + assert orig_ids == reparsed_ids + + def test_real_document_round_trip_via_json_string_is_lossless( + self, real_marker_json + ): + block = MarkerBlock.model_validate(real_marker_json) + json_str = block.model_dump_json() + reparsed = MarkerBlock.model_validate_json(json_str) + assert reparsed.model_dump(mode="json") == block.model_dump(mode="json") + + def test_serialization_is_deterministic_across_repeated_dumps( + self, real_marker_json + ): + + block = MarkerBlock.model_validate(real_marker_json) + dump1 = block.model_dump_json() + dump2 = block.model_dump_json() + assert dump1 == dump2 + + def test_unknown_extra_field_survives_full_round_trip(self): + data = _minimal_leaf_block() + data["some_future_field"] = {"nested": [1, 2, 3]} + block = MarkerBlock.model_validate(data) + json_str = block.model_dump_json() + reparsed = MarkerBlock.model_validate_json(json_str) + assert reparsed.model_dump(mode="json")["some_future_field"] == { + "nested": [1, 2, 3] + } diff --git a/tests/normalizer/test_stage0.py b/tests/normalizer/test_stage0.py new file mode 100644 index 0000000..b555077 --- /dev/null +++ b/tests/normalizer/test_stage0.py @@ -0,0 +1,88 @@ +"""Tests for Stage 0: Front-Matter Detection.""" +from __future__ import annotations + +from betydb_extraction.marker_adapter.raw_model import MarkerBlock +from betydb_extraction.normalizer.internal.stage0 import detect_front_matter + + +def _text_block(html: str) -> MarkerBlock: + return MarkerBlock(block_type="Text", html=html, children=None) + + +def _section_header() -> MarkerBlock: + return MarkerBlock(block_type="SectionHeader", html="

    Intro

    ", children=None) + + +def _page(children: list[MarkerBlock]) -> MarkerBlock: + return MarkerBlock(block_type="Page", children=children) + + +def test_no_signals_is_not_front_matter(): + page = _page([_text_block("Regular body text."), _section_header()]) + flags = detect_front_matter([page]) + assert flags == {0: False} + + +def test_only_s4_is_not_front_matter(): + # No SectionHeader (S4 fires) but no text signals -> only 1 signal total. + page = _page([_text_block("Just some ordinary text with no headers.")]) + flags = detect_front_matter([page]) + assert flags[0] is False + + +def test_s1_alone_is_not_front_matter(): + page = _page([_text_block("Submit your article here."), _section_header()]) + flags = detect_front_matter([page]) + assert flags[0] is False + + +def test_s1_plus_s4_is_front_matter(): + # S1 fires (string present) + S4 fires (no SectionHeader) = 2 signals. + page = _page([_text_block("Submit your article here.")]) + flags = detect_front_matter([page]) + assert flags[0] is True + + +def test_s2_plus_s3_is_front_matter(): + page = _page([ + _text_block("ISSN 1234-5678"), + _text_block("Article views: 42"), + _section_header(), + ]) + flags = detect_front_matter([page]) + assert flags[0] is True + + +def test_s1_case_sensitive(): + # Lowercase variant should NOT match S1 (case-sensitive per spec). + page = _page([_text_block("submit your article here.")]) + flags = detect_front_matter([page]) + # Only S4 fires (no SectionHeader) -> 1 signal -> not front matter. + assert flags[0] is False + + +def test_s4_checked_on_raw_children_not_nested(): + wrapper = MarkerBlock( + block_type="ListGroup", + children=[_section_header()], + ) + page = _page([_text_block("Submit your article here."), wrapper]) + flags = detect_front_matter([page]) + assert flags[0] is True + + +def test_multiple_pages_all_indices_present(): + front_page = _page([_text_block("Submit your article here.")]) + normal_page = _page([_text_block("Body text."), _section_header()]) + flags = detect_front_matter([front_page, normal_page, normal_page]) + assert flags == {0: True, 1: False, 2: False} + + +def test_text_concatenation_ignores_non_text_blocks(): + # ISSN pattern lives inside a non-"Text" block_type -> should not count. + page = _page([ + MarkerBlock(block_type="Caption", html="ISSN 1234-5678", children=None), + _text_block("Submit your article here."), + ]) + flags = detect_front_matter([page]) + assert flags[0] is True \ No newline at end of file diff --git a/tests/normalizer/test_stage1.py b/tests/normalizer/test_stage1.py new file mode 100644 index 0000000..ab92b20 --- /dev/null +++ b/tests/normalizer/test_stage1.py @@ -0,0 +1,90 @@ +"""Tests for Stage 1: Page Builder Construction.""" +from __future__ import annotations + +from betydb_extraction.marker_adapter.raw_model import MarkerBlock +from betydb_extraction.normalizer.internal.stage1 import build_page_shells + + +def _page_block(block_id: str, bbox=None) -> MarkerBlock: + return MarkerBlock(id=block_id, block_type="Page", bbox=bbox, children=[]) + + +def test_single_page_shell_basic_fields(): + page = _page_block("/page/0/Page/0") + shells = build_page_shells([page], {}) + + assert len(shells) == 1 + shell = shells[0] + assert shell.page_number == 0 + assert shell.is_front_matter is False + assert shell.children == [] + assert shell.provenance.marker_block_ids == ["/page/0/Page/0"] + assert shell.provenance.page_number == 0 + assert shell.provenance.bbox is None + assert shell.provenance.polygon is None + + +def test_page_number_is_enumerate_index_not_parsed_from_id(): + page0 = _page_block("/page/5/Page/0") + page1 = _page_block("/page/1/Page/0") + shells = build_page_shells([page0, page1], {}) + + assert shells[0].page_number == 0 + assert shells[1].page_number == 1 + assert shells[0].provenance.page_number == 0 + assert shells[1].provenance.page_number == 1 + + +def test_bbox_set_when_present(): + page = _page_block("/page/0/Page/0", bbox=[10.0, 20.0, 30.0, 40.0]) + shells = build_page_shells([page], {}) + + bbox = shells[0].provenance.bbox + assert bbox is not None + assert bbox.x0 == 10.0 + assert bbox.y0 == 20.0 + assert bbox.x1 == 30.0 + assert bbox.y1 == 40.0 + + +def test_bbox_none_when_absent(): + page = _page_block("/page/0/Page/0", bbox=None) + shells = build_page_shells([page], {}) + assert shells[0].provenance.bbox is None + + +def test_front_matter_flags_applied_per_index(): + pages = [_page_block(f"/page/{i}/Page/0") for i in range(3)] + flags = {0: True, 2: True} # index 1 deliberately omitted + shells = build_page_shells(pages, flags) + + assert shells[0].is_front_matter is True + assert shells[1].is_front_matter is False # default for missing index + assert shells[2].is_front_matter is True + + +def test_missing_index_defaults_to_false(): + page = _page_block("/page/0/Page/0") + shells = build_page_shells([page], {5: True}) # unrelated index + assert shells[0].is_front_matter is False + + +def test_multiple_pages_order_and_count(): + pages = [_page_block(f"/page/{i}/Page/0") for i in range(5)] + shells = build_page_shells(pages, {}) + + assert len(shells) == 5 + for i, shell in enumerate(shells): + assert shell.page_number == i + assert shell.provenance.marker_block_ids == [f"/page/{i}/Page/0"] + + +def test_children_always_empty_at_this_stage(): + page = _page_block("/page/0/Page/0") + shells = build_page_shells([page], {}) + assert shells[0].children == [] + + +def test_empty_page_blocks_returns_empty_list(): + shells = build_page_shells([], {}) + assert shells == [] \ No newline at end of file diff --git a/tests/normalizer/test_stage10.py b/tests/normalizer/test_stage10.py new file mode 100644 index 0000000..eee85e3 --- /dev/null +++ b/tests/normalizer/test_stage10.py @@ -0,0 +1,314 @@ +"""Tests for Stage 10: Materialization.""" +from __future__ import annotations + +from datetime import datetime, timezone + +import pytest + +from betydb_extraction.marker_adapter.raw_model import MarkerBlock +from betydb_extraction.normalizer.builders.base import ClassifiedBlock, Disposition, UnwrappedBlock +from betydb_extraction.normalizer.builders.page import PageBuilder +from betydb_extraction.normalizer.builders.provenance import ProvenanceBuilder +from betydb_extraction.normalizer.context import NormalizerProcessingContext +from betydb_extraction.normalizer.internal.stage3 import build_leaf_builders +from betydb_extraction.normalizer.internal.stage4 import resolve_captions +from betydb_extraction.normalizer.internal.stage5 import build_table_structure +from betydb_extraction.normalizer.internal.stage6 import attach_footnotes +from betydb_extraction.normalizer.internal.stage7 import assemble_section_tree +from betydb_extraction.normalizer.internal.stage8 import assign_reading_order +from betydb_extraction.normalizer.internal.stage9 import compute_canonical_paths +from betydb_extraction.normalizer.internal.stage10 import ( + materialize, + _compute_document_id, + _compute_object_id, +) + + +def _block(block_id, block_type, html="", bbox=None, polygon=None, images=None, section_hierarchy=None): + return MarkerBlock( + id=block_id, block_type=block_type, html=html, bbox=bbox, polygon=polygon, + images=images, section_hierarchy=section_hierarchy or {}, children=None, + ) + + +def _ctx(): + return NormalizerProcessingContext( + marker_version="test-marker-1.0", + normalizer_version="test-normalizer-1.0", + source_marker_artifact_ref="test/path.json", + processed_at=datetime(2026, 1, 1, tzinfo=timezone.utc), + ) + + +def _build_page(page_index, blocks_with_dispositions, is_front_matter=False): + seq = [ + ClassifiedBlock(unwrapped=UnwrappedBlock(block=b, wrapper_context=None), disposition=d) + for b, d in blocks_with_dispositions + ] + builders = build_leaf_builders(seq, page_number=page_index) + resolve_captions(builders, seq, page_index) + build_table_structure(builders, seq) + attach_footnotes(builders, seq, page_index) + page_builder = PageBuilder( + page_number=page_index, + is_front_matter=is_front_matter, + provenance=ProvenanceBuilder( + marker_block_ids=[f"/page/{page_index}/Page/0"], page_number=page_index + ), + children=builders, + ) + return page_builder, seq + + +def _run_pipeline(pages_spec): + page_builders, classified_pages = [], [] + for i, (bwd, ifm) in enumerate(pages_spec): + pb, seq = _build_page(i, bwd, ifm) + page_builders.append(pb) + classified_pages.append(seq) + + assemble_section_tree(page_builders, classified_pages) + assign_reading_order(page_builders) + compute_canonical_paths(page_builders) + return page_builders, classified_pages + + +def test_single_paragraph_document(): + text = _block("/p/0/Text/0", "Text", html="Hello world") + pages, classified = _run_pipeline([([(text, Disposition.BODY_PARAGRAPH)], False)]) + + doc = materialize(pages, "src:1", _ctx(), classified) + + assert doc.source_pdf_identifier == "src:1" + assert len(doc.pages) == 1 + assert doc.pages[0].page_number == 0 + assert doc.pages[0].is_front_matter is False + assert len(doc.pages[0].children) == 1 + para = doc.pages[0].children[0] + assert para.kind == "paragraph" + assert para.text == "Hello world" + + +def test_document_id_matches_compute_document_id(): + text = _block("/p/0/Text/0", "Text", html="x") + pages, classified = _run_pipeline([([(text, Disposition.BODY_PARAGRAPH)], False)]) + + doc = materialize(pages, "src:abc", _ctx(), classified) + assert doc.id == _compute_document_id("src:abc") + + +def test_object_id_deterministic_from_canonical_path(): + text = _block("/p/0/Text/0", "Text", html="x") + pages, classified = _run_pipeline([([(text, Disposition.BODY_PARAGRAPH)], False)]) + + doc = materialize(pages, "src:abc", _ctx(), classified) + para = doc.pages[0].children[0] + document_id = _compute_document_id("src:abc") + expected_id = _compute_object_id(document_id, "/page/0/paragraph/1") + assert para.id == expected_id + + +def test_different_source_pdf_identifier_gives_different_ids(): + text1 = _block("/p/0/Text/0", "Text", html="x") + pages1, classified1 = _run_pipeline([([(text1, Disposition.BODY_PARAGRAPH)], False)]) + doc1 = materialize(pages1, "src:one", _ctx(), classified1) + + text2 = _block("/p/0/Text/0", "Text", html="x") + pages2, classified2 = _run_pipeline([([(text2, Disposition.BODY_PARAGRAPH)], False)]) + doc2 = materialize(pages2, "src:two", _ctx(), classified2) + + assert doc1.id != doc2.id + assert doc1.pages[0].children[0].id != doc2.pages[0].children[0].id + + +# Statistics + +def test_statistics_counts_match_content(): + text1 = _block("/p/0/Text/0", "Text", html="a") + text2 = _block("/p/0/Text/1", "Text", html="b") + table = _block("/p/0/Table/0", "Table", html="
    ") + figure = _block("/p/0/Figure/0", "Figure") + equation = _block("/p/0/Eq/0", "Equation", html="x=y") + + blocks = [ + (text1, Disposition.BODY_PARAGRAPH), + (text2, Disposition.BODY_PARAGRAPH), + (table, Disposition.TABLE_SHELL), + (figure, Disposition.FIGURE_SHELL), + (equation, Disposition.EQUATION), + ] + pages, classified = _run_pipeline([(blocks, False)]) + doc = materialize(pages, "src:1", _ctx(), classified) + + stats = doc.statistics + assert stats.page_count == 1 + assert stats.paragraph_count == 2 + assert stats.table_count == 1 + assert stats.figure_count == 1 + assert stats.equation_count == 1 + assert stats.section_count == 0 + assert stats.footnote_count == 0 + + +def test_metadata_page_count_and_front_matter_flag(): + front_text = _block("/p/0/Text/0", "Text", html="cover") + body_text = _block("/p/1/Text/0", "Text", html="content") + pages, classified = _run_pipeline([ + ([(front_text, Disposition.BODY_PARAGRAPH)], True), + ([(body_text, Disposition.BODY_PARAGRAPH)], False), + ]) + doc = materialize(pages, "src:1", _ctx(), classified) + + assert doc.metadata.page_count == 2 + assert doc.metadata.has_front_matter_page is True + + +#Title extraction + +def test_title_from_first_heading_on_first_non_front_matter_page(): + front_heading = _block("/p/0/SH/0", "SectionHeader", html="Front Matter Heading") + front_content = _block( + "/p/0/Text/0", "Text", html="cover", + section_hierarchy={"0": "/p/0/SH/0"}, + ) + real_heading = _block("/p/1/SH/0", "SectionHeader", html="Real Title") + real_content = _block( + "/p/1/Text/0", "Text", html="body", + section_hierarchy={"0": "/p/1/SH/0"}, + ) + pages, classified = _run_pipeline([ + ([(front_heading, Disposition.GENUINE_SECTION_HEADER), (front_content, Disposition.BODY_PARAGRAPH)], True), + ([(real_heading, Disposition.GENUINE_SECTION_HEADER), (real_content, Disposition.BODY_PARAGRAPH)], False), + ]) + doc = materialize(pages, "src:1", _ctx(), classified) + + assert doc.metadata.title == "Real Title" + + +def test_title_none_when_no_heading_present(): + text = _block("/p/0/Text/0", "Text", html="no headers here") + pages, classified = _run_pipeline([([(text, Disposition.BODY_PARAGRAPH)], False)]) + doc = materialize(pages, "src:1", _ctx(), classified) + assert doc.metadata.title is None + + +#Section materialization + +def test_section_materialized_with_children(): + heading = _block("/p/0/SH/0", "SectionHeader", html="Intro") + para = _block( + "/p/0/Text/0", "Text", html="body", + section_hierarchy={"0": "/p/0/SH/0"}, + ) + blocks = [(heading, Disposition.GENUINE_SECTION_HEADER), (para, Disposition.BODY_PARAGRAPH)] + pages, classified = _run_pipeline([(blocks, False)]) + doc = materialize(pages, "src:1", _ctx(), classified) + + section = doc.pages[0].children[0] + assert section.kind == "section" + assert section.heading_text == "Intro" + assert section.depth == 0 + assert len(section.children) == 1 + assert section.children[0].text == "body" + + +def test_leaf_section_path_translated_to_final_section_id(): + heading = _block("/p/0/SH/0", "SectionHeader", html="Intro") + para = _block( + "/p/0/Text/0", "Text", html="body", + section_hierarchy={"0": "/p/0/SH/0"}, + ) + blocks = [(heading, Disposition.GENUINE_SECTION_HEADER), (para, Disposition.BODY_PARAGRAPH)] + pages, classified = _run_pipeline([(blocks, False)]) + doc = materialize(pages, "src:1", _ctx(), classified) + + section = doc.pages[0].children[0] + leaf = section.children[0] + assert leaf.provenance.section_path == [section.id] + + +#Footnote <-> Table symmetric translation + +def test_footnote_table_ids_translated_and_symmetric(): + table = _block("/p/0/Table/0", "Table", html="
    ", bbox=[0, 0, 10, 50]) + footnote = _block("/p/0/Footnote/0", "Footnote", html="1. note", bbox=[0, 60, 10, 70]) + blocks = [(table, Disposition.TABLE_SHELL), (footnote, Disposition.FOOTNOTE)] + pages, classified = _run_pipeline([(blocks, False)]) + doc = materialize(pages, "src:1", _ctx(), classified) + + mat_table = doc.pages[0].children[0] + mat_footnote = doc.pages[0].children[1] + + assert mat_footnote.attached_object_id == mat_table.id + assert mat_table.footnote_ids == [mat_footnote.id] + + +def test_footnote_unresolved_stays_none_and_counted(): + footnote = _block("/p/0/Footnote/0", "Footnote", html="1. orphan note") + blocks = [(footnote, Disposition.FOOTNOTE)] + pages, classified = _run_pipeline([(blocks, False)]) + doc = materialize(pages, "src:1", _ctx(), classified) + + mat_footnote = doc.pages[0].children[0] + assert mat_footnote.attached_object_id is None + assert doc.statistics.unresolved_footnote_count == 1 + + +#Table row/cell text and empty-cell handling + +def test_table_row_cell_empty_text_becomes_empty_string_not_none(): + table = _block("/p/0/Table/0", "Table", html="
    ") + blocks = [(table, Disposition.TABLE_SHELL)] + pages, classified = _run_pipeline([(blocks, False)]) + doc = materialize(pages, "src:1", _ctx(), classified) + + mat_table = doc.pages[0].children[0] + assert mat_table.rows[0].cells[0].text == "" + + +#BoundingBox / Polygon conversion + +def test_bbox_and_polygon_converted_on_materialized_provenance(): + text = _block( + "/p/0/Text/0", "Text", html="x", + bbox=[1.0, 2.0, 3.0, 4.0], + polygon=[[0.0, 0.0], [1.0, 0.0], [1.0, 1.0], [0.0, 1.0]], + ) + blocks = [(text, Disposition.BODY_PARAGRAPH)] + pages, classified = _run_pipeline([(blocks, False)]) + doc = materialize(pages, "src:1", _ctx(), classified) + + prov = doc.pages[0].children[0].provenance + assert prov.bbox.x0 == 1.0 + assert prov.bbox.y1 == 4.0 + assert prov.polygon.points == ((0.0, 0.0), (1.0, 0.0), (1.0, 1.0), (0.0, 1.0)) + + + + + +#ProcessingMetadata passthrough + +def test_processing_metadata_passed_verbatim(): + text = _block("/p/0/Text/0", "Text", html="x") + pages, classified = _run_pipeline([([(text, Disposition.BODY_PARAGRAPH)], False)]) + ctx = _ctx() + doc = materialize(pages, "src:1", ctx, classified) + + pm = doc.processing_metadata + assert pm.marker_version == ctx.marker_version + assert pm.normalizer_version == ctx.normalizer_version + assert pm.source_marker_artifact_ref == ctx.source_marker_artifact_ref + assert pm.processed_at == ctx.processed_at + + +def test_dangling_footnote_reference_raises(): + footnote = _block("/p/0/Footnote/0", "Footnote", html="orphan") + blocks = [(footnote, Disposition.FOOTNOTE)] + pages, classified = _run_pipeline([(blocks, False)]) + + footnote_builder = pages[0].children[0] + footnote_builder.attached_object_id = "/does/not/exist" + + with pytest.raises(ValueError): + materialize(pages, "src:1", _ctx(), classified) \ No newline at end of file diff --git a/tests/normalizer/test_stage1_5.py b/tests/normalizer/test_stage1_5.py new file mode 100644 index 0000000..6a93f84 --- /dev/null +++ b/tests/normalizer/test_stage1_5.py @@ -0,0 +1,125 @@ +"""Tests for Stage 1.5: Wrapper Unwrapping.""" +from __future__ import annotations + +from betydb_extraction.marker_adapter.raw_model import MarkerBlock +from betydb_extraction.normalizer.builders.base import UnwrappedBlock, WrapperContext +from betydb_extraction.normalizer.internal.stage1_5 import unwrap_page + + +def _block(block_id: str, block_type: str, children=None) -> MarkerBlock: + return MarkerBlock(id=block_id, block_type=block_type, children=children) + + +def test_direct_non_wrapper_child_has_none_context(): + text = _block("/p/0/Text/0", "Text", children=None) + result = unwrap_page([text]) + + assert len(result) == 1 + assert result[0].block is text + assert result[0].wrapper_context is None + + +def test_table_group_unwrapped_one_level(): + cell1 = _block("/p/0/TableCell/0", "TableCell", children=None) + cell2 = _block("/p/0/TableCell/1", "TableCell", children=None) + table_group = _block("/p/0/TableGroup/0", "TableGroup", children=[cell1, cell2]) + + result = unwrap_page([table_group]) + + assert len(result) == 2 + assert result[0].block is cell1 + assert result[1].block is cell2 + for r in result: + assert r.wrapper_context == WrapperContext( + wrapper_type="TableGroup", block_id="/p/0/TableGroup/0" + ) + + +def test_figure_group_unwrapped_one_level(): + pic = _block("/p/0/Picture/0", "Picture", children=None) + figure_group = _block("/p/0/FigureGroup/0", "FigureGroup", children=[pic]) + + result = unwrap_page([figure_group]) + + assert len(result) == 1 + assert result[0].block is pic + assert result[0].wrapper_context == WrapperContext( + wrapper_type="FigureGroup", block_id="/p/0/FigureGroup/0" + ) + + +def test_list_group_unwrapped_one_level(): + item = _block("/p/0/ListItem/0", "ListItem", children=None) + list_group = _block("/p/0/ListGroup/0", "ListGroup", children=[item]) + + result = unwrap_page([list_group]) + + assert len(result) == 1 + assert result[0].block is item + assert result[0].wrapper_context == WrapperContext( + wrapper_type="ListGroup", block_id="/p/0/ListGroup/0" + ) + + +def test_nested_wrapper_not_recursively_unwrapped(): + inner_list_group = _block( + "/p/0/ListGroup/0", "ListGroup", + children=[_block("/p/0/ListItem/0", "ListItem", children=None)], + ) + outer_table_group = _block( + "/p/0/TableGroup/0", "TableGroup", children=[inner_list_group] + ) + + result = unwrap_page([outer_table_group]) + + assert len(result) == 1 + assert result[0].block is inner_list_group + assert result[0].block.block_type == "ListGroup" + assert result[0].wrapper_context == WrapperContext( + wrapper_type="TableGroup", block_id="/p/0/TableGroup/0" + ) + + +def test_order_preserved_across_mixed_blocks(): + text1 = _block("/p/0/Text/0", "Text", children=None) + cell = _block("/p/0/TableCell/0", "TableCell", children=None) + table_group = _block("/p/0/TableGroup/0", "TableGroup", children=[cell]) + text2 = _block("/p/0/Text/1", "Text", children=None) + + result = unwrap_page([text1, table_group, text2]) + + assert [r.block for r in result] == [text1, cell, text2] + assert result[0].wrapper_context is None + assert result[1].wrapper_context is not None + assert result[2].wrapper_context is None + + +def test_wrapper_with_empty_children_contributes_nothing(): + empty_group = _block("/p/0/TableGroup/0", "TableGroup", children=[]) + text = _block("/p/0/Text/0", "Text", children=None) + + result = unwrap_page([empty_group, text]) + + assert len(result) == 1 + assert result[0].block is text + + +def test_empty_page_returns_empty_list(): + assert unwrap_page([]) == [] + + +def test_original_list_not_mutated(): + text = _block("/p/0/Text/0", "Text", children=None) + children = [text] + original_len = len(children) + + unwrap_page(children) + + assert len(children) == original_len + assert children[0] is text + + +def test_return_type_is_unwrapped_block(): + text = _block("/p/0/Text/0", "Text", children=None) + result = unwrap_page([text]) + assert isinstance(result[0], UnwrappedBlock) \ No newline at end of file diff --git a/tests/normalizer/test_stage2.py b/tests/normalizer/test_stage2.py new file mode 100644 index 0000000..7df55c7 --- /dev/null +++ b/tests/normalizer/test_stage2.py @@ -0,0 +1,200 @@ +"""Tests for Stage 2: Block Classification.""" +from __future__ import annotations + +import pytest + +from betydb_extraction.marker_adapter.raw_model import MarkerBlock +from betydb_extraction.normalizer.builders.base import Disposition, UnwrappedBlock +from betydb_extraction.normalizer.errors import UnrecognizedBlockTypeError +from betydb_extraction.normalizer.internal.stage2 import classify_blocks + + +def _block(block_id, block_type, html="", section_hierarchy=None, children=None): + return MarkerBlock( + id=block_id, + block_type=block_type, + html=html, + section_hierarchy=section_hierarchy or {}, + children=children, + ) + + +def _u(block): + return UnwrappedBlock(block=block, wrapper_context=None) + +@pytest.mark.parametrize( + "block_type,expected", + [ + ("Text", Disposition.BODY_PARAGRAPH), + ("Table", Disposition.TABLE_SHELL), + ("Figure", Disposition.FIGURE_SHELL), + ("Caption", Disposition.CAPTION_TEXT), + ("TableCell", Disposition.TABLE_CELL_EVIDENCE), + ("Equation", Disposition.EQUATION), + ("Footnote", Disposition.FOOTNOTE), + ("PageHeader", Disposition.PAGE_HEADER), + ("PageFooter", Disposition.PAGE_FOOTER), + ("Picture", Disposition.PICTURE), + ], +) +def test_static_dispatch(block_type, expected): + block = _block("/p/0/X/0", block_type) + result = classify_blocks([_u(block)], page_index=0) + assert result[0].disposition == expected + + +def test_unrecognized_block_type_raises(): + block = _block("/p/0/Weird/0", "WeirdUnknownType") + with pytest.raises(UnrecognizedBlockTypeError): + classify_blocks([_u(block)], page_index=0) + + +def test_section_header_default_genuine(): + block = _block("/p/0/SH/0", "SectionHeader", html="Introduction") + result = classify_blocks([_u(block)], page_index=0) + assert result[0].disposition == Disposition.GENUINE_SECTION_HEADER + + +def test_section_header_caption_label_override_table(): + label = _block("/p/0/SH/0", "SectionHeader", html="Table 3") + table = _block("/p/0/Table/0", "Table") + result = classify_blocks([_u(label), _u(table)], page_index=0) + assert result[0].disposition == Disposition.CAPTION_LABEL + + +def test_section_header_caption_label_override_figure_with_margin(): + # Pattern B: label, then a Text block, then the Figure (within window=3) + label = _block("/p/0/SH/0", "SectionHeader", html="Figure 12.") + filler = _block("/p/0/Text/0", "Text", html="some filler") + figure = _block("/p/0/Figure/0", "Figure") + result = classify_blocks([_u(label), _u(filler), _u(figure)], page_index=0) + assert result[0].disposition == Disposition.CAPTION_LABEL + + +def test_section_header_text_matches_but_no_table_figure_nearby_stays_genuine(): + label = _block("/p/0/SH/0", "SectionHeader", html="Table 3") + filler1 = _block("/p/0/Text/0", "Text") + filler2 = _block("/p/0/Text/1", "Text") + filler3 = _block("/p/0/Text/2", "Text") + result = classify_blocks( + [_u(label), _u(filler1), _u(filler2), _u(filler3)], page_index=0 + ) + assert result[0].disposition == Disposition.GENUINE_SECTION_HEADER + + +def test_section_header_table_present_but_text_does_not_match_regex(): + label = _block("/p/0/SH/0", "SectionHeader", html="Introduction") + table = _block("/p/0/Table/0", "Table") + result = classify_blocks([_u(label), _u(table)], page_index=0) + assert result[0].disposition == Disposition.GENUINE_SECTION_HEADER + + +def test_caption_label_regex_case_insensitive(): + label = _block("/p/0/SH/0", "SectionHeader", html="TABLE 1:") + table = _block("/p/0/Table/0", "Table") + result = classify_blocks([_u(label), _u(table)], page_index=0) + assert result[0].disposition == Disposition.CAPTION_LABEL + + +def test_caption_label_lookahead_window_boundary(): + label = _block("/p/0/SH/0", "SectionHeader", html="Table 3") + f1 = _block("/p/0/Text/0", "Text") + f2 = _block("/p/0/Text/1", "Text") + table = _block("/p/0/Table/0", "Table") # 3rd block after label (in-window) + result = classify_blocks([_u(label), _u(f1), _u(f2), _u(table)], page_index=0) + assert result[0].disposition == Disposition.CAPTION_LABEL + + +def test_caption_label_lookahead_window_out_of_range_stays_genuine(): + label = _block("/p/0/SH/0", "SectionHeader", html="Table 3") + f1 = _block("/p/0/Text/0", "Text") + f2 = _block("/p/0/Text/1", "Text") + f3 = _block("/p/0/Text/2", "Text") + table = _block("/p/0/Table/0", "Table") # 4th block after label — out of window + result = classify_blocks( + [_u(label), _u(f1), _u(f2), _u(f3), _u(table)], page_index=0 + ) + assert result[0].disposition == Disposition.GENUINE_SECTION_HEADER + +def test_list_item_no_section_hierarchy_is_body_paragraph(): + item = _block("/p/0/LI/0", "ListItem", section_hierarchy={}) + result = classify_blocks([_u(item)], page_index=0) + assert result[0].disposition == Disposition.BODY_PARAGRAPH + + +def test_list_item_under_references_heading_is_reference_entry(): + heading = _block("/p/0/SH/0", "SectionHeader", html="References") + item = _block("/p/0/LI/0", "ListItem", section_hierarchy={"1": "/p/0/SH/0"}) + result = classify_blocks([_u(heading), _u(item)], page_index=0) + assert result[0].disposition == Disposition.GENUINE_SECTION_HEADER + assert result[1].disposition == Disposition.REFERENCE_ENTRY + + +@pytest.mark.parametrize( + "heading_text", + ["References", "BIBLIOGRAPHY", "Works Cited", "literature cited"], +) +def test_list_item_references_vocabulary_case_insensitive(heading_text): + heading = _block("/p/0/SH/0", "SectionHeader", html=heading_text) + item = _block("/p/0/LI/0", "ListItem", section_hierarchy={"1": "/p/0/SH/0"}) + result = classify_blocks([_u(heading), _u(item)], page_index=0) + assert result[1].disposition == Disposition.REFERENCE_ENTRY + + +def test_list_item_under_non_references_heading_is_body_paragraph(): + heading = _block("/p/0/SH/0", "SectionHeader", html="Methods") + item = _block("/p/0/LI/0", "ListItem", section_hierarchy={"1": "/p/0/SH/0"}) + result = classify_blocks([_u(heading), _u(item)], page_index=0) + assert result[1].disposition == Disposition.BODY_PARAGRAPH + + +def test_list_item_deepest_key_resolved_numerically(): + shallow_heading = _block("/p/0/SH/0", "SectionHeader", html="Methods") + deep_heading = _block("/p/0/SH/1", "SectionHeader", html="References") + item = _block( + "/p/0/LI/0", "ListItem", + section_hierarchy={"2": "/p/0/SH/0", "10": "/p/0/SH/1"}, + ) + result = classify_blocks( + [_u(shallow_heading), _u(deep_heading), _u(item)], page_index=0 + ) + assert result[2].disposition == Disposition.REFERENCE_ENTRY + + +def test_list_item_heading_not_yet_seen_is_body_paragraph(): + item = _block("/p/0/LI/0", "ListItem", section_hierarchy={"1": "/p/0/SH/999"}) + result = classify_blocks([_u(item)], page_index=0) + assert result[0].disposition == Disposition.BODY_PARAGRAPH + +def test_heading_registry_shared_across_pages_for_references_split(): + shared_registry: dict = {} + heading = _block("/p/0/SH/0", "SectionHeader", html="References") + page0 = classify_blocks([_u(heading)], page_index=0, heading_registry=shared_registry) + assert page0[0].disposition == Disposition.GENUINE_SECTION_HEADER + + # Page 1's ListItem refers back to page 0's heading id. + item = _block("/p/1/LI/0", "ListItem", section_hierarchy={"1": "/p/0/SH/0"}) + page1 = classify_blocks([_u(item)], page_index=1, heading_registry=shared_registry) + assert page1[0].disposition == Disposition.REFERENCE_ENTRY + + +def test_none_heading_registry_creates_fresh_dict_isolated(): + heading = _block("/p/0/SH/0", "SectionHeader", html="References") + item = _block("/p/0/LI/0", "ListItem", section_hierarchy={"1": "/p/0/SH/0"}) + result = classify_blocks([_u(heading), _u(item)], page_index=0) + assert result[1].disposition == Disposition.REFERENCE_ENTRY + + +def test_result_same_length_and_order_as_input(): + blocks = [ + _u(_block("/p/0/Text/0", "Text")), + _u(_block("/p/0/Table/0", "Table")), + _u(_block("/p/0/Figure/0", "Figure")), + ] + result = classify_blocks(blocks, page_index=0) + assert len(result) == 3 + assert [r.unwrapped.block.block_type for r in result] == ["Text", "Table", "Figure"] + + +def test_empty_sequence_returns_empty_list(): + assert classify_blocks([], page_index=0) == [] \ No newline at end of file diff --git a/tests/normalizer/test_stage3.py b/tests/normalizer/test_stage3.py new file mode 100644 index 0000000..4cfc6f5 --- /dev/null +++ b/tests/normalizer/test_stage3.py @@ -0,0 +1,204 @@ +"""Tests for Stage 3: Leaf Builder Construction.""" +from __future__ import annotations + +import base64 + +import pytest + +from betydb_extraction.marker_adapter.raw_model import MarkerBlock +from betydb_extraction.normalizer.builders.base import ClassifiedBlock, Disposition, UnwrappedBlock +from betydb_extraction.normalizer.builders.figure import FigureBuilder +from betydb_extraction.normalizer.builders.footnote import FootnoteBuilder +from betydb_extraction.normalizer.builders.page_footer import PageFooterBuilder +from betydb_extraction.normalizer.builders.page_header import PageHeaderBuilder +from betydb_extraction.normalizer.builders.paragraph import ParagraphBuilder +from betydb_extraction.normalizer.builders.reference import ReferenceBuilder +from betydb_extraction.normalizer.builders.table import TableBuilder +from betydb_extraction.normalizer.internal.stage3 import build_leaf_builders + + +def _block(block_id, block_type, html="", bbox=None, polygon=None, images=None): + return MarkerBlock( + id=block_id, + block_type=block_type, + html=html, + bbox=bbox, + polygon=polygon, + images=images, + children=None, + ) + + +def _cb(block, disposition): + return ClassifiedBlock( + unwrapped=UnwrappedBlock(block=block, wrapper_context=None), + disposition=disposition, + ) + + +def test_body_paragraph_builds_paragraph_builder(): + block = _block("/p/0/Text/0", "Text", html="

    Hello

    ") + result = build_leaf_builders([_cb(block, Disposition.BODY_PARAGRAPH)], page_number=2) + + assert len(result) == 1 + b = result[0] + assert isinstance(b, ParagraphBuilder) + assert b.kind == "paragraph" + assert b.text == "

    Hello

    " + assert b.canonical_path is None + assert b.provenance.marker_block_ids == ["/p/0/Text/0"] + assert b.provenance.page_number == 2 + assert b.provenance.reading_order_index is None + assert b.provenance.section_path == [] + + +def test_equation_builds_equation_builder(): + block = _block("/p/0/Equation/0", "Equation", html="x=y") + result = build_leaf_builders([_cb(block, Disposition.EQUATION)], page_number=0) + + b = result[0] + assert b.kind == "equation" + assert b.raw_math == "x=y" + assert b.equation_number is None + + +def test_footnote_builds_footnote_builder(): + block = _block("/p/0/Footnote/0", "Footnote", html="1. Note text") + result = build_leaf_builders([_cb(block, Disposition.FOOTNOTE)], page_number=0) + + b = result[0] + assert isinstance(b, FootnoteBuilder) + assert b.kind == "footnote" + assert b.raw_text == "1. Note text" + assert b.attached_object_id is None + + +def test_reference_entry_builds_reference_builder(): + block = _block("/p/0/LI/0", "ListItem", html="Smith, J. (2020).") + result = build_leaf_builders([_cb(block, Disposition.REFERENCE_ENTRY)], page_number=0) + + b = result[0] + assert isinstance(b, ReferenceBuilder) + assert b.kind == "reference" + assert b.raw_text == "Smith, J. (2020)." + + +def test_page_header_builds_page_header_builder(): + block = _block("/p/0/PH/0", "PageHeader", html="Journal Name") + result = build_leaf_builders([_cb(block, Disposition.PAGE_HEADER)], page_number=3) + + b = result[0] + assert isinstance(b, PageHeaderBuilder) + assert b.kind == "page_header" + assert b.raw_text == "Journal Name" + + +def test_page_footer_builds_page_footer_builder(): + block = _block("/p/0/PF/0", "PageFooter", html="Page 3 of 10") + result = build_leaf_builders([_cb(block, Disposition.PAGE_FOOTER)], page_number=3) + + b = result[0] + assert isinstance(b, PageFooterBuilder) + assert b.kind == "page_footer" + assert b.raw_text == "Page 3 of 10" + + +def test_table_shell_builds_table_builder_with_empty_defaults(): + block = _block("/p/0/Table/0", "Table", html="...
    ") + result = build_leaf_builders([_cb(block, Disposition.TABLE_SHELL)], page_number=0) + + b = result[0] + assert isinstance(b, TableBuilder) + assert b.kind == "table" + assert b.raw_html == "...
    " + assert b.caption is None + assert b.rows == [] + assert b.cells == [] + assert b.footnote_ids == [] + + +def test_figure_shell_builds_figure_builder_defaults(): + block = _block("/p/0/Figure/0", "Figure", html="") + result = build_leaf_builders([_cb(block, Disposition.FIGURE_SHELL)], page_number=0) + + b = result[0] + assert isinstance(b, FigureBuilder) + assert b.kind == "figure" + assert b.caption is None + assert b.footnote_ids == [] + + +#image_data extraction + +def test_figure_image_data_decodes_base64_string(): + payload = base64.b64encode(b"raw-bytes-here").decode() + block = _block("/p/0/Figure/0", "Figure", images={"img1.png": payload}) + result = build_leaf_builders([_cb(block, Disposition.FIGURE_SHELL)], page_number=0) + assert result[0].image_data == b"raw-bytes-here" + + +def test_figure_image_data_none_when_images_empty(): + block = _block("/p/0/Figure/0", "Figure", images={}) + result = build_leaf_builders([_cb(block, Disposition.FIGURE_SHELL)], page_number=0) + assert result[0].image_data is None + + +def test_figure_image_data_none_when_images_absent(): + block = _block("/p/0/Figure/0", "Figure", images=None) + result = build_leaf_builders([_cb(block, Disposition.FIGURE_SHELL)], page_number=0) + assert result[0].image_data is None + + + +#Proveance population + +def test_provenance_bbox_and_polygon_carried_from_block(): + block = _block( + "/p/0/Text/0", "Text", html="hi", + bbox=[1.0, 2.0, 3.0, 4.0], + polygon=[[0.0, 0.0], [1.0, 0.0], [1.0, 1.0], [0.0, 1.0]], + ) + result = build_leaf_builders([_cb(block, Disposition.BODY_PARAGRAPH)], page_number=0) + prov = result[0].provenance + assert prov.bbox.x0 == 1.0 + assert prov.polygon[0].x == 0.0 + assert len(prov.polygon) == 4 + + +@pytest.mark.parametrize( + "disposition", + [ + Disposition.CAPTION_TEXT, + Disposition.TABLE_CELL_EVIDENCE, + Disposition.GENUINE_SECTION_HEADER, + Disposition.CAPTION_LABEL, + Disposition.PICTURE, + ], +) +def test_no_builder_dispositions_produce_nothing(disposition): + block = _block("/p/0/X/0", "Caption", html="ignored") + result = build_leaf_builders([_cb(block, disposition)], page_number=0) + assert result == [] + + +#Ordering / mixed sequence + +def test_order_preserved_and_skipped_blocks_excluded(): + text = _block("/p/0/Text/0", "Text", html="body") + caption = _block("/p/0/Caption/0", "Caption", html="skip me") + table = _block("/p/0/Table/0", "Table", html="") + + seq = [ + _cb(text, Disposition.BODY_PARAGRAPH), + _cb(caption, Disposition.CAPTION_TEXT), + _cb(table, Disposition.TABLE_SHELL), + ] + result = build_leaf_builders(seq, page_number=0) + + assert len(result) == 2 + assert result[0].kind == "paragraph" + assert result[1].kind == "table" + + +def test_empty_sequence_returns_empty_list(): + assert build_leaf_builders([], page_number=0) == [] \ No newline at end of file diff --git a/tests/normalizer/test_stage4.py b/tests/normalizer/test_stage4.py new file mode 100644 index 0000000..8d60242 --- /dev/null +++ b/tests/normalizer/test_stage4.py @@ -0,0 +1,273 @@ +"""Tests for Stage 4: Caption Resolution.""" +from __future__ import annotations + +from betydb_extraction.marker_adapter.raw_model import MarkerBlock +from betydb_extraction.normalizer.builders.base import ( + ClassifiedBlock, + Disposition, + UnwrappedBlock, + WrapperContext, +) +from betydb_extraction.normalizer.builders.figure import FigureBuilder +from betydb_extraction.normalizer.builders.provenance import ProvenanceBuilder +from betydb_extraction.normalizer.builders.table import TableBuilder +from betydb_extraction.normalizer.internal.stage4 import resolve_captions + + +def _block(block_id, block_type, html="", bbox=None): + return MarkerBlock(id=block_id, block_type=block_type, html=html, bbox=bbox, children=None) + + +def _cb(block, disposition, wrapper_context=None): + return ClassifiedBlock( + unwrapped=UnwrappedBlock(block=block, wrapper_context=wrapper_context), + disposition=disposition, + ) + + +def _table_builder(block_id, page_number=0): + return TableBuilder( + kind="table", + raw_html="
    ", + caption=None, + rows=[], + cells=[], + footnote_ids=[], + provenance=ProvenanceBuilder(marker_block_ids=[block_id], page_number=page_number), + canonical_path=None, + ) + + +def _figure_builder(block_id, page_number=0): + return FigureBuilder( + kind="figure", + image_data=None, + caption=None, + footnote_ids=[], + provenance=ProvenanceBuilder(marker_block_ids=[block_id], page_number=page_number), + canonical_path=None, + ) + + +# Pattern A (wrapped) + +def test_pattern_a_table_group_caption_before_table(): + ctx = WrapperContext(wrapper_type="TableGroup", block_id="/p/0/TableGroup/0") + caption_block = _block("/p/0/Caption/0", "Caption", html="Table 1. My table caption.") + table_block = _block("/p/0/Table/0", "Table") + + seq = [ + _cb(caption_block, Disposition.CAPTION_TEXT, wrapper_context=ctx), + _cb(table_block, Disposition.TABLE_SHELL, wrapper_context=ctx), + ] + builder = _table_builder("/p/0/Table/0") + resolve_captions([builder], seq, page_number=0) + + assert builder.caption is not None + assert builder.caption.kind == "caption" + assert builder.caption.label == "Table 1" + assert builder.caption.text == "My table caption." + assert builder.caption.provenance.marker_block_ids == [ + "/p/0/Caption/0", "/p/0/TableGroup/0", + ] + + +def test_pattern_a_figure_group_caption_after_figure(): + ctx = WrapperContext(wrapper_type="FigureGroup", block_id="/p/0/FigureGroup/0") + figure_block = _block("/p/0/Figure/0", "Figure") + caption_block = _block("/p/0/Caption/0", "Caption", html="Figure 2. My figure caption.") + + seq = [ + _cb(figure_block, Disposition.FIGURE_SHELL, wrapper_context=ctx), + _cb(caption_block, Disposition.CAPTION_TEXT, wrapper_context=ctx), + ] + builder = _figure_builder("/p/0/Figure/0") + resolve_captions([builder], seq, page_number=0) + + assert builder.caption is not None + assert builder.caption.label == "Figure 2" + assert builder.caption.text == "My figure caption." + + +def test_pattern_a_no_matching_wrapper_sibling_gives_none(): + ctx = WrapperContext(wrapper_type="TableGroup", block_id="/p/0/TableGroup/0") + table_block = _block("/p/0/Table/0", "Table") + seq = [_cb(table_block, Disposition.TABLE_SHELL, wrapper_context=ctx)] + + builder = _table_builder("/p/0/Table/0") + resolve_captions([builder], seq, page_number=0) + assert builder.caption is None + + +# Pattern B (bare) + +def test_pattern_b_label_text_shell_resolves(): + label = _block("/p/0/SH/0", "SectionHeader", html="Table 3") + text = _block("/p/0/Text/0", "Text", html="Effect of treatment on yield.") + table = _block("/p/0/Table/0", "Table") + + seq = [ + _cb(label, Disposition.CAPTION_LABEL), + _cb(text, Disposition.BODY_PARAGRAPH), + _cb(table, Disposition.TABLE_SHELL), + ] + builder = _table_builder("/p/0/Table/0") + resolve_captions([builder], seq, page_number=0) + + assert builder.caption is not None + assert builder.caption.label == "Table 3" + assert builder.caption.text == "Effect of treatment on yield." + assert builder.caption.trailing_notes is None + assert builder.caption.provenance.marker_block_ids == [ + "/p/0/SH/0", "/p/0/Text/0", + ] + + +def test_pattern_b_trailing_notes_attached(): + label = _block("/p/0/SH/0", "SectionHeader", html="Table 3") + text = _block("/p/0/Text/0", "Text", html="Caption text.") + table = _block("/p/0/Table/0", "Table") + notes = _block("/p/0/Text/1", "Text", html="Note: values are means.") + + seq = [ + _cb(label, Disposition.CAPTION_LABEL), + _cb(text, Disposition.BODY_PARAGRAPH), + _cb(table, Disposition.TABLE_SHELL), + _cb(notes, Disposition.BODY_PARAGRAPH), + ] + builder = _table_builder("/p/0/Table/0") + resolve_captions([builder], seq, page_number=0) + + assert builder.caption.trailing_notes == "Note: values are means." + assert builder.caption.provenance.marker_block_ids == [ + "/p/0/SH/0", "/p/0/Text/0", "/p/0/Text/1", + ] + + +def test_pattern_b_trailing_notes_case_sensitive_no_match(): + label = _block("/p/0/SH/0", "SectionHeader", html="Table 3") + text = _block("/p/0/Text/0", "Text", html="Caption text.") + table = _block("/p/0/Table/0", "Table") + not_notes = _block("/p/0/Text/1", "Text", html="note: lowercase, should not match.") + + seq = [ + _cb(label, Disposition.CAPTION_LABEL), + _cb(text, Disposition.BODY_PARAGRAPH), + _cb(table, Disposition.TABLE_SHELL), + _cb(not_notes, Disposition.BODY_PARAGRAPH), + ] + builder = _table_builder("/p/0/Table/0") + resolve_captions([builder], seq, page_number=0) + + assert builder.caption.trailing_notes is None + + +def test_pattern_b_picture_blocks_transparent(): + label = _block("/p/0/SH/0", "SectionHeader", html="Figure 4") + pic1 = _block("/p/0/Picture/0", "Picture") + text = _block("/p/0/Text/0", "Text", html="Figure caption text.") + pic2 = _block("/p/0/Picture/1", "Picture") + figure = _block("/p/0/Figure/0", "Figure") + + seq = [ + _cb(label, Disposition.CAPTION_LABEL), + _cb(pic1, Disposition.PICTURE), + _cb(text, Disposition.BODY_PARAGRAPH), + _cb(pic2, Disposition.PICTURE), + _cb(figure, Disposition.FIGURE_SHELL), + ] + builder = _figure_builder("/p/0/Figure/0") + resolve_captions([builder], seq, page_number=0) + + assert builder.caption is not None + assert builder.caption.label == "Figure 4" + assert builder.caption.text == "Figure caption text." + + +def test_pattern_b_missing_label_gives_none(): + text1 = _block("/p/0/Text/0", "Text", html="Random paragraph.") + text2 = _block("/p/0/Text/1", "Text", html="Caption-looking text.") + table = _block("/p/0/Table/0", "Table") + + seq = [ + _cb(text1, Disposition.BODY_PARAGRAPH), + _cb(text2, Disposition.BODY_PARAGRAPH), + _cb(table, Disposition.TABLE_SHELL), + ] + builder = _table_builder("/p/0/Table/0") + resolve_captions([builder], seq, page_number=0) + assert builder.caption is None + + +def test_pattern_b_no_caption_text_immediately_before_shell_gives_none(): + label = _block("/p/0/SH/0", "SectionHeader", html="Table 5") + table = _block("/p/0/Table/0", "Table") + + seq = [ + _cb(label, Disposition.CAPTION_LABEL), + _cb(table, Disposition.TABLE_SHELL), + ] + builder = _table_builder("/p/0/Table/0") + resolve_captions([builder], seq, page_number=0) + assert builder.caption is None + + +def test_pattern_b_no_preceding_block_at_all_gives_none(): + table = _block("/p/0/Table/0", "Table") + seq = [_cb(table, Disposition.TABLE_SHELL)] + builder = _table_builder("/p/0/Table/0") + resolve_captions([builder], seq, page_number=0) + assert builder.caption is None + + +def test_pattern_b_wrong_disposition_before_label_position_gives_none(): + label = _block("/p/0/SH/0", "SectionHeader", html="Table 6") + equation = _block("/p/0/Eq/0", "Equation", html="x=y") + table = _block("/p/0/Table/0", "Table") + + seq = [ + _cb(label, Disposition.CAPTION_LABEL), + _cb(equation, Disposition.EQUATION), + _cb(table, Disposition.TABLE_SHELL), + ] + builder = _table_builder("/p/0/Table/0") + resolve_captions([builder], seq, page_number=0) + assert builder.caption is None + +def test_non_table_figure_builders_ignored(): + from betydb_extraction.normalizer.builders.paragraph import ParagraphBuilder + + para = ParagraphBuilder( + kind="paragraph", + text="hi", + provenance=ProvenanceBuilder(marker_block_ids=["/p/0/Text/0"], page_number=0), + canonical_path=None, + ) + seq = [_cb(_block("/p/0/Text/0", "Text"), Disposition.BODY_PARAGRAPH)] + resolve_captions([para], seq, page_number=0) + assert not hasattr(para, "caption") + + +def test_multiple_builders_resolved_independently(): + label1 = _block("/p/0/SH/0", "SectionHeader", html="Table 1") + text1 = _block("/p/0/Text/0", "Text", html="First caption.") + table1 = _block("/p/0/Table/0", "Table") + + label2 = _block("/p/0/SH/1", "SectionHeader", html="Table 2") + text2 = _block("/p/0/Text/1", "Text", html="Second caption.") + table2 = _block("/p/0/Table/1", "Table") + + seq = [ + _cb(label1, Disposition.CAPTION_LABEL), + _cb(text1, Disposition.BODY_PARAGRAPH), + _cb(table1, Disposition.TABLE_SHELL), + _cb(label2, Disposition.CAPTION_LABEL), + _cb(text2, Disposition.BODY_PARAGRAPH), + _cb(table2, Disposition.TABLE_SHELL), + ] + b1 = _table_builder("/p/0/Table/0") + b2 = _table_builder("/p/0/Table/1") + resolve_captions([b1, b2], seq, page_number=0) + + assert b1.caption.text == "First caption." + assert b2.caption.text == "Second caption." \ No newline at end of file diff --git a/tests/normalizer/test_stage5.py b/tests/normalizer/test_stage5.py new file mode 100644 index 0000000..c0d370c --- /dev/null +++ b/tests/normalizer/test_stage5.py @@ -0,0 +1,223 @@ +"""Tests for Stage 5: Table Internal Structure.""" +from __future__ import annotations + +from betydb_extraction.marker_adapter.raw_model import MarkerBlock +from betydb_extraction.normalizer.builders.base import ( + ClassifiedBlock, + Disposition, + UnwrappedBlock, + WrapperContext, +) +from betydb_extraction.normalizer.builders.provenance import ProvenanceBuilder +from betydb_extraction.normalizer.builders.table import TableBuilder +from betydb_extraction.normalizer.internal.stage5 import build_table_structure + + +def _table_builder(raw_html, marker_block_id="/p/0/Table/0"): + return TableBuilder( + kind="table", + raw_html=raw_html, + caption=None, + rows=[], + cells=[], + footnote_ids=[], + provenance=ProvenanceBuilder(marker_block_ids=[marker_block_id], page_number=0), + canonical_path=None, + ) + + +def _shell_block(block_id, bbox=None): + return MarkerBlock(id=block_id, block_type="Table", bbox=bbox, children=None) + + +def _cell_evidence_block(block_id, html, bbox=None): + return MarkerBlock(id=block_id, block_type="TableCell", html=html, bbox=bbox, children=None) + + +def _cb(block, disposition, wrapper_context=None): + return ClassifiedBlock( + unwrapped=UnwrappedBlock(block=block, wrapper_context=wrapper_context), + disposition=disposition, + ) + + +def test_empty_raw_html_gives_empty_rows(): + builder = _table_builder(raw_html="") + build_table_structure([builder], []) + assert builder.rows == [] + + +def test_whitespace_only_raw_html_gives_empty_rows(): + builder = _table_builder(raw_html=" \n ") + build_table_structure([builder], []) + assert builder.rows == [] + + +def test_simple_table_parses_rows_and_cells(): + html = "
    NameValue
    " + builder = _table_builder(raw_html=html) + build_table_structure([builder], []) + + assert len(builder.rows) == 1 + row = builder.rows[0] + assert row.kind == "table_row" + assert len(row.cells) == 2 + + header_cell, data_cell = row.cells + assert header_cell.kind == "table_row_cell" + assert header_cell.text == "Name" + assert header_cell.is_header is True + assert header_cell.structural_notes is None + + assert data_cell.text == "Value" + assert data_cell.is_header is False + + +def test_multiple_rows_preserved_in_order(): + html = ( + "" + "" + "" + "" + "
    R1C1
    R2C1
    R3C1
    " + ) + builder = _table_builder(raw_html=html) + build_table_structure([builder], []) + + assert len(builder.rows) == 3 + assert [r.cells[0].text for r in builder.rows] == ["R1C1", "R2C1", "R3C1"] + + +def test_empty_cell_text_becomes_none(): + html = "
    " + builder = _table_builder(raw_html=html) + build_table_structure([builder], []) + assert builder.rows[0].cells[0].text is None + + +def test_math_wrapper_stripped_inner_content_preserved(): + html = ( + "
    " + "x=2" + "
    " + ) + builder = _table_builder(raw_html=html) + build_table_structure([builder], []) + + cell_text = builder.rows[0].cells[0].text + assert "" not in cell_text + assert "x" in cell_text + assert "=" in cell_text + assert "2" in cell_text + + +def test_math_wrapper_mixed_with_plain_text(): + html = ( + "
    " + "Result: 5 units" + "
    " + ) + builder = _table_builder(raw_html=html) + build_table_structure([builder], []) + text = builder.rows[0].cells[0].text + assert text == "Result: 5 units" + + +def test_bare_table_cells_always_empty(): + builder = _table_builder(raw_html="
    x
    ") + shell = _shell_block("/p/0/Table/0") + seq = [_cb(shell, Disposition.TABLE_SHELL, wrapper_context=None)] + + build_table_structure([builder], seq) + assert builder.cells == [] + + +def test_wrapped_table_collects_matching_evidence_cells(): + ctx = WrapperContext(wrapper_type="TableGroup", block_id="/p/0/TableGroup/0") + shell = _shell_block("/p/0/Table/0") + ev1 = _cell_evidence_block("/p/0/TableCell/0", html="Alpha") + ev2 = _cell_evidence_block("/p/0/TableCell/1", html="Beta") + + seq = [ + _cb(ev1, Disposition.TABLE_CELL_EVIDENCE, wrapper_context=ctx), + _cb(shell, Disposition.TABLE_SHELL, wrapper_context=ctx), + _cb(ev2, Disposition.TABLE_CELL_EVIDENCE, wrapper_context=ctx), + ] + builder = _table_builder(raw_html="
    ") + build_table_structure([builder], seq) + + assert len(builder.cells) == 2 + assert builder.cells[0].kind == "table_cell" + assert builder.cells[0].marker_block_id == "/p/0/TableCell/0" + assert builder.cells[0].text == "Alpha" + assert builder.cells[1].text == "Beta" + + +def test_wrapped_table_evidence_text_not_math_stripped(): + ctx = WrapperContext(wrapper_type="TableGroup", block_id="/p/0/TableGroup/0") + shell = _shell_block("/p/0/Table/0") + ev = _cell_evidence_block("/p/0/TableCell/0", html="5") + + seq = [ + _cb(shell, Disposition.TABLE_SHELL, wrapper_context=ctx), + _cb(ev, Disposition.TABLE_CELL_EVIDENCE, wrapper_context=ctx), + ] + builder = _table_builder(raw_html="
    ") + build_table_structure([builder], seq) + + assert builder.cells[0].text == "5" + + +def test_evidence_cells_from_other_wrapper_id_excluded(): + ctx_this = WrapperContext(wrapper_type="TableGroup", block_id="/p/0/TableGroup/0") + ctx_other = WrapperContext(wrapper_type="TableGroup", block_id="/p/0/TableGroup/1") + shell = _shell_block("/p/0/Table/0") + ev_this = _cell_evidence_block("/p/0/TableCell/0", html="Mine") + ev_other = _cell_evidence_block("/p/0/TableCell/1", html="NotMine") + + seq = [ + _cb(shell, Disposition.TABLE_SHELL, wrapper_context=ctx_this), + _cb(ev_this, Disposition.TABLE_CELL_EVIDENCE, wrapper_context=ctx_this), + _cb(ev_other, Disposition.TABLE_CELL_EVIDENCE, wrapper_context=ctx_other), + ] + builder = _table_builder(raw_html="
    ") + build_table_structure([builder], seq) + + assert len(builder.cells) == 1 + assert builder.cells[0].text == "Mine" + + +def test_evidence_cell_bbox_and_polygon_carried(): + ctx = WrapperContext(wrapper_type="TableGroup", block_id="/p/0/TableGroup/0") + shell = _shell_block("/p/0/Table/0") + ev = _cell_evidence_block( + "/p/0/TableCell/0", html="X", bbox=[1.0, 2.0, 3.0, 4.0] + ) + seq = [ + _cb(shell, Disposition.TABLE_SHELL, wrapper_context=ctx), + _cb(ev, Disposition.TABLE_CELL_EVIDENCE, wrapper_context=ctx), + ] + builder = _table_builder(raw_html="
    ") + build_table_structure([builder], seq) + + assert builder.cells[0].bbox.x0 == 1.0 + +def test_non_table_builders_ignored(): + from betydb_extraction.normalizer.builders.paragraph import ParagraphBuilder + + para = ParagraphBuilder( + kind="paragraph", + text="hi", + provenance=ProvenanceBuilder(marker_block_ids=["/p/0/Text/0"], page_number=0), + canonical_path=None, + ) + build_table_structure([para], []) + assert not hasattr(para, "rows") + + +def test_shell_not_in_classified_seq_defaults_to_bare(): + builder = _table_builder(raw_html="
    x
    ") + build_table_structure([builder], []) + assert builder.cells == [] + assert len(builder.rows) == 1 \ No newline at end of file diff --git a/tests/normalizer/test_stage6.py b/tests/normalizer/test_stage6.py new file mode 100644 index 0000000..411c399 --- /dev/null +++ b/tests/normalizer/test_stage6.py @@ -0,0 +1,203 @@ +"""Tests for Stage 6: Footnote Attachment.""" +from __future__ import annotations + +import pytest + +from betydb_extraction.marker_adapter.raw_model import MarkerBBox +from betydb_extraction.normalizer.builders.base import ClassifiedBlock, Disposition, UnwrappedBlock +from betydb_extraction.normalizer.builders.figure import FigureBuilder +from betydb_extraction.normalizer.builders.footnote import FootnoteBuilder +from betydb_extraction.normalizer.builders.provenance import ProvenanceBuilder +from betydb_extraction.normalizer.builders.table import TableBuilder +from betydb_extraction.normalizer.internal.stage6 import attach_footnotes +from betydb_extraction.marker_adapter.raw_model import MarkerBlock + + +def _bbox(y0, y1, x0=0.0, x1=100.0): + return MarkerBBox(x0=x0, y0=y0, x1=x1, y1=y1) + + +def _footnote_builder(marker_block_id, bbox=None): + return FootnoteBuilder( + kind="footnote", + raw_text="1. Note.", + attached_object_id=None, + provenance=ProvenanceBuilder(marker_block_ids=[marker_block_id], page_number=0, bbox=bbox), + canonical_path=None, + ) + + +def _table_builder(marker_block_id, bbox=None): + return TableBuilder( + kind="table", + raw_html="", + caption=None, + rows=[], + cells=[], + footnote_ids=[], + provenance=ProvenanceBuilder(marker_block_ids=[marker_block_id], page_number=0, bbox=bbox), + canonical_path=None, + ) + + +def _figure_builder(marker_block_id, bbox=None): + return FigureBuilder( + kind="figure", + image_data=None, + caption=None, + footnote_ids=[], + provenance=ProvenanceBuilder(marker_block_ids=[marker_block_id], page_number=0, bbox=bbox), + canonical_path=None, + ) + + +def _seq_entry(marker_block_id, block_type="Table"): + block = MarkerBlock(id=marker_block_id, block_type=block_type, children=None) + return ClassifiedBlock( + unwrapped=UnwrappedBlock(block=block, wrapper_context=None), + disposition=Disposition.TABLE_SHELL, + ) + + +#Basic attachment + +def test_footnote_attaches_to_table_above_it(): + table = _table_builder("/p/0/Table/0", bbox=_bbox(y0=100, y1=200)) + footnote = _footnote_builder("/p/0/Footnote/0", bbox=_bbox(y0=250, y1=270)) + seq = [_seq_entry("/p/0/Table/0"), _seq_entry("/p/0/Footnote/0")] + + attach_footnotes([table, footnote], seq, page_number=0) + + assert footnote.attached_object_id == "/p/0/Table/0" + assert table.footnote_ids == ["/p/0/Footnote/0"] + + +def test_footnote_attaches_to_figure_above_it(): + figure = _figure_builder("/p/0/Figure/0", bbox=_bbox(y0=100, y1=200)) + footnote = _footnote_builder("/p/0/Footnote/0", bbox=_bbox(y0=250, y1=270)) + seq = [_seq_entry("/p/0/Figure/0"), _seq_entry("/p/0/Footnote/0")] + + attach_footnotes([figure, footnote], seq, page_number=0) + + assert footnote.attached_object_id == "/p/0/Figure/0" + assert figure.footnote_ids == ["/p/0/Footnote/0"] + + +def test_candidate_below_footnote_not_eligible(): + table = _table_builder("/p/0/Table/0", bbox=_bbox(y0=300, y1=400)) + footnote = _footnote_builder("/p/0/Footnote/0", bbox=_bbox(y0=250, y1=270)) + seq = [_seq_entry("/p/0/Table/0"), _seq_entry("/p/0/Footnote/0")] + + attach_footnotes([table, footnote], seq, page_number=0) + assert footnote.attached_object_id is None + assert table.footnote_ids == [] + + +def test_equal_y1_and_y0_not_eligible_strict_inequality(): + table = _table_builder("/p/0/Table/0", bbox=_bbox(y0=100, y1=250)) + footnote = _footnote_builder("/p/0/Footnote/0", bbox=_bbox(y0=250, y1=270)) + seq = [_seq_entry("/p/0/Table/0"), _seq_entry("/p/0/Footnote/0")] + + attach_footnotes([table, footnote], seq, page_number=0) + assert footnote.attached_object_id is None + + +def test_no_candidates_leaves_attached_object_id_none(): + footnote = _footnote_builder("/p/0/Footnote/0", bbox=_bbox(y0=250, y1=270)) + seq = [_seq_entry("/p/0/Footnote/0")] + + attach_footnotes([footnote], seq, page_number=0) + assert footnote.attached_object_id is None + + +def test_footnote_missing_bbox_leaves_none(): + table = _table_builder("/p/0/Table/0", bbox=_bbox(y0=100, y1=200)) + footnote = _footnote_builder("/p/0/Footnote/0", bbox=None) + seq = [_seq_entry("/p/0/Table/0"), _seq_entry("/p/0/Footnote/0")] + + attach_footnotes([table, footnote], seq, page_number=0) + assert footnote.attached_object_id is None + assert table.footnote_ids == [] + + +def test_candidate_missing_bbox_skipped(): + table_no_bbox = _table_builder("/p/0/Table/0", bbox=None) + table_with_bbox = _table_builder("/p/0/Table/1", bbox=_bbox(y0=100, y1=200)) + footnote = _footnote_builder("/p/0/Footnote/0", bbox=_bbox(y0=250, y1=270)) + seq = [ + _seq_entry("/p/0/Table/0"), + _seq_entry("/p/0/Table/1"), + _seq_entry("/p/0/Footnote/0"), + ] + + attach_footnotes([table_no_bbox, table_with_bbox, footnote], seq, page_number=0) + assert footnote.attached_object_id == "/p/0/Table/1" + + +def test_closest_candidate_by_max_y1_selected(): + far_table = _table_builder("/p/0/Table/0", bbox=_bbox(y0=10, y1=50)) + near_table = _table_builder("/p/0/Table/1", bbox=_bbox(y0=100, y1=200)) + footnote = _footnote_builder("/p/0/Footnote/0", bbox=_bbox(y0=250, y1=270)) + seq = [ + _seq_entry("/p/0/Table/0"), + _seq_entry("/p/0/Table/1"), + _seq_entry("/p/0/Footnote/0"), + ] + + attach_footnotes([far_table, near_table, footnote], seq, page_number=0) + assert footnote.attached_object_id == "/p/0/Table/1" + + +#Tie-break by seq index + +def test_tie_break_by_smaller_seq_index(): + table_a = _table_builder("/p/0/Table/0", bbox=_bbox(y0=100, y1=200)) + table_b = _table_builder("/p/0/Table/1", bbox=_bbox(y0=100, y1=200)) # same y1 + footnote = _footnote_builder("/p/0/Footnote/0", bbox=_bbox(y0=250, y1=270)) + + # table_b appears earlier in classified_seq than table_a. + seq = [ + _seq_entry("/p/0/Table/1"), + _seq_entry("/p/0/Table/0"), + _seq_entry("/p/0/Footnote/0"), + ] + + attach_footnotes([table_a, table_b, footnote], seq, page_number=0) + assert footnote.attached_object_id == "/p/0/Table/1" + assert table_b.footnote_ids == ["/p/0/Footnote/0"] + assert table_a.footnote_ids == [] + + +#Multiple footnotes, atomic both-sides update + +def test_multiple_footnotes_each_attached_independently(): + table = _table_builder("/p/0/Table/0", bbox=_bbox(y0=100, y1=200)) + fn1 = _footnote_builder("/p/0/Footnote/0", bbox=_bbox(y0=250, y1=270)) + fn2 = _footnote_builder("/p/0/Footnote/1", bbox=_bbox(y0=280, y1=300)) + seq = [ + _seq_entry("/p/0/Table/0"), + _seq_entry("/p/0/Footnote/0"), + _seq_entry("/p/0/Footnote/1"), + ] + + attach_footnotes([table, fn1, fn2], seq, page_number=0) + + assert fn1.attached_object_id == "/p/0/Table/0" + assert fn2.attached_object_id == "/p/0/Table/0" + assert set(table.footnote_ids) == {"/p/0/Footnote/0", "/p/0/Footnote/1"} + + +def test_no_footnotes_no_op(): + table = _table_builder("/p/0/Table/0", bbox=_bbox(y0=100, y1=200)) + seq = [_seq_entry("/p/0/Table/0")] + attach_footnotes([table], seq, page_number=0) + assert table.footnote_ids == [] + + +def test_candidate_not_in_classified_seq_raises(): + table = _table_builder("/p/0/Table/0", bbox=_bbox(y0=100, y1=200)) + footnote = _footnote_builder("/p/0/Footnote/0", bbox=_bbox(y0=250, y1=270)) + seq = [_seq_entry("/p/0/Footnote/0")] # table missing from seq + + with pytest.raises(ValueError): + attach_footnotes([table, footnote], seq, page_number=0) \ No newline at end of file diff --git a/tests/normalizer/test_stage7.py b/tests/normalizer/test_stage7.py new file mode 100644 index 0000000..b570ce4 --- /dev/null +++ b/tests/normalizer/test_stage7.py @@ -0,0 +1,299 @@ +"""Tests for Stage 7: Section Tree Assembly.""" +from __future__ import annotations + +from betydb_extraction.marker_adapter.raw_model import MarkerBlock +from betydb_extraction.normalizer.builders.base import ( + ClassifiedBlock, + Disposition, + UnwrappedBlock, +) +from betydb_extraction.normalizer.builders.page import PageBuilder +from betydb_extraction.normalizer.builders.page_footer import PageFooterBuilder +from betydb_extraction.normalizer.builders.page_header import PageHeaderBuilder +from betydb_extraction.normalizer.builders.paragraph import ParagraphBuilder +from betydb_extraction.normalizer.builders.provenance import ProvenanceBuilder +from betydb_extraction.normalizer.builders.section import SectionBuilder +from betydb_extraction.normalizer.internal.stage3 import build_leaf_builders +from betydb_extraction.normalizer.internal.stage7 import assemble_section_tree + + +def _block(block_id, block_type, html="", section_hierarchy=None): + return MarkerBlock( + id=block_id, + block_type=block_type, + html=html, + section_hierarchy=section_hierarchy or {}, + children=None, + ) + + +def _make_page(blocks_with_dispositions, page_index): + seq = [ + ClassifiedBlock( + unwrapped=UnwrappedBlock(block=b, wrapper_context=None), + disposition=d, + ) + for b, d in blocks_with_dispositions + ] + builders = build_leaf_builders(seq, page_number=page_index) + page_builder = PageBuilder( + page_number=page_index, + is_front_matter=False, + provenance=ProvenanceBuilder( + marker_block_ids=[f"/page/{page_index}/Page/0"], page_number=page_index + ), + children=builders, + ) + return page_builder, seq + + +def test_no_headings_leaves_children_untouched(): + text = _block("/p/0/Text/0", "Text", html="hello") + pb, seq = _make_page([(text, Disposition.BODY_PARAGRAPH)], page_index=0) + original_children = list(pb.children) + + assemble_section_tree([pb], [seq]) + + assert pb.children == original_children + assert pb.children[0].provenance.section_path == [] + + +#Single top-level section + +def test_single_top_level_section_with_content(): + heading = _block("/p/0/SH/0", "SectionHeader", html="Intro") + para = _block( + "/p/0/Text/0", "Text", html="body", + section_hierarchy={"0": "/p/0/SH/0"}, + ) + pb, seq = _make_page( + [(heading, Disposition.GENUINE_SECTION_HEADER), (para, Disposition.BODY_PARAGRAPH)], + page_index=0, + ) + + assemble_section_tree([pb], [seq]) + + assert len(pb.children) == 1 + section = pb.children[0] + assert isinstance(section, SectionBuilder) + assert section.kind == "section" + assert section.depth == 0 + assert section.heading_text == "Intro" + assert section.heading_marker_block_id == "/p/0/SH/0" + assert section.provenance.section_path == [] + + assert len(section.children) == 1 + para_builder = section.children[0] + assert isinstance(para_builder, ParagraphBuilder) + assert para_builder.provenance.section_path == ["/p/0/SH/0"] + + +# Nested sections + +def test_nested_two_level_sections(): + h0 = _block("/p/0/SH/0", "SectionHeader", html="Parent") + h1 = _block("/p/0/SH/1", "SectionHeader", html="Child") + content = _block( + "/p/0/Text/0", "Text", html="deep content", + section_hierarchy={"0": "/p/0/SH/0", "1": "/p/0/SH/1"}, + ) + pb, seq = _make_page( + [ + (h0, Disposition.GENUINE_SECTION_HEADER), + (h1, Disposition.GENUINE_SECTION_HEADER), + (content, Disposition.BODY_PARAGRAPH), + ], + page_index=0, + ) + + assemble_section_tree([pb], [seq]) + + assert len(pb.children) == 1 + parent_section = pb.children[0] + assert parent_section.heading_marker_block_id == "/p/0/SH/0" + assert parent_section.depth == 0 + assert parent_section.provenance.section_path == [] + + assert len(parent_section.children) == 1 + child_section = parent_section.children[0] + assert isinstance(child_section, SectionBuilder) + assert child_section.heading_marker_block_id == "/p/0/SH/1" + assert child_section.depth == 1 + assert child_section.provenance.section_path == ["/p/0/SH/0"] + + assert len(child_section.children) == 1 + leaf = child_section.children[0] + assert leaf.provenance.section_path == ["/p/0/SH/0", "/p/0/SH/1"] + + +# Omission rule + +def test_heading_with_no_governed_content_is_omitted(): + heading = _block("/p/0/SH/0", "SectionHeader", html="Empty Section") + ungoverned = _block("/p/0/Text/0", "Text", html="not in any section") + pb, seq = _make_page( + [ + (heading, Disposition.GENUINE_SECTION_HEADER), + (ungoverned, Disposition.BODY_PARAGRAPH), + ], + page_index=0, + ) + + assemble_section_tree([pb], [seq]) + + assert len(pb.children) == 1 + assert isinstance(pb.children[0], ParagraphBuilder) + assert pb.children[0].provenance.section_path == [] + + +# Multi-page section spanning + +def test_section_spans_pages_content_attached_to_heading_page(): + heading = _block("/p/0/SH/0", "SectionHeader", html="Methods") + page0, seq0 = _make_page([(heading, Disposition.GENUINE_SECTION_HEADER)], page_index=0) + + content = _block( + "/p/1/Text/0", "Text", html="continued on next page", + section_hierarchy={"0": "/p/0/SH/0"}, + ) + page1, seq1 = _make_page([(content, Disposition.BODY_PARAGRAPH)], page_index=1) + + assemble_section_tree([page0, page1], [seq0, seq1]) + + assert len(page0.children) == 1 + section = page0.children[0] + assert isinstance(section, SectionBuilder) + + assert page1.children == [] + assert len(section.children) == 1 + assert section.children[0].provenance.marker_block_ids == ["/p/1/Text/0"] + assert section.children[0].provenance.section_path == ["/p/0/SH/0"] + + +#PageHeader / PageFooter always page-level + +def test_page_header_forced_to_page_level_even_if_governed(): + heading = _block("/p/0/SH/0", "SectionHeader", html="Section") + header = _block( + "/p/0/PH/0", "PageHeader", html="Journal Name", + section_hierarchy={"0": "/p/0/SH/0"}, + ) + para = _block( + "/p/0/Text/0", "Text", html="content", + section_hierarchy={"0": "/p/0/SH/0"}, + ) + pb, seq = _make_page( + [ + (heading, Disposition.GENUINE_SECTION_HEADER), + (header, Disposition.PAGE_HEADER), + (para, Disposition.BODY_PARAGRAPH), + ], + page_index=0, + ) + + assemble_section_tree([pb], [seq]) + header_builders = [b for b in pb.children if isinstance(b, PageHeaderBuilder)] + assert len(header_builders) == 1 + assert header_builders[0].provenance.section_path == [] + + section = [b for b in pb.children if isinstance(b, SectionBuilder)][0] + assert all(not isinstance(c, PageHeaderBuilder) for c in section.children) + assert len(section.children) == 1 # only the paragraph + + +def test_page_footer_forced_to_page_level(): + heading = _block("/p/0/SH/0", "SectionHeader", html="Section") + footer = _block( + "/p/0/PF/0", "PageFooter", html="Page 1", + section_hierarchy={"0": "/p/0/SH/0"}, + ) + para = _block( + "/p/0/Text/0", "Text", html="content", + section_hierarchy={"0": "/p/0/SH/0"}, + ) + pb, seq = _make_page( + [ + (heading, Disposition.GENUINE_SECTION_HEADER), + (footer, Disposition.PAGE_FOOTER), + (para, Disposition.BODY_PARAGRAPH), + ], + page_index=0, + ) + + assemble_section_tree([pb], [seq]) + + footer_builders = [b for b in pb.children if isinstance(b, PageFooterBuilder)] + assert len(footer_builders) == 1 + assert footer_builders[0].provenance.section_path == [] + + +# Ordering (Step 6) + +def test_children_ordered_by_classified_sequence_position(): + text_before = _block("/p/0/Text/0", "Text", html="before section") + heading = _block("/p/0/SH/0", "SectionHeader", html="Section") + para = _block( + "/p/0/Text/1", "Text", html="inside", + section_hierarchy={"0": "/p/0/SH/0"}, + ) + pb, seq = _make_page( + [ + (text_before, Disposition.BODY_PARAGRAPH), + (heading, Disposition.GENUINE_SECTION_HEADER), + (para, Disposition.BODY_PARAGRAPH), + ], + page_index=0, + ) + + assemble_section_tree([pb], [seq]) + + assert len(pb.children) == 2 + assert isinstance(pb.children[0], ParagraphBuilder) + assert pb.children[0].provenance.marker_block_ids == ["/p/0/Text/0"] + assert isinstance(pb.children[1], SectionBuilder) + + +def test_two_top_level_sections_ordered_by_heading_position(): + h_second_in_doc = _block("/p/0/SH/0", "SectionHeader", html="First Heading") + para1 = _block( + "/p/0/Text/0", "Text", html="p1", + section_hierarchy={"0": "/p/0/SH/0"}, + ) + h_second = _block("/p/0/SH/1", "SectionHeader", html="Second Heading") + para2 = _block( + "/p/0/Text/1", "Text", html="p2", + section_hierarchy={"0": "/p/0/SH/1"}, + ) + pb, seq = _make_page( + [ + (h_second_in_doc, Disposition.GENUINE_SECTION_HEADER), + (para1, Disposition.BODY_PARAGRAPH), + (h_second, Disposition.GENUINE_SECTION_HEADER), + (para2, Disposition.BODY_PARAGRAPH), + ], + page_index=0, + ) + + assemble_section_tree([pb], [seq]) + + assert len(pb.children) == 2 + assert pb.children[0].heading_marker_block_id == "/p/0/SH/0" + assert pb.children[1].heading_marker_block_id == "/p/0/SH/1" + + +#Invariant sanity + +def test_section_builder_heading_id_matches_provenance_marker_block_ids(): + heading = _block("/p/0/SH/0", "SectionHeader", html="X") + para = _block( + "/p/0/Text/0", "Text", html="y", + section_hierarchy={"0": "/p/0/SH/0"}, + ) + pb, seq = _make_page( + [(heading, Disposition.GENUINE_SECTION_HEADER), (para, Disposition.BODY_PARAGRAPH)], + page_index=0, + ) + assemble_section_tree([pb], [seq]) + + section = pb.children[0] + assert section.heading_marker_block_id == section.provenance.marker_block_ids[0] \ No newline at end of file diff --git a/tests/normalizer/test_stage8.py b/tests/normalizer/test_stage8.py new file mode 100644 index 0000000..3fa40d2 --- /dev/null +++ b/tests/normalizer/test_stage8.py @@ -0,0 +1,225 @@ +"""Tests for Stage 8: Global Reading-Order Assignment.""" +from __future__ import annotations + +from betydb_extraction.normalizer.builders.caption import CaptionBuilder +from betydb_extraction.normalizer.builders.figure import FigureBuilder +from betydb_extraction.normalizer.builders.page import PageBuilder +from betydb_extraction.normalizer.builders.paragraph import ParagraphBuilder +from betydb_extraction.normalizer.builders.provenance import ProvenanceBuilder +from betydb_extraction.normalizer.builders.section import SectionBuilder +from betydb_extraction.normalizer.builders.table import TableBuilder +from betydb_extraction.normalizer.internal.stage8 import assign_reading_order + + +def _para(marker_block_id="/p/0/Text/0"): + return ParagraphBuilder( + kind="paragraph", + text="hi", + provenance=ProvenanceBuilder(marker_block_ids=[marker_block_id]), + canonical_path=None, + ) + + +def _table(marker_block_id="/p/0/Table/0", caption=None): + return TableBuilder( + kind="table", + raw_html="
    ", + caption=caption, + rows=[], + cells=[], + footnote_ids=[], + provenance=ProvenanceBuilder(marker_block_ids=[marker_block_id]), + canonical_path=None, + ) + + +def _figure(marker_block_id="/p/0/Figure/0", caption=None): + return FigureBuilder( + kind="figure", + image_data=None, + caption=caption, + footnote_ids=[], + provenance=ProvenanceBuilder(marker_block_ids=[marker_block_id]), + canonical_path=None, + ) + + +def _caption(marker_block_id="/p/0/Caption/0"): + return CaptionBuilder( + kind="caption", + label="Table 1", + text="A caption", + trailing_notes=None, + provenance=ProvenanceBuilder(marker_block_ids=[marker_block_id]), + canonical_path=None, + ) + + +def _section(heading_id="/p/0/SH/0", children=None): + return SectionBuilder( + kind="section", + heading_text="Heading", + depth=0, + heading_marker_block_id=heading_id, + provenance=ProvenanceBuilder(marker_block_ids=[heading_id]), + children=children if children is not None else [], + ) + + +def _page(page_number, children=None): + return PageBuilder( + page_number=page_number, + is_front_matter=False, + provenance=ProvenanceBuilder(marker_block_ids=[f"/page/{page_number}/Page/0"], page_number=page_number), + children=children if children is not None else [], + ) + + +# Basic single-page traversal + +def test_single_leaf_gets_sequential_indices(): + para = _para() + page = _page(0, children=[para]) + + assign_reading_order([page]) + + assert page.provenance.reading_order_index == 0 + assert para.provenance.reading_order_index == 1 + + +def test_multiple_leaves_sequential_in_order(): + p1, p2, p3 = _para("/a"), _para("/b"), _para("/c") + page = _page(0, children=[p1, p2, p3]) + + assign_reading_order([page]) + + assert page.provenance.reading_order_index == 0 + assert p1.provenance.reading_order_index == 1 + assert p2.provenance.reading_order_index == 2 + assert p3.provenance.reading_order_index == 3 + + +#Nested sections + +def test_section_recursion_before_siblings(): + inner_para = _para("/inner") + section = _section(children=[inner_para]) + after_para = _para("/after") + page = _page(0, children=[section, after_para]) + + assign_reading_order([page]) + + assert page.provenance.reading_order_index == 0 + assert section.provenance.reading_order_index == 1 + assert inner_para.provenance.reading_order_index == 2 + assert after_para.provenance.reading_order_index == 3 + + +def test_nested_two_level_sections(): + leaf = _para("/leaf") + inner_section = _section(heading_id="/inner_sh", children=[leaf]) + outer_section = _section(heading_id="/outer_sh", children=[inner_section]) + page = _page(0, children=[outer_section]) + + assign_reading_order([page]) + + assert page.provenance.reading_order_index == 0 + assert outer_section.provenance.reading_order_index == 1 + assert inner_section.provenance.reading_order_index == 2 + assert leaf.provenance.reading_order_index == 3 + + +# Caption handling + +def test_table_with_caption_assigns_caption_index_after_table(): + caption = _caption() + table = _table(caption=caption) + page = _page(0, children=[table]) + + assign_reading_order([page]) + + assert page.provenance.reading_order_index == 0 + assert table.provenance.reading_order_index == 1 + assert caption.provenance.reading_order_index == 2 + + +def test_figure_with_caption_assigns_caption_index_after_figure(): + caption = _caption() + figure = _figure(caption=caption) + page = _page(0, children=[figure]) + + assign_reading_order([page]) + + assert figure.provenance.reading_order_index == 1 + assert caption.provenance.reading_order_index == 2 + + +def test_table_without_caption_no_error_no_extra_index(): + table = _table(caption=None) + after = _para("/after") + page = _page(0, children=[table, after]) + + assign_reading_order([page]) + + assert table.provenance.reading_order_index == 1 + assert after.provenance.reading_order_index == 2 # no gap for missing caption + + +def test_caption_between_table_and_next_sibling(): + caption = _caption() + table = _table(caption=caption) + next_para = _para("/next") + page = _page(0, children=[table, next_para]) + + assign_reading_order([page]) + + assert table.provenance.reading_order_index == 1 + assert caption.provenance.reading_order_index == 2 + assert next_para.provenance.reading_order_index == 3 + + +#Multi-page: shared counter, sorted by page_number─ + +def test_counter_shared_and_continues_across_pages(): + p0_para = _para("/p0") + p1_para = _para("/p1") + page0 = _page(0, children=[p0_para]) + page1 = _page(1, children=[p1_para]) + + assign_reading_order([page0, page1]) + + assert page0.provenance.reading_order_index == 0 + assert p0_para.provenance.reading_order_index == 1 + assert page1.provenance.reading_order_index == 2 + assert p1_para.provenance.reading_order_index == 3 + + +def test_pages_processed_in_page_number_order_regardless_of_input_order(): + p0_para = _para("/p0") + p1_para = _para("/p1") + page0 = _page(0, children=[p0_para]) + page1 = _page(1, children=[p1_para]) + + # Deliberately pass out of order. + assign_reading_order([page1, page0]) + + assert page0.provenance.reading_order_index < page1.provenance.reading_order_index + assert p0_para.provenance.reading_order_index < p1_para.provenance.reading_order_index + + + +def test_all_assigned_indices_unique_and_strictly_increasing(): + leaf1 = _para("/a") + section = _section(children=[_para("/b"), _para("/c")]) + leaf2 = _para("/d") + page0 = _page(0, children=[leaf1, section, leaf2]) + page1 = _page(1, children=[_para("/e")]) + + assign_reading_order([page0, page1]) + + all_builders = [page0, leaf1, section, *section.children, leaf2, page1, *page1.children] + indices = [b.provenance.reading_order_index for b in all_builders] + + assert all(i is not None for i in indices) + assert indices == sorted(indices) + assert len(set(indices)) == len(indices) \ No newline at end of file diff --git a/tests/normalizer/test_stage9.py b/tests/normalizer/test_stage9.py new file mode 100644 index 0000000..9fb767d --- /dev/null +++ b/tests/normalizer/test_stage9.py @@ -0,0 +1,297 @@ +"""Tests for Stage 9: Canonical Path Computation.""" +from __future__ import annotations + +import pytest + +from betydb_extraction.normalizer.builders.caption import CaptionBuilder +from betydb_extraction.normalizer.builders.equation import EquationBuilder +from betydb_extraction.normalizer.builders.figure import FigureBuilder +from betydb_extraction.normalizer.builders.footnote import FootnoteBuilder +from betydb_extraction.normalizer.builders.page import PageBuilder +from betydb_extraction.normalizer.builders.page_footer import PageFooterBuilder +from betydb_extraction.normalizer.builders.page_header import PageHeaderBuilder +from betydb_extraction.normalizer.builders.paragraph import ParagraphBuilder +from betydb_extraction.normalizer.builders.provenance import ProvenanceBuilder +from betydb_extraction.normalizer.builders.reference import ReferenceBuilder +from betydb_extraction.normalizer.builders.section import SectionBuilder +from betydb_extraction.normalizer.builders.table import ( + TableBuilder, + TableCellBuilder, + TableRowBuilder, + TableRowCellBuilder, +) +from betydb_extraction.normalizer.internal.stage9 import compute_canonical_paths + + +def _prov(marker_block_ids, page_number=0, roi=None, section_path=None): + return ProvenanceBuilder( + marker_block_ids=marker_block_ids, + page_number=page_number, + reading_order_index=roi, + section_path=section_path or [], + ) + + +def _page(page_number, children=None): + return PageBuilder( + page_number=page_number, + is_front_matter=False, + provenance=_prov([f"/page/{page_number}/Page/0"], page_number=page_number, roi=0), + children=children if children is not None else [], + ) + + +def _para(mbid, page_number=0, roi=1): + return ParagraphBuilder( + kind="paragraph", text="x", + provenance=_prov([mbid], page_number=page_number, roi=roi), + canonical_path=None, + ) + + +def _equation(mbid, page_number=0, roi=1): + return EquationBuilder( + kind="equation", raw_math="x=y", equation_number=None, + provenance=_prov([mbid], page_number=page_number, roi=roi), + canonical_path=None, + ) + + +def _footnote(mbid, page_number=0, roi=1): + return FootnoteBuilder( + kind="footnote", raw_text="note", attached_object_id=None, + provenance=_prov([mbid], page_number=page_number, roi=roi), + canonical_path=None, + ) + + +def _reference(mbid, page_number=0, roi=1): + return ReferenceBuilder( + kind="reference", raw_text="ref", + provenance=_prov([mbid], page_number=page_number, roi=roi), + canonical_path=None, + ) + + +def _page_header(mbid, page_number=0, roi=1): + return PageHeaderBuilder( + kind="page_header", raw_text="hdr", + provenance=_prov([mbid], page_number=page_number, roi=roi), + canonical_path=None, + ) + + +def _page_footer(mbid, page_number=0, roi=1): + return PageFooterBuilder( + kind="page_footer", raw_text="ftr", + provenance=_prov([mbid], page_number=page_number, roi=roi), + canonical_path=None, + ) + + +def _table(mbid, page_number=0, roi=1, rows=None, cells=None, caption=None): + return TableBuilder( + kind="table", raw_html="
    ", caption=caption, + rows=rows or [], cells=cells or [], footnote_ids=[], + provenance=_prov([mbid], page_number=page_number, roi=roi), + canonical_path=None, + ) + + +def _figure(mbid, page_number=0, roi=1, caption=None): + return FigureBuilder( + kind="figure", image_data=None, caption=caption, footnote_ids=[], + provenance=_prov([mbid], page_number=page_number, roi=roi), + canonical_path=None, + ) + + +def _caption(mbid="/p/0/Caption/0"): + return CaptionBuilder( + kind="caption", label="Table 1", text="A caption", trailing_notes=None, + provenance=_prov([mbid]), + canonical_path=None, + ) + + +def _section(heading_id, page_number=0, children=None): + return SectionBuilder( + kind="section", heading_text="H", depth=0, + heading_marker_block_id=heading_id, + provenance=_prov([heading_id], page_number=page_number), + children=children if children is not None else [], + ) + + +def _row(cells=None): + return TableRowBuilder(kind="table_row", cells=cells or [], canonical_path=None) + + +def _row_cell(text="x", is_header=False): + return TableRowCellBuilder( + kind="table_row_cell", text=text, is_header=is_header, + structural_notes=None, canonical_path=None, + ) + + +def _flat_cell(mbid, text="x"): + return TableCellBuilder( + kind="table_cell", marker_block_id=mbid, text=text, + bbox=None, polygon=None, canonical_path=None, + ) + + +def test_page_canonical_path(): + page = _page(3) + compute_canonical_paths([page]) + assert page.canonical_path == "/page/3" + + +def test_paragraph_uses_reading_order_index(): + page = _page(0, children=[_para("/p/0/Text/0", roi=5)]) + compute_canonical_paths([page]) + assert page.children[0].canonical_path == "/page/0/paragraph/5" + + +def test_equation_uses_reading_order_index(): + page = _page(0, children=[_equation("/p/0/Eq/0", roi=7)]) + compute_canonical_paths([page]) + assert page.children[0].canonical_path == "/page/0/equation/7" + + +def test_reference_uses_reading_order_index(): + page = _page(0, children=[_reference("/p/0/LI/0", roi=9)]) + compute_canonical_paths([page]) + assert page.children[0].canonical_path == "/page/0/reference/9" + + +def test_footnote_uses_marker_block_id(): + page = _page(0, children=[_footnote("/p/0/Footnote/0")]) + compute_canonical_paths([page]) + assert page.children[0].canonical_path == "/page/0/footnote//p/0/Footnote/0" + + +def test_page_header_uses_marker_block_id(): + page = _page(0, children=[_page_header("/p/0/PH/0")]) + compute_canonical_paths([page]) + assert page.children[0].canonical_path == "/page/0/page_header//p/0/PH/0" + + +def test_page_footer_uses_marker_block_id(): + page = _page(0, children=[_page_footer("/p/0/PF/0")]) + compute_canonical_paths([page]) + assert page.children[0].canonical_path == "/page/0/page_footer//p/0/PF/0" + + +def test_table_path_and_row_cell_paths(): + row = _row(cells=[_row_cell("a"), _row_cell("b")]) + table = _table("/p/0/Table/0", rows=[row]) + page = _page(0, children=[table]) + compute_canonical_paths([page]) + + assert table.canonical_path == "/page/0/table//p/0/Table/0" + assert row.canonical_path == "/page/0/table//p/0/Table/0/row/0" + assert row.cells[0].canonical_path == "/page/0/table//p/0/Table/0/row/0/cell/0" + assert row.cells[1].canonical_path == "/page/0/table//p/0/Table/0/row/0/cell/1" + + +def test_table_flat_cell_evidence_path(): + flat_cell = _flat_cell("/p/0/TableCell/0") + table = _table("/p/0/Table/0", cells=[flat_cell]) + page = _page(0, children=[table]) + compute_canonical_paths([page]) + + assert flat_cell.canonical_path == "/page/0/table//p/0/Table/0/cell_evidence//p/0/TableCell/0" + + +def test_table_caption_path_and_section_path_copied(): + caption = _caption() + table = _table("/p/0/Table/0", caption=caption) + table.provenance.section_path = ["/p/0/SH/0"] + page = _page(0, children=[table]) + compute_canonical_paths([page]) + + assert caption.canonical_path == "/page/0/table//p/0/Table/0/caption" + assert caption.provenance.section_path == ["/p/0/SH/0"] + + +def test_figure_caption_path(): + caption = _caption("/p/0/Caption/1") + figure = _figure("/p/0/Figure/0", caption=caption) + figure.provenance.section_path = ["/p/0/SH/1"] + page = _page(0, children=[figure]) + compute_canonical_paths([page]) + + assert figure.canonical_path == "/page/0/figure//p/0/Figure/0" + assert caption.canonical_path == "/page/0/figure//p/0/Figure/0/caption" + assert caption.provenance.section_path == ["/p/0/SH/1"] + + +def test_figure_no_caption_no_error(): + figure = _figure("/p/0/Figure/0", caption=None) + page = _page(0, children=[figure]) + compute_canonical_paths([page]) + assert figure.canonical_path == "/page/0/figure//p/0/Figure/0" + + +def test_section_path_uses_own_page_number(): + section = _section("/p/0/SH/0", page_number=0) + page = _page(0, children=[section]) + compute_canonical_paths([page]) + assert section.canonical_path == "/page/0/section//p/0/SH/0" + + +def test_leaf_inside_section_uses_own_page_number_not_sections(): + leaf = _para("/p/1/Text/0", page_number=1, roi=2) + section = _section("/p/0/SH/0", page_number=0, children=[leaf]) + page0 = _page(0, children=[section]) + page1 = _page(1, children=[]) + + compute_canonical_paths([page0, page1]) + + assert section.canonical_path == "/page/0/section//p/0/SH/0" + assert leaf.canonical_path == "/page/1/paragraph/2" + + +def test_nested_sections_each_get_own_path(): + inner_leaf = _para("/p/0/Text/0", roi=3) + inner_section = _section("/p/0/SH/1", children=[inner_leaf]) + outer_section = _section("/p/0/SH/0", children=[inner_section]) + page = _page(0, children=[outer_section]) + + compute_canonical_paths([page]) + + assert outer_section.canonical_path == "/page/0/section//p/0/SH/0" + assert inner_section.canonical_path == "/page/0/section//p/0/SH/1" + assert inner_leaf.canonical_path == "/page/0/paragraph/3" + + +# Missing prerequisites raise + +def test_missing_reading_order_index_raises(): + para = _para("/p/0/Text/0", roi=None) + page = _page(0, children=[para]) + with pytest.raises(ValueError): + compute_canonical_paths([page]) + + +def test_missing_marker_block_ids_raises(): + footnote = _footnote("/p/0/Footnote/0") + footnote.provenance.marker_block_ids = [] + page = _page(0, children=[footnote]) + with pytest.raises(ValueError): + compute_canonical_paths([page]) + + +# Multi-page ordering + +def test_pages_processed_in_page_number_order(): + page0 = _page(0, children=[_para("/p/0/Text/0", page_number=0, roi=1)]) + page1 = _page(1, children=[_para("/p/1/Text/0", page_number=1, roi=1)]) + + compute_canonical_paths([page1, page0]) # deliberately out of order + + assert page0.canonical_path == "/page/0" + assert page1.canonical_path == "/page/1" + assert page0.children[0].canonical_path == "/page/0/paragraph/1" + assert page1.children[0].canonical_path == "/page/1/paragraph/1" \ No newline at end of file