diff --git a/.gitignore b/.gitignore
index f87aa10..818082d 100644
--- a/.gitignore
+++ b/.gitignore
@@ -35,3 +35,7 @@ CLAUDE.md
 # Local data
 static/
 storage/
+
+# Large embedding caches and test artifacts
+data/ref_cache/
+apps/web/test-results/
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..8f43228
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,21 @@
+This is free and unencumbered software released into the public domain.
+
+Anyone is free to copy, modify, publish, use, compile, sell, or distribute
+this software, either in source code form or as a compiled binary, for any
+purpose, commercial or non-commercial, and by any means.
+
+In jurisdictions that recognize copyright laws, the author or authors of this
+software dedicate any and all copyright interest in the software to the public
+domain. We make this dedication for the benefit of the public at large and to
+the detriment of our heirs and successors. We intend this dedication to be an
+overt act of relinquishment in perpetuity of all present and future rights to
+this software under copyright law.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN
+ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
+WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+For more information, please refer to <https://unlicense.org/>
diff --git a/README.md b/README.md
index df4dc9c..9cefcd8 100644
--- a/README.md
+++ b/README.md
@@ -39,7 +39,9 @@ PROTEA provides a unified backend for ingesting protein data from UniProt, compu
 
 ## Getting started
 
-### Docker (recommended)
+### Docker
+
+> **Not yet validated.** The Docker configuration exists but has not been tested end-to-end. It will likely need adjustments before it works out of the box — contributions welcome.
 
 ```bash
 git clone https://github.com/frapercan/PROTEA.git
@@ -52,7 +54,7 @@ Services available at:
 - API: http://localhost:8000
 - RabbitMQ management: http://localhost:15672 (guest/guest)
 
-### From source
+### From source (recommended)
 
 **Requirements:** Python 3.12, PostgreSQL 16 + pgvector, RabbitMQ 3.x
 
@@ -118,4 +120,4 @@ PROTEA is the natural evolution of two prior systems developed at **Ana Rojas' L
 
 PROTEA was designed to unify and supersede both systems under a single, maintainable codebase — removing the tight coupling between infrastructure, orchestration, and domain logic that accumulated across those projects.
 
-The evaluation pipeline and scoring methodology are directly informed by our participation in **CAFA6** (Critical Assessment of protein Function Annotation, 6th edition). The competition provided real-world benchmarking experience that shaped PROTEA's prediction and evaluation architecture, including the integration of [cafaeval](https://github.com/claradepaolis/CAFA-evaluator-PK) for standardised GO term prediction assessment.
+The evaluation pipeline and scoring methodology are directly informed by following the **CAFA** (Critical Assessment of protein Function Annotation) competition series. This benchmarking framework shaped PROTEA's prediction and evaluation architecture, including the integration of [cafaeval](https://github.com/claradepaolis/CAFA-evaluator-PK) for standardised GO term prediction assessment.
diff --git a/alembic/versions/489835ed5b31_add_composite_index_pga_set_accession.py b/alembic/versions/489835ed5b31_add_composite_index_pga_set_accession.py
new file mode 100644
index 0000000..b99dc62
--- /dev/null
+++ b/alembic/versions/489835ed5b31_add_composite_index_pga_set_accession.py
@@ -0,0 +1,32 @@
+"""add_composite_index_pga_set_accession
+
+Revision ID: 489835ed5b31
+Revises: 7737a352d4fe
+Create Date: 2026-03-15 11:17:30.865922
+
+"""
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision: str = '489835ed5b31'
+down_revision: Union[str, Sequence[str], None] = '7737a352d4fe'
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    """Upgrade schema."""
+    op.create_index(
+        "ix_pga_set_accession",
+        "protein_go_annotation",
+        ["annotation_set_id", "protein_accession"],
+    )
+
+
+def downgrade() -> None:
+    """Downgrade schema."""
+    op.drop_index("ix_pga_set_accession", table_name="protein_go_annotation")
diff --git a/alembic/versions/513355a1d933_add_scoring_config_id_to_evaluation_.py b/alembic/versions/513355a1d933_add_scoring_config_id_to_evaluation_.py
new file mode 100644
index 0000000..1890a22
--- /dev/null
+++ b/alembic/versions/513355a1d933_add_scoring_config_id_to_evaluation_.py
@@ -0,0 +1,38 @@
+"""add scoring_config_id to evaluation_result
+
+Revision ID: 513355a1d933
+Revises: 489835ed5b31
+Create Date: 2026-03-15 12:37:19.930750
+
+"""
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision: str = '513355a1d933'
+down_revision: Union[str, Sequence[str], None] = '489835ed5b31'
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    """Upgrade schema."""
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.add_column('evaluation_result', sa.Column('scoring_config_id', sa.UUID(), nullable=True))
+    op.create_index(op.f('ix_evaluation_result_scoring_config_id'), 'evaluation_result', ['scoring_config_id'], unique=False)
+    op.create_foreign_key(None, 'evaluation_result', 'scoring_config', ['scoring_config_id'], ['id'], ondelete='SET NULL')
+    op.drop_index(op.f('ix_pga_set_accession'), table_name='protein_go_annotation')
+    # ### end Alembic commands ###
+
+
+def downgrade() -> None:
+    """Downgrade schema."""
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.create_index(op.f('ix_pga_set_accession'), 'protein_go_annotation', ['annotation_set_id', 'protein_accession'], unique=False)
+    op.drop_constraint(None, 'evaluation_result', type_='foreignkey')
+    op.drop_index(op.f('ix_evaluation_result_scoring_config_id'), table_name='evaluation_result')
+    op.drop_column('evaluation_result', 'scoring_config_id')
+    # ### end Alembic commands ###
diff --git a/alembic/versions/54e758c210c8_add_ia_url_to_ontology_snapshot.py b/alembic/versions/54e758c210c8_add_ia_url_to_ontology_snapshot.py
new file mode 100644
index 0000000..cde8fca
--- /dev/null
+++ b/alembic/versions/54e758c210c8_add_ia_url_to_ontology_snapshot.py
@@ -0,0 +1,41 @@
+"""add_ia_url_to_ontology_snapshot
+
+Revision ID: 54e758c210c8
+Revises: c1d2e3f4a5b6
+Create Date: 2026-03-16 11:42:10.636169
+
+"""
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+from sqlalchemy.dialects import postgresql
+
+# revision identifiers, used by Alembic.
+revision: str = '54e758c210c8'
+down_revision: Union[str, Sequence[str], None] = 'c1d2e3f4a5b6'
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    """Upgrade schema."""
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.add_column('ontology_snapshot', sa.Column('ia_url', sa.String(), nullable=True, comment='URL of the Information Accretion TSV for this ontology release (two columns: go_id, ia_value). Used by run_cafa_evaluation to weight GO terms by information content. NULL means uniform IC=1.'))
+    op.alter_column('scoring_config', 'evidence_weights',
+               existing_type=postgresql.JSONB(astext_type=sa.Text()),
+               comment=None,
+               existing_comment='Optional per-GO-evidence-code quality multipliers in [0, 1]. NULL means use the system defaults defined in DEFAULT_EVIDENCE_WEIGHTS. Partial dicts are allowed; absent codes fall back to the system table.',
+               existing_nullable=True)
+    # ### end Alembic commands ###
+
+
+def downgrade() -> None:
+    """Downgrade schema."""
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.alter_column('scoring_config', 'evidence_weights',
+               existing_type=postgresql.JSONB(astext_type=sa.Text()),
+               comment='Optional per-GO-evidence-code quality multipliers in [0, 1]. NULL means use the system defaults defined in DEFAULT_EVIDENCE_WEIGHTS. Partial dicts are allowed; absent codes fall back to the system table.',
+               existing_nullable=True)
+    op.drop_column('ontology_snapshot', 'ia_url')
+    # ### end Alembic commands ###
diff --git a/alembic/versions/7737a352d4fe_merge_scoring_config_branch.py b/alembic/versions/7737a352d4fe_merge_scoring_config_branch.py
new file mode 100644
index 0000000..f759c30
--- /dev/null
+++ b/alembic/versions/7737a352d4fe_merge_scoring_config_branch.py
@@ -0,0 +1,28 @@
+"""merge_scoring_config_branch
+
+Revision ID: 7737a352d4fe
+Revises: 47de89cf6fec, b1c2d3e4f5a6
+Create Date: 2026-03-15 10:11:56.507967
+
+"""
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision: str = '7737a352d4fe'
+down_revision: Union[str, Sequence[str], None] = ('47de89cf6fec', 'b1c2d3e4f5a6')
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    """Upgrade schema."""
+    pass
+
+
+def downgrade() -> None:
+    """Downgrade schema."""
+    pass
diff --git a/alembic/versions/7c19ca08d5d4_add_support_entry_table.py b/alembic/versions/7c19ca08d5d4_add_support_entry_table.py
new file mode 100644
index 0000000..599214c
--- /dev/null
+++ b/alembic/versions/7c19ca08d5d4_add_support_entry_table.py
@@ -0,0 +1,37 @@
+"""add support_entry table
+
+Revision ID: 7c19ca08d5d4
+Revises: 513355a1d933
+Create Date: 2026-03-15 12:42:43.832417
+
+"""
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision: str = '7c19ca08d5d4'
+down_revision: Union[str, Sequence[str], None] = '513355a1d933'
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    """Upgrade schema."""
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.create_table('support_entry',
+    sa.Column('id', sa.UUID(), nullable=False),
+    sa.Column('comment', sa.Text(), nullable=True),
+    sa.Column('created_at', sa.DateTime(timezone=True), server_default=sa.text('now()'), nullable=False),
+    sa.PrimaryKeyConstraint('id')
+    )
+    # ### end Alembic commands ###
+
+
+def downgrade() -> None:
+    """Downgrade schema."""
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.drop_table('support_entry')
+    # ### end Alembic commands ###
diff --git a/alembic/versions/b1c2d3e4f5a6_add_scoring_config.py b/alembic/versions/b1c2d3e4f5a6_add_scoring_config.py
new file mode 100644
index 0000000..5eae559
--- /dev/null
+++ b/alembic/versions/b1c2d3e4f5a6_add_scoring_config.py
@@ -0,0 +1,38 @@
+"""add scoring_config table
+
+Revision ID: b1c2d3e4f5a6
+Revises: a7b8c9d0e1f2
+Create Date: 2026-03-15
+"""
+from __future__ import annotations
+
+from alembic import op
+import sqlalchemy as sa
+from sqlalchemy.dialects import postgresql
+
+revision = "b1c2d3e4f5a6"
+down_revision = "a7b8c9d0e1f2"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    op.create_table(
+        "scoring_config",
+        sa.Column("id", postgresql.UUID(as_uuid=True), nullable=False),
+        sa.Column("name", sa.String(255), nullable=False),
+        sa.Column("formula", sa.String(50), nullable=False, server_default="linear"),
+        sa.Column("weights", postgresql.JSONB(astext_type=sa.Text()), nullable=False),
+        sa.Column("description", sa.Text(), nullable=True),
+        sa.Column(
+            "created_at",
+            sa.DateTime(timezone=True),
+            server_default=sa.text("now()"),
+            nullable=False,
+        ),
+        sa.PrimaryKeyConstraint("id"),
+    )
+
+
+def downgrade() -> None:
+    op.drop_table("scoring_config")
diff --git a/alembic/versions/c1d2e3f4a5b6_add_evidence_weights_to_scoring_config.py b/alembic/versions/c1d2e3f4a5b6_add_evidence_weights_to_scoring_config.py
new file mode 100644
index 0000000..4a3a4c9
--- /dev/null
+++ b/alembic/versions/c1d2e3f4a5b6_add_evidence_weights_to_scoring_config.py
@@ -0,0 +1,49 @@
+"""Add evidence_weights column to scoring_config.
+
+Revision ID: c1d2e3f4a5b6
+Revises: 7c19ca08d5d4
+Create Date: 2026-03-16
+
+Motivation
+----------
+``ScoringConfig`` previously hard-coded the per-evidence-code quality
+weights inside the Python scoring engine, making them invisible to users
+and impossible to customise without a code change.
+
+This migration adds an optional ``evidence_weights`` JSONB column that
+stores per-code overrides at the config level.  Existing rows receive
+``NULL``, which is interpreted by the engine as "use system defaults"
+(:data:`protea.infrastructure.orm.models.embedding.scoring_config.DEFAULT_EVIDENCE_WEIGHTS`).
+The change is therefore fully backwards-compatible with all existing
+``ScoringConfig`` rows.
+"""
+from __future__ import annotations
+
+import sqlalchemy as sa
+from alembic import op
+from sqlalchemy.dialects import postgresql
+
+revision = "c1d2e3f4a5b6"
+down_revision = "7c19ca08d5d4"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    op.add_column(
+        "scoring_config",
+        sa.Column(
+            "evidence_weights",
+            postgresql.JSONB(astext_type=sa.Text()),
+            nullable=True,
+            comment=(
+                "Optional per-GO-evidence-code quality multipliers in [0, 1]. "
+                "NULL means use the system defaults defined in DEFAULT_EVIDENCE_WEIGHTS. "
+                "Partial dicts are allowed; absent codes fall back to the system table."
+            ),
+        ),
+    )
+
+
+def downgrade() -> None:
+    op.drop_column("scoring_config", "evidence_weights")
diff --git a/apps/web/app/annotations/page.tsx b/apps/web/app/[locale]/annotations/page.tsx
similarity index 64%
rename from apps/web/app/annotations/page.tsx
rename to apps/web/app/[locale]/annotations/page.tsx
index d352126..73be48b 100644
--- a/apps/web/app/annotations/page.tsx
+++ b/apps/web/app/[locale]/annotations/page.tsx
@@ -5,6 +5,7 @@ import Link from "next/link";
 import {
   listAnnotationSets,
   listOntologySnapshots,
+  setSnapshotIaUrl,
   deleteAnnotationSet,
   createJob,
   AnnotationSet,
@@ -12,6 +13,7 @@ import {
 } from "@/lib/api";
 import { useToast } from "@/components/Toast";
 import { SkeletonTableRow } from "@/components/Skeleton";
+import { useTranslations } from "next-intl";
 
 type Tab = "sets" | "snapshots" | "load-snapshot" | "load-goa" | "load-quickgo";
 
@@ -28,6 +30,7 @@ function shortId(id: string) {
 }
 
 export default function AnnotationsPage() {
+  const t = useTranslations("annotations");
   const toast = useToast();
   const [activeTab, setActiveTab] = useState<Tab>("sets");
 
@@ -36,6 +39,27 @@ export default function AnnotationsPage() {
   const [loadingSets, setLoadingSets] = useState(true);
   const [loadingSnaps, setLoadingSnaps] = useState(true);
 
+  // IA URL inline edit state: snapshotId → current input value (undefined = not editing)
+  const [iaEditId, setIaEditId] = useState<string | null>(null);
+  const [iaEditValue, setIaEditValue] = useState("");
+  const [iaSaving, setIaSaving] = useState(false);
+
+  async function handleSaveIa(snapshotId: string) {
+    setIaSaving(true);
+    try {
+      const result = await setSnapshotIaUrl(snapshotId, iaEditValue.trim() || null);
+      setSnapshots((prev) =>
+        prev.map((s) => (s.id === snapshotId ? { ...s, ia_url: result.ia_url } : s))
+      );
+      setIaEditId(null);
+      toast("IA URL saved", "success");
+    } catch (err: any) {
+      toast(String(err), "error");
+    } finally {
+      setIaSaving(false);
+    }
+  }
+
   // Load Snapshot form
   const [oboUrl, setOboUrl] = useState("http://purl.obolibrary.org/obo/go/go-basic.obo");
   const [snapResult, setSnapResult] = useState<{ id: string } | null>(null);
@@ -90,8 +114,8 @@ export default function AnnotationsPage() {
     const s = sets.find((a) => a.id === id);
     const count = s?.annotation_count ?? 0;
     const msg = count > 0
-      ? `Delete this annotation set and its ${count.toLocaleString()} GO annotations? This cannot be undone.`
-      : "Delete this annotation set?";
+      ? t("setsTab.deleteConfirm", { count: count.toLocaleString() })
+      : t("setsTab.deleteConfirmNoAnnotations");
     if (!confirm(msg)) return;
     try {
       const r = await deleteAnnotationSet(id);
@@ -164,31 +188,31 @@ export default function AnnotationsPage() {
   }
 
   const tabs: { key: Tab; label: string }[] = [
-    { key: "sets", label: "Annotation Sets" },
-    { key: "snapshots", label: "Ontology Snapshots" },
-    { key: "load-snapshot", label: "Load Snapshot" },
-    { key: "load-goa", label: "Load GOA" },
-    { key: "load-quickgo", label: "Load QuickGO" },
+    { key: "sets", label: t("tabs.sets") },
+    { key: "snapshots", label: t("tabs.snapshots") },
+    { key: "load-snapshot", label: t("tabs.loadSnapshot") },
+    { key: "load-goa", label: t("tabs.loadGoa") },
+    { key: "load-quickgo", label: t("tabs.loadQuickgo") },
   ];
 
   return (
     <>
       <div className="flex items-center justify-between mb-4">
-        <h1 className="text-xl font-semibold">Annotations</h1>
+        <h1 className="text-xl font-semibold">{t("title")}</h1>
       </div>
 
-      <div className="flex gap-1 border-b mb-6">
-        {tabs.map((t) => (
+      <div className="flex gap-1 border-b mb-6 overflow-x-auto">
+        {tabs.map((tab) => (
           <button
-            key={t.key}
-            onClick={() => setActiveTab(t.key)}
+            key={tab.key}
+            onClick={() => setActiveTab(tab.key)}
             className={`px-4 py-2 text-sm font-medium border-b-2 transition-colors ${
-              activeTab === t.key
+              activeTab === tab.key
                 ? "border-blue-600 text-blue-600"
                 : "border-transparent text-gray-500 hover:text-gray-700"
             }`}
           >
-            {t.label}
+            {tab.label}
           </button>
         ))}
       </div>
@@ -197,19 +221,19 @@ export default function AnnotationsPage() {
       {activeTab === "sets" && (
         <div>
           <div className="flex items-center justify-between mb-3">
-            <p className="text-sm text-gray-500">{sets.length} annotation set{sets.length !== 1 ? "s" : ""}</p>
+            <p className="text-sm text-gray-500">{t("setsTab.annotationSets", { count: sets.length })}</p>
             <button onClick={loadSets} className="rounded-md border bg-white px-3 py-1.5 text-sm hover:bg-gray-50">
-              Refresh
+              {t("setsTab.refresh")}
             </button>
           </div>
-          <div className="overflow-hidden rounded-lg border bg-white shadow-sm">
+          <div className="overflow-x-auto rounded-lg border bg-white shadow-sm">
             <div className="grid grid-cols-[80px_100px_140px_100px_1fr_160px_60px] gap-2 border-b bg-gray-50 px-4 py-2.5 text-xs font-semibold uppercase tracking-wide text-gray-500">
-              <div>ID</div><div>Source</div><div>Version</div><div>Annotations</div><div>Meta</div><div>Created</div><div></div>
+              <div>{t("setsTab.tableHeaders.id")}</div><div>{t("setsTab.tableHeaders.source")}</div><div>{t("setsTab.tableHeaders.version")}</div><div>{t("setsTab.tableHeaders.annotations")}</div><div>{t("setsTab.tableHeaders.meta")}</div><div>{t("setsTab.tableHeaders.created")}</div><div></div>
             </div>
             {loadingSets && Array.from({ length: 3 }).map((_, i) => <SkeletonTableRow key={i} cols={7} />)}
             {!loadingSets && sets.length === 0 && (
               <div className="px-4 py-8 text-center text-sm text-gray-400">
-                No annotation sets yet. Load GO annotations from the Load GOA or Load QuickGO tabs.
+                {t("setsTab.noSetsFound")}
               </div>
             )}
             {sets.map((a) => (
@@ -236,7 +260,7 @@ export default function AnnotationsPage() {
                     onClick={() => handleDeleteSet(a.id)}
                     className="rounded border border-red-200 px-2 py-1 text-xs text-red-600 hover:bg-red-50 transition-colors"
                   >
-                    Delete
+                    {t("setsTab.delete")}
                   </button>
                 </div>
               </div>
@@ -249,26 +273,70 @@ export default function AnnotationsPage() {
       {activeTab === "snapshots" && (
         <div>
           <div className="flex items-center justify-between mb-3">
-            <p className="text-sm text-gray-500">{snapshots.length} snapshot{snapshots.length !== 1 ? "s" : ""}</p>
+            <p className="text-sm text-gray-500">{t("snapshotsTab.snapshots", { count: snapshots.length })}</p>
             <button onClick={loadSnapshots} className="rounded-md border bg-white px-3 py-1.5 text-sm hover:bg-gray-50">
-              Refresh
+              {t("snapshotsTab.refresh")}
             </button>
           </div>
-          <div className="overflow-hidden rounded-lg border bg-white shadow-sm">
-            <div className="grid grid-cols-[80px_1fr_120px_200px] gap-2 border-b bg-gray-50 px-4 py-2.5 text-xs font-semibold uppercase tracking-wide text-gray-500">
-              <div>ID</div><div>Version</div><div>GO Terms</div><div>Loaded</div>
+          <div className="overflow-x-auto rounded-lg border bg-white shadow-sm">
+            <div className="grid grid-cols-[80px_160px_100px_minmax(160px,1fr)_160px] min-w-[700px] gap-2 border-b bg-gray-50 px-4 py-2.5 text-xs font-semibold uppercase tracking-wide text-gray-500">
+              <div>{t("snapshotsTab.tableHeaders.id")}</div><div>{t("snapshotsTab.tableHeaders.version")}</div><div>{t("snapshotsTab.tableHeaders.goTerms")}</div><div>{t("snapshotsTab.tableHeaders.iaUrl")}</div><div>{t("snapshotsTab.tableHeaders.loaded")}</div>
             </div>
-            {loadingSnaps && Array.from({ length: 2 }).map((_, i) => <SkeletonTableRow key={i} cols={4} />)}
+            {loadingSnaps && Array.from({ length: 2 }).map((_, i) => <SkeletonTableRow key={i} cols={5} />)}
             {!loadingSnaps && snapshots.length === 0 && (
               <div className="px-4 py-8 text-center text-sm text-gray-400">
-                No ontology snapshots yet. Use the Load Snapshot tab.
+                {t("snapshotsTab.noSnapshotsFound")}
               </div>
             )}
             {snapshots.map((s) => (
-              <div key={s.id} className="grid grid-cols-[80px_1fr_120px_200px] gap-2 border-b px-4 py-3 text-sm last:border-0 items-center">
+              <div key={s.id} className="grid grid-cols-[80px_160px_100px_minmax(160px,1fr)_160px] min-w-[700px] gap-2 border-b px-4 py-3 text-sm last:border-0 items-center">
                 <div className="font-mono text-xs text-gray-400" title={s.id}>{shortId(s.id)}</div>
                 <div className="font-medium text-gray-800">{s.obo_version}</div>
                 <div className="text-gray-700">{(s.go_term_count ?? 0).toLocaleString()}</div>
+                <div className="min-w-0">
+                  {iaEditId === s.id ? (
+                    <div className="flex items-center gap-1">
+                      <input
+                        autoFocus
+                        type="text"
+                        value={iaEditValue}
+                        onChange={(e) => setIaEditValue(e.target.value)}
+                        placeholder="https://…/IA_cafa6.tsv or file path"
+                        className="flex-1 min-w-0 rounded border px-2 py-1 text-xs focus:outline-none focus:ring-1 focus:ring-blue-500"
+                        onKeyDown={(e) => {
+                          if (e.key === "Enter") handleSaveIa(s.id);
+                          if (e.key === "Escape") setIaEditId(null);
+                        }}
+                      />
+                      <button
+                        onClick={() => handleSaveIa(s.id)}
+                        disabled={iaSaving}
+                        className="rounded bg-blue-600 px-2 py-1 text-xs text-white hover:bg-blue-700 disabled:opacity-50"
+                      >
+                        {t("snapshotsTab.save")}
+                      </button>
+                      <button
+                        onClick={() => setIaEditId(null)}
+                        className="rounded border px-2 py-1 text-xs text-gray-500 hover:bg-gray-50"
+                      >
+                        {t("snapshotsTab.cancel")}
+                      </button>
+                    </div>
+                  ) : (
+                    <button
+                      onClick={() => { setIaEditId(s.id); setIaEditValue(s.ia_url ?? ""); }}
+                      className="w-full text-left flex items-center gap-2 rounded px-1 py-0.5 hover:bg-gray-50 active:bg-gray-100 transition-colors"
+                      title={t("snapshotsTab.editTooltip")}
+                    >
+                      {s.ia_url ? (
+                        <span className="truncate text-xs text-gray-500 font-mono flex-1">{s.ia_url}</span>
+                      ) : (
+                        <span className="text-xs text-amber-500 italic flex-1">{t("snapshotsTab.notSet")}</span>
+                      )}
+                      <span className="shrink-0 text-gray-400 text-xs">✎</span>
+                    </button>
+                  )}
+                </div>
                 <div className="text-xs text-gray-400">{formatDate(s.loaded_at)}</div>
               </div>
             ))}
@@ -278,13 +346,13 @@ export default function AnnotationsPage() {
 
       {/* ── Load Ontology Snapshot ── */}
       {activeTab === "load-snapshot" && (
-        <div className="max-w-lg">
+        <div className="max-w-2xl">
           <div className="rounded-lg border bg-white p-6 shadow-sm">
-            <h2 className="text-base font-semibold mb-1">Load Ontology Snapshot</h2>
-            <p className="text-sm text-gray-500 mb-4">Downloads a GO OBO file and populates GOTerm rows.</p>
+            <h2 className="text-base font-semibold mb-1">{t("loadSnapshotTab.title")}</h2>
+            <p className="text-sm text-gray-500 mb-4">{t("loadSnapshotTab.description")}</p>
             <form onSubmit={handleLoadSnapshot} className="space-y-4">
               <div>
-                <label className={labelClass}>OBO URL</label>
+                <label className={labelClass}>{t("loadSnapshotTab.oboUrlLabel")}</label>
                 <input
                   type="text"
                   value={oboUrl}
@@ -303,7 +371,7 @@ export default function AnnotationsPage() {
               )}
               <div className="flex justify-end">
                 <button type="submit" disabled={snapSubmitting} className="rounded-md bg-blue-600 px-4 py-2 text-sm text-white hover:bg-blue-700 disabled:opacity-50">
-                  {snapSubmitting ? "Launching…" : "Launch Job"}
+                  {snapSubmitting ? t("loadSnapshotTab.launching") : t("loadSnapshotTab.launchJob")}
                 </button>
               </div>
             </form>
@@ -313,42 +381,42 @@ export default function AnnotationsPage() {
 
       {/* ── Load GOA Annotations ── */}
       {activeTab === "load-goa" && (
-        <div className="max-w-lg">
+        <div className="max-w-2xl">
           <div className="rounded-lg border bg-white p-6 shadow-sm">
-            <h2 className="text-base font-semibold mb-1">Load GOA Annotations</h2>
-            <p className="text-sm text-gray-500 mb-4">Bulk-loads GO annotations from a GAF file.</p>
+            <h2 className="text-base font-semibold mb-1">{t("loadGoaTab.title")}</h2>
+            <p className="text-sm text-gray-500 mb-4">{t("loadGoaTab.description")}</p>
             <form onSubmit={handleLoadGoa} className="space-y-4">
               <div>
-                <label className={labelClass}>Ontology Snapshot</label>
+                <label className={labelClass}>{t("loadGoaTab.snapshotLabel")}</label>
                 <select value={goaSnapshotId} onChange={(e) => setGoaSnapshotId(e.target.value)} required className={inputClass}>
-                  <option value="">— select snapshot —</option>
+                  <option value="">{t("loadGoaTab.selectSnapshot")}</option>
                   {snapshots.map((s) => (
                     <option key={s.id} value={s.id}>{s.obo_version} · {shortId(s.id)}…</option>
                   ))}
                 </select>
                 {snapshots.length === 0 && (
-                  <p className="mt-1 text-xs text-amber-600">No snapshots — run Load Snapshot first.</p>
+                  <p className="mt-1 text-xs text-amber-600">{t("loadGoaTab.noSnapshots")}</p>
                 )}
               </div>
               <div>
-                <label className={labelClass}>GAF URL</label>
+                <label className={labelClass}>{t("loadGoaTab.gafUrlLabel")}</label>
                 <input
                   type="text"
                   value={goaUrl}
                   onChange={(e) => setGoaUrl(e.target.value)}
                   required
-                  placeholder="https://current.geneontology.org/annotations/goa_human.gaf.gz"
+                  placeholder={t("loadGoaTab.gafUrlPlaceholder")}
                   className={inputClass}
                 />
               </div>
               <div>
-                <label className={labelClass}>Source version</label>
+                <label className={labelClass}>{t("loadGoaTab.sourceVersionLabel")}</label>
                 <input
                   type="text"
                   value={goaVersion}
                   onChange={(e) => setGoaVersion(e.target.value)}
                   required
-                  placeholder="2025-03"
+                  placeholder={t("loadGoaTab.sourceVersionPlaceholder")}
                   className={inputClass}
                 />
               </div>
@@ -362,7 +430,7 @@ export default function AnnotationsPage() {
               )}
               <div className="flex justify-end">
                 <button type="submit" disabled={goaSubmitting} className="rounded-md bg-blue-600 px-4 py-2 text-sm text-white hover:bg-blue-700 disabled:opacity-50">
-                  {goaSubmitting ? "Launching…" : "Launch Job"}
+                  {goaSubmitting ? t("loadGoaTab.launching") : t("loadGoaTab.launchJob")}
                 </button>
               </div>
             </form>
@@ -372,31 +440,31 @@ export default function AnnotationsPage() {
 
       {/* ── Load QuickGO Annotations ── */}
       {activeTab === "load-quickgo" && (
-        <div className="max-w-lg">
+        <div className="max-w-2xl">
           <div className="rounded-lg border bg-white p-6 shadow-sm">
-            <h2 className="text-base font-semibold mb-1">Load QuickGO Annotations</h2>
-            <p className="text-sm text-gray-500 mb-4">Streams GO annotations from the QuickGO bulk download API.</p>
+            <h2 className="text-base font-semibold mb-1">{t("loadQuickgoTab.title")}</h2>
+            <p className="text-sm text-gray-500 mb-4">{t("loadQuickgoTab.description")}</p>
             <form onSubmit={handleLoadQuickgo} className="space-y-4">
               <div>
-                <label className={labelClass}>Ontology Snapshot</label>
+                <label className={labelClass}>{t("loadQuickgoTab.snapshotLabel")}</label>
                 <select value={qgoSnapshotId} onChange={(e) => setQgoSnapshotId(e.target.value)} required className={inputClass}>
-                  <option value="">— select snapshot —</option>
+                  <option value="">{t("loadQuickgoTab.selectSnapshot")}</option>
                   {snapshots.map((s) => (
                     <option key={s.id} value={s.id}>{s.obo_version} · {shortId(s.id)}…</option>
                   ))}
                 </select>
                 {snapshots.length === 0 && (
-                  <p className="mt-1 text-xs text-amber-600">No snapshots — run Load Snapshot first.</p>
+                  <p className="mt-1 text-xs text-amber-600">{t("loadQuickgoTab.noSnapshots")}</p>
                 )}
               </div>
               <div>
-                <label className={labelClass}>Source version</label>
+                <label className={labelClass}>{t("loadQuickgoTab.sourceVersionLabel")}</label>
                 <input
                   type="text"
                   value={qgoVersion}
                   onChange={(e) => setQgoVersion(e.target.value)}
                   required
-                  placeholder="2025-03"
+                  placeholder={t("loadQuickgoTab.sourceVersionPlaceholder")}
                   className={inputClass}
                 />
               </div>
@@ -410,7 +478,7 @@ export default function AnnotationsPage() {
               )}
               <div className="flex justify-end">
                 <button type="submit" disabled={qgoSubmitting} className="rounded-md bg-blue-600 px-4 py-2 text-sm text-white hover:bg-blue-700 disabled:opacity-50">
-                  {qgoSubmitting ? "Launching…" : "Launch Job"}
+                  {qgoSubmitting ? t("loadQuickgoTab.launching") : t("loadQuickgoTab.launchJob")}
                 </button>
               </div>
             </form>
diff --git a/apps/web/app/embeddings/page.tsx b/apps/web/app/[locale]/embeddings/page.tsx
similarity index 80%
rename from apps/web/app/embeddings/page.tsx
rename to apps/web/app/[locale]/embeddings/page.tsx
index 718c4fb..7110cb8 100644
--- a/apps/web/app/embeddings/page.tsx
+++ b/apps/web/app/[locale]/embeddings/page.tsx
@@ -2,6 +2,18 @@
 
 import { useEffect, useState } from "react";
 import Link from "next/link";
+import { useTranslations } from "next-intl";
+import { useToast } from "@/components/Toast";
+import { SkeletonTableRow } from "@/components/Skeleton";
+import {
+  listEmbeddingConfigs,
+  createEmbeddingConfig,
+  deleteEmbeddingConfig,
+  createJob,
+  listQuerySets,
+  EmbeddingConfig,
+  QuerySet,
+} from "@/lib/api";
 
 type ModelPreset = {
   value: string;
@@ -31,17 +43,6 @@ const MODEL_PRESETS: Record<string, ModelPreset[]> = {
     { value: "facebook/esm2_t33_650M_UR50D", label: "ESM-2 650M (auto backend)", layers: 33, defaultMaxLength: 1022 },
   ],
 };
-import { useToast } from "@/components/Toast";
-import { SkeletonTableRow } from "@/components/Skeleton";
-import {
-  listEmbeddingConfigs,
-  createEmbeddingConfig,
-  deleteEmbeddingConfig,
-  createJob,
-  listQuerySets,
-  EmbeddingConfig,
-  QuerySet,
-} from "@/lib/api";
 
 type Tab = "configs" | "compute";
 
@@ -55,6 +56,7 @@ function shortId(id: string) {
 }
 
 export default function EmbeddingsPage() {
+  const t = useTranslations("embeddings");
   const [activeTab, setActiveTab] = useState<Tab>("configs");
   const toast = useToast();
 
@@ -67,7 +69,7 @@ export default function EmbeddingsPage() {
   // New config form state
   const [showConfigForm, setShowConfigForm] = useState(false);
   const [cfgBackend, setCfgBackend] = useState("esm");
-  const [cfgModelPreset, setCfgModelPreset] = useState(MODEL_PRESETS.esm[3].value); // 650M default
+  const [cfgModelPreset, setCfgModelPreset] = useState(MODEL_PRESETS.esm[3].value);
   const [cfgModelCustom, setCfgModelCustom] = useState("");
   const cfgModelName = cfgModelPreset === "__custom__" ? cfgModelCustom : cfgModelPreset;
   const [cfgLayerIndices, setCfgLayerIndices] = useState("0");
@@ -94,7 +96,6 @@ export default function EmbeddingsPage() {
   const [cmpError, setCmpError] = useState("");
   const [cmpSubmitting, setCmpSubmitting] = useState(false);
 
-
   async function loadAll() {
     setLoading(true);
     setError("");
@@ -162,8 +163,8 @@ export default function EmbeddingsPage() {
     const cfg = configs.find((c) => c.id === id);
     const count = cfg?.embedding_count ?? 0;
     const msg = count > 0
-      ? `Delete this embedding config and its ${count.toLocaleString()} stored embeddings? This cannot be undone.`
-      : "Delete this embedding config?";
+      ? t("configsTab.deleteConfirm", { count: count.toLocaleString() })
+      : t("configsTab.deleteConfirmNoEmbeddings");
     if (!confirm(msg)) return;
     try {
       await deleteEmbeddingConfig(id);
@@ -209,8 +210,8 @@ export default function EmbeddingsPage() {
   }
 
   const tabs: { key: Tab; label: string }[] = [
-    { key: "configs", label: "Configs" },
-    { key: "compute", label: "Compute" },
+    { key: "configs", label: t("tabs.configs") },
+    { key: "compute", label: t("tabs.compute") },
   ];
 
   const inputClass =
@@ -220,7 +221,7 @@ export default function EmbeddingsPage() {
   return (
     <>
       <div className="flex flex-wrap items-center gap-3 mb-4">
-        <h1 className="text-xl font-semibold">Embeddings</h1>
+        <h1 className="text-xl font-semibold">{t("title")}</h1>
       </div>
 
       {error && (
@@ -230,18 +231,18 @@ export default function EmbeddingsPage() {
       )}
 
       {/* Tab bar */}
-      <div className="flex gap-1 border-b mb-6">
-        {tabs.map((t) => (
+      <div className="flex gap-1 border-b mb-6 overflow-x-auto">
+        {tabs.map((tab) => (
           <button
-            key={t.key}
-            onClick={() => setActiveTab(t.key)}
+            key={tab.key}
+            onClick={() => setActiveTab(tab.key)}
             className={`px-4 py-2 text-sm font-medium border-b-2 transition-colors ${
-              activeTab === t.key
+              activeTab === tab.key
                 ? "border-blue-600 text-blue-600"
                 : "border-transparent text-gray-500 hover:text-gray-700"
             }`}
           >
-            {t.label}
+            {tab.label}
           </button>
         ))}
       </div>
@@ -250,30 +251,27 @@ export default function EmbeddingsPage() {
       {activeTab === "configs" && (
         <div>
           <div className="flex items-center justify-between mb-3">
-            <p className="text-sm text-gray-500">{configs.length} config{configs.length !== 1 ? "s" : ""}</p>
+            <p className="text-sm text-gray-500">{t("configsTab.configs", { count: configs.length })}</p>
             <button
               onClick={() => setShowConfigForm((v) => !v)}
               className="rounded-md bg-blue-600 px-3 py-1.5 text-sm text-white hover:bg-blue-700"
             >
-              {showConfigForm ? "Cancel" : "+ New Config"}
+              {showConfigForm ? t("configsTab.cancel") : t("configsTab.newConfig")}
             </button>
           </div>
 
           {showConfigForm && (
             <div className="mb-6 rounded-lg border bg-white p-5 shadow-sm">
-              <h2 className="text-base font-semibold mb-4">New Embedding Config</h2>
+              <h2 className="text-base font-semibold mb-4">{t("configsTab.newConfigForm.title")}</h2>
               <form onSubmit={handleCreateConfig} className="space-y-4">
                 {/* Layer indexing convention warning */}
                 <div className="rounded-md border border-amber-200 bg-amber-50 px-3 py-2 text-xs text-amber-800">
-                  <strong>Layer indexing — reverse convention:</strong>{" "}
-                  <code>0</code> = last (most semantic) layer,{" "}
-                  <code>1</code> = penultimate, etc.
-                  This matches PIS / FANTASIA. Use <code>0</code> for the standard last-layer embedding.
+                  {t("configsTab.newConfigForm.layerIndexingWarning")}
                 </div>
 
                 <div className="grid grid-cols-1 gap-4 sm:grid-cols-2">
                   <div>
-                    <label className={labelClass}>Model Backend</label>
+                    <label className={labelClass}>{t("configsTab.newConfigForm.modelBackendLabel")}</label>
                     <select
                       value={cfgBackend}
                       onChange={(e) => {
@@ -287,20 +285,18 @@ export default function EmbeddingsPage() {
                         } else {
                           setCfgModelPreset("__custom__");
                         }
-                        // Reset layer indices — each backend has a different
-                        // model depth; 0 is always safe (= last layer).
                         setCfgLayerIndices("0");
                       }}
                       className={inputClass}
                     >
-                      <option value="esm">esm — HuggingFace EsmModel (ESM-2)</option>
-                      <option value="esm3c">esm3c — ESM SDK ESMC (ESM3c) · FP16 on GPU</option>
-                      <option value="t5">t5 — HuggingFace T5EncoderModel (ProstT5…)</option>
-                      <option value="auto">auto — falls back to esm</option>
+                      <option value="esm">{t("configsTab.newConfigForm.modelBackendEsm")}</option>
+                      <option value="esm3c">{t("configsTab.newConfigForm.modelBackendEsm3c")}</option>
+                      <option value="t5">{t("configsTab.newConfigForm.modelBackendT5")}</option>
+                      <option value="auto">{t("configsTab.newConfigForm.modelBackendAuto")}</option>
                     </select>
                   </div>
                   <div>
-                    <label className={labelClass}>Model</label>
+                    <label className={labelClass}>{t("configsTab.newConfigForm.modelLabel")}</label>
                     <select
                       value={cfgModelPreset}
                       onChange={(e) => {
@@ -321,7 +317,7 @@ export default function EmbeddingsPage() {
                         type="text"
                         value={cfgModelCustom}
                         onChange={(e) => setCfgModelCustom(e.target.value)}
-                        placeholder="e.g. facebook/esm2_t33_650M_UR50D"
+                        placeholder={t("configsTab.newConfigForm.customModelPlaceholder")}
                         required
                         className={`${inputClass} mt-1 font-mono text-xs`}
                       />
@@ -334,37 +330,37 @@ export default function EmbeddingsPage() {
                   </div>
                   <div>
                     <label className={labelClass}>
-                      Layer Indices{" "}
-                      <span className="font-normal text-gray-400">(0 = last, 1 = penultimate…)</span>
+                      {t("configsTab.newConfigForm.layerIndicesLabel")}{" "}
+                      <span className="font-normal text-gray-400">{t("configsTab.newConfigForm.layerIndicesHelper")}</span>
                     </label>
                     <input
                       type="text"
                       value={cfgLayerIndices}
                       onChange={(e) => setCfgLayerIndices(e.target.value)}
-                      placeholder="0  or  0,1,2"
+                      placeholder={t("configsTab.newConfigForm.layerIndicesPlaceholder")}
                       required
                       className={inputClass}
                     />
                   </div>
                   <div>
-                    <label className={labelClass}>Layer Aggregation</label>
+                    <label className={labelClass}>{t("configsTab.newConfigForm.layerAggLabel")}</label>
                     <select value={cfgLayerAgg} onChange={(e) => setCfgLayerAgg(e.target.value)} className={inputClass}>
-                      <option value="mean">mean — element-wise average</option>
-                      <option value="last">last — only the last selected layer</option>
-                      <option value="concat">concat — concatenate all (dim × n_layers)</option>
+                      <option value="mean">{t("configsTab.newConfigForm.layerAggMean")}</option>
+                      <option value="last">{t("configsTab.newConfigForm.layerAggLast")}</option>
+                      <option value="concat">{t("configsTab.newConfigForm.layerAggConcat")}</option>
                     </select>
                   </div>
                   <div>
-                    <label className={labelClass}>Sequence Pooling</label>
+                    <label className={labelClass}>{t("configsTab.newConfigForm.poolingLabel")}</label>
                     <select value={cfgPooling} onChange={(e) => setCfgPooling(e.target.value)} className={inputClass}>
-                      <option value="mean">mean — mean over residues</option>
-                      <option value="max">max — max over residues</option>
-                      <option value="mean_max">mean_max — concat(mean, max) · dim × 2</option>
-                      <option value="cls">cls — CLS/BOS token at position 0</option>
+                      <option value="mean">{t("configsTab.newConfigForm.poolingMean")}</option>
+                      <option value="max">{t("configsTab.newConfigForm.poolingMax")}</option>
+                      <option value="mean_max">{t("configsTab.newConfigForm.poolingMeanMax")}</option>
+                      <option value="cls">{t("configsTab.newConfigForm.poolingCls")}</option>
                     </select>
                   </div>
                   <div>
-                    <label className={labelClass}>Max Length (tokens)</label>
+                    <label className={labelClass}>{t("configsTab.newConfigForm.maxLengthLabel")}</label>
                     <input
                       type="number"
                       value={cfgMaxLength}
@@ -374,7 +370,7 @@ export default function EmbeddingsPage() {
                     />
                   </div>
                   <div className="sm:col-span-2">
-                    <label className={labelClass}>Description (optional)</label>
+                    <label className={labelClass}>{t("configsTab.newConfigForm.descriptionLabel")}</label>
                     <input
                       type="text"
                       value={cfgDescription}
@@ -393,7 +389,7 @@ export default function EmbeddingsPage() {
                       className="rounded"
                     />
                     <label htmlFor="cfg-norm-residues" className="text-sm text-gray-700 cursor-pointer">
-                      Normalize residues (L2 per-residue before pooling)
+                      {t("configsTab.newConfigForm.normalizeResidues")}
                     </label>
                   </div>
                   <div className="flex items-center gap-2">
@@ -405,7 +401,7 @@ export default function EmbeddingsPage() {
                       className="rounded"
                     />
                     <label htmlFor="cfg-normalize" className="text-sm text-gray-700 cursor-pointer">
-                      Normalize final embedding (L2 after pooling)
+                      {t("configsTab.newConfigForm.normalizeFinal")}
                     </label>
                   </div>
 
@@ -419,13 +415,13 @@ export default function EmbeddingsPage() {
                       className="rounded"
                     />
                     <label htmlFor="cfg-chunking" className="text-sm text-gray-700 cursor-pointer">
-                      Enable chunking (long sequences → multiple embeddings per sequence)
+                      {t("configsTab.newConfigForm.enableChunking")}
                     </label>
                   </div>
                   {cfgUseChunking && (
                     <>
                       <div>
-                        <label className={labelClass}>Chunk Size (residues)</label>
+                        <label className={labelClass}>{t("configsTab.newConfigForm.chunkSizeLabel")}</label>
                         <input
                           type="number"
                           value={cfgChunkSize}
@@ -435,7 +431,7 @@ export default function EmbeddingsPage() {
                         />
                       </div>
                       <div>
-                        <label className={labelClass}>Chunk Overlap (residues)</label>
+                        <label className={labelClass}>{t("configsTab.newConfigForm.chunkOverlapLabel")}</label>
                         <input
                           type="number"
                           value={cfgChunkOverlap}
@@ -460,14 +456,14 @@ export default function EmbeddingsPage() {
                     onClick={() => setShowConfigForm(false)}
                     className="rounded-md border px-4 py-2 text-sm hover:bg-gray-50"
                   >
-                    Cancel
+                    {t("configsTab.cancel")}
                   </button>
                   <button
                     type="submit"
                     disabled={cfgSubmitting}
                     className="rounded-md bg-blue-600 px-4 py-2 text-sm text-white hover:bg-blue-700 disabled:opacity-50"
                   >
-                    {cfgSubmitting ? "Creating…" : "Create Config"}
+                    {cfgSubmitting ? t("configsTab.newConfigForm.creating") : t("configsTab.newConfigForm.createConfig")}
                   </button>
                 </div>
               </form>
@@ -475,20 +471,20 @@ export default function EmbeddingsPage() {
           )}
 
           {loading ? (
-            <div className="overflow-hidden rounded-lg border bg-white shadow-sm">
+            <div className="overflow-x-auto rounded-lg border bg-white shadow-sm">
               {Array.from({ length: 3 }).map((_, i) => <SkeletonTableRow key={i} cols={9} />)}
             </div>
           ) : (
-            <div className="overflow-hidden rounded-lg border bg-white shadow-sm">
+            <div className="overflow-x-auto rounded-lg border bg-white shadow-sm">
               <div className="grid grid-cols-[1fr_140px_80px_100px_80px_80px_60px_160px_60px] gap-2 border-b bg-gray-50 px-4 py-2.5 text-xs font-semibold uppercase tracking-wide text-gray-500">
-                <div>Description</div>
-                <div>Model</div>
-                <div>Backend</div>
-                <div>Layers</div>
-                <div>Agg</div>
-                <div>Pool</div>
-                <div>Norm</div>
-                <div>Created</div>
+                <div>{t("configsTab.tableHeaders.description")}</div>
+                <div>{t("configsTab.tableHeaders.model")}</div>
+                <div>{t("configsTab.tableHeaders.backend")}</div>
+                <div>{t("configsTab.tableHeaders.layers")}</div>
+                <div>{t("configsTab.tableHeaders.agg")}</div>
+                <div>{t("configsTab.tableHeaders.pool")}</div>
+                <div>{t("configsTab.tableHeaders.norm")}</div>
+                <div>{t("configsTab.tableHeaders.created")}</div>
                 <div></div>
               </div>
               {configs.map((c) => (
@@ -519,9 +515,9 @@ export default function EmbeddingsPage() {
               ))}
               {configs.length === 0 && (
                 <div className="px-4 py-8 text-center text-sm text-gray-400">
-                  No embedding configs yet.{" "}
+                  {t("configsTab.noConfigs")}{" "}
                   <button onClick={() => setShowConfigForm(true)} className="text-blue-600 underline">
-                    Create one
+                    ↑
                   </button>
                 </div>
               )}
@@ -532,15 +528,15 @@ export default function EmbeddingsPage() {
 
       {/* ── Compute Tab ── */}
       {activeTab === "compute" && (
-        <div className="max-w-lg">
+        <div className="max-w-2xl">
           <div className="rounded-lg border bg-white p-6 shadow-sm">
-            <h2 className="text-base font-semibold mb-4">Compute Embeddings</h2>
+            <h2 className="text-base font-semibold mb-4">{t("computeTab.title")}</h2>
             {loading ? (
-              <p className="text-sm text-gray-400">Loading…</p>
+              <p className="text-sm text-gray-400">{t("computeTab.loading")}</p>
             ) : (
               <form onSubmit={handleComputeSubmit} className="space-y-4">
                 <div>
-                  <label className={labelClass}>Embedding Config</label>
+                  <label className={labelClass}>{t("computeTab.configLabel")}</label>
                   <select
                     value={cmpConfigId || configs[0]?.id || ""}
                     onChange={(e) => setCmpConfigId(e.target.value)}
@@ -548,7 +544,7 @@ export default function EmbeddingsPage() {
                     className={inputClass}
                   >
                     {configs.length === 0 && (
-                      <option value="">— no configs available —</option>
+                      <option value="">{t("computeTab.noConfigs")}</option>
                     )}
                     {configs.map((c) => (
                       <option key={c.id} value={c.id}>
@@ -560,14 +556,14 @@ export default function EmbeddingsPage() {
 
                 <div>
                   <label className={labelClass}>
-                    Query Set <span className="font-normal text-gray-400">(optional — leave empty to compute all)</span>
+                    {t("computeTab.querySetLabel")} <span className="font-normal text-gray-400">{t("computeTab.querySetHelper")}</span>
                   </label>
                   <select
                     value={cmpQuerySetId}
                     onChange={(e) => setCmpQuerySetId(e.target.value)}
                     className={inputClass}
                   >
-                    <option value="">— all sequences —</option>
+                    <option value="">{t("computeTab.allSequences")}</option>
                     {querySets.map((qs) => (
                       <option key={qs.id} value={qs.id}>
                         {qs.name} ({qs.entry_count} seqs)
@@ -579,8 +575,8 @@ export default function EmbeddingsPage() {
                 <div className="grid grid-cols-2 gap-3">
                   <div>
                     <label className={labelClass}>
-                      Queue Batch Size{" "}
-                      <span className="font-normal text-gray-400">(seqs/job)</span>
+                      {t("computeTab.queueBatchSizeLabel")}{" "}
+                      <span className="font-normal text-gray-400">{t("computeTab.queueBatchSizeHelper")}</span>
                     </label>
                     <input
                       type="number"
@@ -592,8 +588,8 @@ export default function EmbeddingsPage() {
                   </div>
                   <div>
                     <label className={labelClass}>
-                      Model Batch Size{" "}
-                      <span className="font-normal text-gray-400">(seqs/forward)</span>
+                      {t("computeTab.modelBatchSizeLabel")}{" "}
+                      <span className="font-normal text-gray-400">{t("computeTab.modelBatchSizeHelper")}</span>
                     </label>
                     <input
                       type="number"
@@ -606,7 +602,7 @@ export default function EmbeddingsPage() {
                 </div>
 
                 <div>
-                  <label className={labelClass}>Device</label>
+                  <label className={labelClass}>{t("computeTab.deviceLabel")}</label>
                   <select
                     value={["cpu", "cuda", "cuda:0", "cuda:1"].includes(cmpDevice) ? cmpDevice : "custom"}
                     onChange={(e) => {
@@ -614,11 +610,11 @@ export default function EmbeddingsPage() {
                     }}
                     className={inputClass}
                   >
-                    <option value="cpu">cpu — CPU (FP32)</option>
-                    <option value="cuda">cuda — GPU default (FP16 for ESM3c/T5)</option>
-                    <option value="cuda:0">cuda:0 — GPU 0</option>
-                    <option value="cuda:1">cuda:1 — GPU 1</option>
-                    <option value="custom">custom…</option>
+                    <option value="cpu">{t("computeTab.deviceCpu")}</option>
+                    <option value="cuda">{t("computeTab.deviceCuda")}</option>
+                    <option value="cuda:0">{t("computeTab.deviceCuda0")}</option>
+                    <option value="cuda:1">{t("computeTab.deviceCuda1")}</option>
+                    <option value="custom">{t("computeTab.deviceCustom")}</option>
                   </select>
                   {(cmpDevice === "custom" || !["cpu", "cuda", "cuda:0", "cuda:1"].includes(cmpDevice)) && (
                     <input
@@ -640,7 +636,7 @@ export default function EmbeddingsPage() {
                     className="rounded"
                   />
                   <label htmlFor="cmp-skip-existing" className="text-sm text-gray-700 cursor-pointer">
-                    Skip existing embeddings
+                    {t("computeTab.skipExisting")}
                   </label>
                 </div>
 
@@ -665,7 +661,7 @@ export default function EmbeddingsPage() {
                     disabled={cmpSubmitting || configs.length === 0}
                     className="rounded-md bg-blue-600 px-4 py-2 text-sm text-white hover:bg-blue-700 disabled:opacity-50"
                   >
-                    {cmpSubmitting ? "Launching…" : "Launch Compute Job"}
+                    {cmpSubmitting ? t("computeTab.launching") : t("computeTab.launchComputeJob")}
                   </button>
                 </div>
               </form>
@@ -673,7 +669,6 @@ export default function EmbeddingsPage() {
           </div>
         </div>
       )}
-
     </>
   );
 }
diff --git a/apps/web/app/[locale]/evaluation/page.tsx b/apps/web/app/[locale]/evaluation/page.tsx
new file mode 100644
index 0000000..a061dbd
--- /dev/null
+++ b/apps/web/app/[locale]/evaluation/page.tsx
@@ -0,0 +1,691 @@
+"use client";
+
+import { useEffect, useState } from "react";
+import { useTranslations } from "next-intl";
+import { listAnnotationSets, listPredictionSets, listScoringConfigs, baseUrl } from "@/lib/api";
+import type { AnnotationSet, PredictionSet, ScoringConfig } from "@/lib/api";
+
+const labelClass = "block text-sm font-medium text-gray-700 mb-1";
+const selectClass =
+  "w-full rounded-md border border-gray-300 px-3 py-2 text-sm focus:outline-none focus:ring-2 focus:ring-blue-500";
+const btnPrimary =
+  "rounded-md bg-blue-600 px-4 py-2 text-sm font-medium text-white hover:bg-blue-700 disabled:opacity-50 transition-colors";
+const btnSecondary =
+  "rounded-md border border-gray-300 bg-white px-4 py-2 text-sm font-medium text-gray-700 hover:bg-gray-50 disabled:opacity-50 transition-colors";
+
+function InfoTooltip({ text }: { text: string }) {
+  return (
+    <span className="relative inline-block group ml-1 align-middle">
+      <span
+        aria-label={text}
+        className="inline-flex items-center justify-center w-4 h-4 rounded-full bg-gray-200 text-gray-500 text-[10px] font-bold cursor-help select-none"
+      >
+        ?
+      </span>
+      <span className="pointer-events-none absolute bottom-full left-1/2 -translate-x-1/2 mb-1.5 z-10 hidden group-hover:block w-64 rounded-md border border-gray-200 bg-white px-3 py-2 text-xs text-gray-600 shadow-lg leading-relaxed">
+        {text}
+      </span>
+    </span>
+  );
+}
+
+type NsMetrics = {
+  fmax: number;
+  precision: number;
+  recall: number;
+  tau: number;
+  coverage: number;
+  n_proteins?: number;
+};
+
+type SettingResults = Record<string, NsMetrics>; // BPO | MFO | CCO
+
+type EvaluationResult = {
+  id: string;
+  evaluation_set_id: string;
+  prediction_set_id: string;
+  scoring_config_id: string | null;
+  job_id: string | null;
+  created_at: string;
+  results: Record<string, SettingResults>; // NK | LK | PK
+};
+
+type EvaluationSet = {
+  id: string;
+  old_annotation_set_id: string;
+  new_annotation_set_id: string;
+  job_id: string | null;
+  created_at: string;
+  stats: {
+    delta_proteins?: number;
+    nk_proteins?: number;
+    lk_proteins?: number;
+    pk_proteins?: number;
+    nk_annotations?: number;
+    lk_annotations?: number;
+    pk_annotations?: number;
+    known_terms_count?: number;
+  };
+};
+
+async function apiFetch<T>(path: string, init?: RequestInit): Promise<T> {
+  const res = await fetch(`${baseUrl()}${path}`, { cache: "no-store", ...init });
+  if (!res.ok) throw new Error(await res.text());
+  return res.json();
+}
+
+const listEvaluationSets = () => apiFetch<EvaluationSet[]>("/annotations/evaluation-sets");
+const listResults = (evalId: string) =>
+  apiFetch<EvaluationResult[]>(`/annotations/evaluation-sets/${evalId}/results`);
+const deleteEvaluationSet = (evalId: string) =>
+  fetch(`${baseUrl()}/annotations/evaluation-sets/${evalId}`, { method: "DELETE" });
+
+function setLabel(s: AnnotationSet) {
+  const date = new Date(s.created_at).toLocaleDateString();
+  const count = s.annotation_count != null ? ` · ${s.annotation_count.toLocaleString()} ann.` : "";
+  return `[${s.source.toUpperCase()}] ${s.source_version ?? "—"} · ${date}${count}`;
+}
+
+function predLabel(p: PredictionSet) {
+  const date = new Date(p.created_at).toLocaleDateString();
+  const count = p.prediction_count != null ? ` · ${p.prediction_count.toLocaleString()} preds.` : "";
+  return `${p.id.slice(0, 8)}… · ${date}${count}`;
+}
+
+function evalLabel(e: EvaluationSet, annotationSets: AnnotationSet[]) {
+  const date = new Date(e.created_at).toLocaleDateString();
+  const oldSet = annotationSets.find((a) => a.id === e.old_annotation_set_id);
+  const newSet = annotationSets.find((a) => a.id === e.new_annotation_set_id);
+  const delta = e.stats.delta_proteins ?? "?";
+  return `${oldSet?.source_version ?? "old"} → ${newSet?.source_version ?? "new"} · ${delta} delta proteins · ${date}`;
+}
+
+function DownloadLink({ href, label, filename }: { href: string; label: string; filename: string }) {
+  return (
+    <a
+      href={href}
+      download={filename}
+      className="inline-flex items-center gap-1 rounded-md border border-gray-300 bg-white px-3 py-1.5 text-sm hover:bg-gray-50 transition-colors"
+    >
+      ↓ {label}
+    </a>
+  );
+}
+
+function StatBadge({ label, value, tooltip }: { label: string; value: number | undefined; tooltip?: string }) {
+  return (
+    <div className="rounded-lg border border-gray-200 bg-gray-50 px-4 py-3 text-center">
+      <div className="text-lg font-semibold text-gray-900">
+        {value != null ? value.toLocaleString() : "—"}
+      </div>
+      <div className="text-xs text-gray-500 mt-0.5">
+        {label}
+        {tooltip && <InfoTooltip text={tooltip} />}
+      </div>
+    </div>
+  );
+}
+
+const SETTING_COLORS: Record<string, string> = {
+  NK: "bg-purple-50 border-purple-200",
+  LK: "bg-blue-50 border-blue-200",
+  PK: "bg-green-50 border-green-200",
+};
+
+function ResultsTable({ results }: { results: Record<string, SettingResults> }) {
+  const t = useTranslations("evaluation");
+  const settings = ["NK", "LK", "PK"].filter((s) => results[s] && Object.keys(results[s]).length > 0);
+  if (settings.length === 0) return <p className="text-sm text-gray-400">{t("evaluationSetCard.noEvaluations")}</p>;
+
+  const NS_LABELS: Record<string, string> = {
+    BPO: t("resultMetrics.biologicalProcess"),
+    MFO: t("resultMetrics.molecularFunction"),
+    CCO: t("resultMetrics.cellularComponent"),
+  };
+
+  const SETTING_TOOLTIPS: Record<string, string> = {
+    NK: "No-Knowledge: proteins with no experimental annotations in any namespace at t0. Evaluated without -known.",
+    LK: "Limited-Knowledge: proteins annotated in some namespaces at t0 but not in the evaluated namespace. Evaluated without -known.",
+    PK: "Partial-Knowledge: proteins that already had annotations in the evaluated namespace at t0. Evaluated with -known (old terms excluded from scoring).",
+  };
+
+  return (
+    <div className="space-y-4">
+      {settings.map((setting) => (
+        <div key={setting} className={`rounded-lg border p-4 ${SETTING_COLORS[setting] ?? ""}`}>
+          <div className="text-sm font-semibold text-gray-700 mb-3">
+            {setting}
+            {SETTING_TOOLTIPS[setting] && <InfoTooltip text={SETTING_TOOLTIPS[setting]} />}
+          </div>
+          <div className="grid grid-cols-3 gap-3">
+            {["BPO", "MFO", "CCO"].map((ns) => {
+              const m = results[setting]?.[ns];
+              if (!m) return null;
+              return (
+                <div key={ns} className="rounded-md bg-white border border-gray-200 p-3">
+                  <div className="text-xs font-medium text-gray-500 mb-2">{NS_LABELS[ns]}</div>
+                  <div className="space-y-1 text-sm">
+                    <div className="flex justify-between">
+                      <span className="text-gray-500">{t("resultMetrics.fmax")}</span>
+                      <span className="font-semibold text-gray-900">{m.fmax.toFixed(3)}</span>
+                    </div>
+                    <div className="flex justify-between">
+                      <span className="text-gray-500">{t("resultMetrics.precision")}</span>
+                      <span className="text-gray-700">{m.precision.toFixed(3)}</span>
+                    </div>
+                    <div className="flex justify-between">
+                      <span className="text-gray-500">{t("resultMetrics.recall")}</span>
+                      <span className="text-gray-700">{m.recall.toFixed(3)}</span>
+                    </div>
+                    <div className="flex justify-between">
+                      <span className="text-gray-500">{t("resultMetrics.coverage")}</span>
+                      <span className="text-gray-700">{(m.coverage * 100).toFixed(1)}%</span>
+                    </div>
+                    <div className="flex justify-between">
+                      <span className="text-gray-500">{t("resultMetrics.tau")}</span>
+                      <span className="text-gray-700">{m.tau.toFixed(2)}</span>
+                    </div>
+                  </div>
+                </div>
+              );
+            })}
+          </div>
+        </div>
+      ))}
+    </div>
+  );
+}
+
+function EvaluationSetCard({
+  e,
+  annotationSets,
+  predictionSets,
+  scoringConfigs,
+  isSelected,
+  onSelect,
+  onDeleted,
+}: {
+  e: EvaluationSet;
+  annotationSets: AnnotationSet[];
+  predictionSets: PredictionSet[];
+  scoringConfigs: ScoringConfig[];
+  isSelected: boolean;
+  onSelect: () => void;
+  onDeleted: () => void;
+}) {
+  const t = useTranslations("evaluation");
+  const [results, setResults] = useState<EvaluationResult[]>([]);
+  const [loadingResults, setLoadingResults] = useState(false);
+  const [predSetId, setPredSetId] = useState("");
+  const [maxDistance, setMaxDistance] = useState("");
+  const [scoringConfigId, setScoringConfigId] = useState("");
+  const [running, setRunning] = useState(false);
+  const [runError, setRunError] = useState("");
+  const [pendingJobId, setPendingJobId] = useState<string | null>(null);
+  const [pollingResults, setPollingResults] = useState(false);
+
+  useEffect(() => {
+    if (!isSelected) return;
+    setLoadingResults(true);
+    listResults(e.id).then(setResults).finally(() => setLoadingResults(false));
+  }, [isSelected, e.id]);
+
+  useEffect(() => {
+    if (!pendingJobId) return;
+
+    setPollingResults(true);
+    let attempts = 0;
+    const MAX_ATTEMPTS = 30;
+
+    const interval = setInterval(async () => {
+      attempts++;
+      try {
+        const fresh = await listResults(e.id);
+        if (fresh.length > results.length) {
+          setResults(fresh);
+          setPendingJobId(null);
+          setPollingResults(false);
+          clearInterval(interval);
+          return;
+        }
+      } catch {
+        // Ignore transient errors during polling.
+      }
+      if (attempts >= MAX_ATTEMPTS) {
+        setPollingResults(false);
+        clearInterval(interval);
+      }
+    }, 10_000);
+
+    return () => clearInterval(interval);
+  }, [pendingJobId]);
+
+  async function refreshResults() {
+    setLoadingResults(true);
+    try {
+      setResults(await listResults(e.id));
+    } finally {
+      setLoadingResults(false);
+    }
+  }
+
+  async function handleRun() {
+    if (!predSetId) return;
+    setRunning(true);
+    setRunError("");
+    setPendingJobId(null);
+    try {
+      const body: Record<string, any> = { prediction_set_id: predSetId };
+      if (maxDistance) body.max_distance = parseFloat(maxDistance);
+      if (scoringConfigId) body.scoring_config_id = scoringConfigId;
+      const res = await apiFetch<{ id: string; status: string }>(
+        `/annotations/evaluation-sets/${e.id}/run`,
+        {
+          method: "POST",
+          headers: { "Content-Type": "application/json" },
+          body: JSON.stringify(body),
+        },
+      );
+      setPendingJobId(res.id ?? res.status ?? null);
+    } catch (err: any) {
+      setRunError(err.message ?? "Unknown error");
+    } finally {
+      setRunning(false);
+    }
+  }
+
+  return (
+    <div
+      className={`rounded-lg border transition-colors ${
+        isSelected ? "border-blue-400" : "border-gray-200"
+      }`}
+    >
+      {/* Header */}
+      <div
+        className="cursor-pointer p-4 hover:bg-gray-50 rounded-t-lg"
+        onClick={onSelect}
+      >
+        <div className="flex items-center justify-between">
+          <div className="text-sm font-medium text-gray-800">{evalLabel(e, annotationSets)}</div>
+          <button
+            onClick={async (ev) => {
+              ev.stopPropagation();
+              if (!confirm(t("evaluationSetCard.deleteConfirm"))) return;
+              await deleteEvaluationSet(e.id);
+              onDeleted();
+            }}
+            className="rounded border border-red-200 px-2 py-1 text-xs text-red-600 hover:bg-red-50 transition-colors"
+          >
+            {t("evaluationSetCard.delete")}
+          </button>
+        </div>
+        <div className="mt-3 grid grid-cols-4 gap-2">
+          <StatBadge
+            label={t("evaluationSetCard.deltaProteins")}
+            value={e.stats.delta_proteins}
+            tooltip="Proteins that gained ≥1 new experimental GO annotation between the old and new snapshot."
+          />
+          <StatBadge
+            label={t("evaluationSetCard.nkProteins")}
+            value={e.stats.nk_proteins}
+            tooltip="No-Knowledge: proteins with no experimental annotations in any namespace at t0. All new annotations form the ground truth."
+          />
+          <StatBadge
+            label={t("evaluationSetCard.lkProteins")}
+            value={e.stats.lk_proteins}
+            tooltip="Limited-Knowledge: proteins annotated in some namespaces at t0, but which gained new terms in a namespace where they had nothing. Only the new terms in that empty namespace are evaluated."
+          />
+          <StatBadge
+            label={t("evaluationSetCard.pkProteins")}
+            value={e.stats.pk_proteins}
+            tooltip="Partial-Knowledge: proteins that already had annotations in a namespace at t0 and gained new terms in that same namespace. Only the novel terms are evaluated; old terms are excluded via -known. A protein can be LK in one namespace and PK in another simultaneously."
+          />
+        </div>
+      </div>
+
+      {isSelected && (
+        <div className="border-t border-gray-200 p-4 space-y-5 bg-gray-50 rounded-b-lg">
+
+          {/* Downloads */}
+          <div>
+            <p className="text-xs font-medium text-gray-500 mb-2">
+              {t("evaluationSetCard.groundTruthFiles")}
+              <InfoTooltip text="2-column TSV files (protein accession → GO term) used as input to the cafaeval evaluator. Each file contains only the novel experimental annotations for that category." />
+            </p>
+            <div className="flex flex-wrap gap-2">
+              <DownloadLink
+                href={`${baseUrl()}/annotations/evaluation-sets/${e.id}/ground-truth-NK.tsv`}
+                label={t("evaluationSetCard.downloadNK")}
+                filename="ground_truth_NK.tsv"
+              />
+              <DownloadLink
+                href={`${baseUrl()}/annotations/evaluation-sets/${e.id}/ground-truth-LK.tsv`}
+                label={t("evaluationSetCard.downloadLK")}
+                filename="ground_truth_LK.tsv"
+              />
+              <DownloadLink
+                href={`${baseUrl()}/annotations/evaluation-sets/${e.id}/ground-truth-PK.tsv`}
+                label={t("evaluationSetCard.downloadPK")}
+                filename="ground_truth_PK.tsv"
+              />
+              <DownloadLink
+                href={`${baseUrl()}/annotations/evaluation-sets/${e.id}/known-terms.tsv`}
+                label={t("evaluationSetCard.downloadKnownTerms")}
+                filename="known_terms.tsv"
+              />
+            </div>
+            <p className="text-xs text-gray-400 mt-1">
+              {t("evaluationSetCard.downloadKnownTerms")}
+              <InfoTooltip text="All experimental annotations from the old snapshot for PK proteins in the relevant namespace. Passed to cafaeval with -known to exclude them from scoring — this penalises methods that simply repeat prior annotations." />
+              : passed to cafaeval as <code className="font-mono">-known</code> for the PK pass only.
+            </p>
+          </div>
+
+          {/* FASTA downloads */}
+          <div>
+            <p className="text-xs font-medium text-gray-500 mb-2">
+              {t("evaluationSetCard.deltaProteinSequences")}
+              <InfoTooltip text="Sequences of proteins that gained new experimental GO annotations (delta proteins). Download the full set or per-category subsets to compute embeddings and run predictions before evaluation." />
+            </p>
+            <div className="flex flex-wrap gap-2">
+              <DownloadLink
+                href={`${baseUrl()}/annotations/evaluation-sets/${e.id}/delta-proteins.fasta?category=all`}
+                label={t("evaluationSetCard.allDelta")}
+                filename={`delta_proteins_all_${e.id.slice(0, 8)}.fasta`}
+              />
+              <DownloadLink
+                href={`${baseUrl()}/annotations/evaluation-sets/${e.id}/delta-proteins.fasta?category=nk`}
+                label={t("evaluationSetCard.nkOnly")}
+                filename={`delta_proteins_nk_${e.id.slice(0, 8)}.fasta`}
+              />
+              <DownloadLink
+                href={`${baseUrl()}/annotations/evaluation-sets/${e.id}/delta-proteins.fasta?category=lk`}
+                label={t("evaluationSetCard.lkOnly")}
+                filename={`delta_proteins_lk_${e.id.slice(0, 8)}.fasta`}
+              />
+              <DownloadLink
+                href={`${baseUrl()}/annotations/evaluation-sets/${e.id}/delta-proteins.fasta?category=pk`}
+                label={t("evaluationSetCard.pkOnly")}
+                filename={`delta_proteins_pk_${e.id.slice(0, 8)}.fasta`}
+              />
+            </div>
+          </div>
+
+          {/* Run evaluation */}
+          <div className="space-y-3">
+            <p className="text-xs font-medium text-gray-500">{t("evaluationSetCard.runCafaEvaluator")}</p>
+            <div className="grid grid-cols-3 gap-3">
+              <div>
+                <label className={labelClass}>{t("evaluationSetCard.predictionSetLabel")}</label>
+                <select
+                  value={predSetId}
+                  onChange={(ev) => setPredSetId(ev.target.value)}
+                  className={selectClass}
+                >
+                  <option value="">{t("generateSection.selectSet")}</option>
+                  {predictionSets.map((p) => (
+                    <option key={p.id} value={p.id}>{predLabel(p)}</option>
+                  ))}
+                </select>
+              </div>
+              <div>
+                <label className={labelClass}>
+                  {t("evaluationSetCard.scoringConfigLabel")}
+                  <InfoTooltip text="Scoring formula applied to compute CAFA prediction scores. If omitted, falls back to 1 − cosine_distance / 2." />
+                </label>
+                <select
+                  value={scoringConfigId}
+                  onChange={(ev) => setScoringConfigId(ev.target.value)}
+                  className={selectClass}
+                >
+                  <option value="">{t("evaluationSetCard.fallbackFormula")}</option>
+                  {scoringConfigs.map((c) => (
+                    <option key={c.id} value={c.id}>{c.name}</option>
+                  ))}
+                </select>
+              </div>
+              <div>
+                <label className={labelClass}>{t("evaluationSetCard.maxDistanceLabel")}</label>
+                <input
+                  type="number" min="0" max="2" step="0.05" placeholder="no limit"
+                  value={maxDistance}
+                  onChange={(ev) => setMaxDistance(ev.target.value)}
+                  className="w-full rounded-md border border-gray-300 px-3 py-2 text-sm focus:outline-none focus:ring-2 focus:ring-blue-500"
+                />
+              </div>
+            </div>
+            {runError && (
+              <p className="rounded border border-red-200 bg-red-50 px-3 py-2 text-sm text-red-700">
+                {runError}
+              </p>
+            )}
+
+            {pendingJobId && (
+              <div className="rounded border border-blue-200 bg-blue-50 px-3 py-2.5 text-sm text-blue-800 flex items-center justify-between gap-3">
+                <span>
+                  {t("evaluationSetCard.jobQueued")}{" "}
+                  {pollingResults
+                    ? "Checking for results every 10 s…"
+                    : "Results will appear below when the job completes."}
+                </span>
+                <a
+                  href={`/jobs/${pendingJobId}`}
+                  className="shrink-0 rounded border border-blue-300 bg-white px-2 py-1 text-xs font-medium text-blue-700 hover:bg-blue-100 transition-colors"
+                >
+                  {t("evaluationSetCard.viewJob")}
+                </a>
+              </div>
+            )}
+
+            <button
+              onClick={handleRun}
+              disabled={!predSetId || running}
+              className={btnPrimary}
+            >
+              {running ? t("generateSection.generating") : t("evaluationSetCard.runEvaluation")}
+            </button>
+          </div>
+
+          {/* Results */}
+          <div>
+            <div className="flex items-center justify-between mb-3">
+              <p className="text-xs font-medium text-gray-500">
+                {t("evaluationSetCard.resultsHeading")}
+                {pollingResults && (
+                  <span className="ml-2 text-blue-500 animate-pulse">{t("evaluationSetCard.pollingResults")}</span>
+                )}
+              </p>
+              <button
+                onClick={refreshResults}
+                disabled={loadingResults}
+                className="text-xs text-gray-400 hover:text-gray-700 border rounded px-2 py-0.5 disabled:opacity-40"
+              >
+                {loadingResults ? t("evaluationSetCard.refreshing") : t("evaluationSetCard.refreshResults")}
+              </button>
+            </div>
+            {loadingResults ? (
+              <p className="text-sm text-gray-400">Loading…</p>
+            ) : results.length === 0 ? (
+              <p className="text-sm text-gray-400">{t("evaluationSetCard.noEvaluations")}</p>
+            ) : (
+              <div className="space-y-6">
+                {results.map((r) => {
+                  const pred = predictionSets.find((p) => p.id === r.prediction_set_id);
+                  const sc = scoringConfigs.find((c) => c.id === r.scoring_config_id);
+                  return (
+                    <div key={r.id} className="rounded-lg border border-gray-200 bg-white p-4 space-y-3">
+                      {/* Meta header */}
+                      <div className="flex items-start justify-between gap-2">
+                        <div className="space-y-0.5 text-xs text-gray-500 min-w-0">
+                          <div>
+                            <span className="font-medium text-gray-700">{t("evaluationSetCard.predictionSet")} </span>
+                            {pred
+                              ? <span title={r.prediction_set_id}>{r.prediction_set_id.slice(0, 8)}… · {new Date(pred.created_at).toLocaleDateString()}{pred.prediction_count != null ? ` · ${pred.prediction_count.toLocaleString()} preds.` : ""}</span>
+                              : <span className="font-mono">{r.prediction_set_id.slice(0, 8)}…</span>
+                            }
+                          </div>
+                          <div>
+                            <span className="font-medium text-gray-700">{t("evaluationSetCard.scoring")} </span>
+                            {sc ? sc.name : <span className="italic text-gray-400">{t("evaluationSetCard.fallbackFormula")}</span>}
+                            {sc?.description && <InfoTooltip text={sc.description} />}
+                          </div>
+                          <div className="text-gray-400">{new Date(r.created_at).toLocaleString()}</div>
+                        </div>
+                        <div className="flex items-center gap-2 shrink-0">
+                          <a
+                            href={`${baseUrl()}/annotations/evaluation-sets/${e.id}/results/${r.id}/artifacts.zip`}
+                            download={`cafaeval_${r.id.slice(0, 8)}.zip`}
+                            className="inline-flex items-center gap-1 rounded-md border border-gray-300 bg-white px-3 py-1 text-xs hover:bg-gray-50 transition-colors"
+                          >
+                            {t("evaluationSetCard.artifactsDownload")}
+                          </a>
+                          <button
+                            onClick={async () => {
+                              if (!confirm(t("evaluationSetCard.deleteResultConfirm"))) return;
+                              const res = await fetch(`${baseUrl()}/annotations/evaluation-sets/${e.id}/results/${r.id}`, { method: "DELETE" });
+                              if (!res.ok) throw new Error(await res.text());
+                              setResults((prev) => prev.filter((x) => x.id !== r.id));
+                            }}
+                            className="rounded-md border border-red-200 bg-white px-3 py-1 text-xs text-red-600 hover:bg-red-50 transition-colors"
+                          >
+                            {t("evaluationSetCard.resultDelete")}
+                          </button>
+                        </div>
+                      </div>
+                      <ResultsTable results={r.results} />
+                    </div>
+                  );
+                })}
+              </div>
+            )}
+          </div>
+        </div>
+      )}
+    </div>
+  );
+}
+
+export default function EvaluationPage() {
+  const t = useTranslations("evaluation");
+  const [annotationSets, setAnnotationSets] = useState<AnnotationSet[]>([]);
+  const [predictionSets, setPredictionSets] = useState<PredictionSet[]>([]);
+  const [evaluationSets, setEvaluationSets] = useState<EvaluationSet[]>([]);
+  const [scoringConfigs, setScoringConfigs] = useState<ScoringConfig[]>([]);
+  const [loading, setLoading] = useState(true);
+
+  const [oldSetId, setOldSetId] = useState("");
+  const [newSetId, setNewSetId] = useState("");
+  const [generating, setGenerating] = useState(false);
+  const [genError, setGenError] = useState("");
+  const [selectedEvalId, setSelectedEvalId] = useState("");
+
+  const reload = () =>
+    Promise.all([listAnnotationSets(), listPredictionSets(), listEvaluationSets(), listScoringConfigs()])
+      .then(([ann, pred, ev, sc]) => {
+        setAnnotationSets(ann);
+        setPredictionSets(pred);
+        setEvaluationSets(ev);
+        setScoringConfigs(sc);
+      })
+      .finally(() => setLoading(false));
+
+  useEffect(() => { reload(); }, []);
+
+  const goaSets = annotationSets.filter((s) => s.source === "goa");
+  const canGenerate = oldSetId && newSetId && oldSetId !== newSetId;
+
+  async function handleGenerate() {
+    setGenerating(true);
+    setGenError("");
+    try {
+      await apiFetch("/annotations/evaluation-sets/generate", {
+        method: "POST",
+        headers: { "Content-Type": "application/json" },
+        body: JSON.stringify({ old_annotation_set_id: oldSetId, new_annotation_set_id: newSetId }),
+      });
+      setOldSetId("");
+      setNewSetId("");
+      await reload();
+    } catch (e: any) {
+      setGenError(e.message ?? "Unknown error");
+    } finally {
+      setGenerating(false);
+    }
+  }
+
+  if (loading) return <div className="p-8 text-sm text-gray-500">Loading…</div>;
+
+  return (
+    <div className="p-8 max-w-3xl space-y-10">
+      <h1 className="text-xl font-semibold text-gray-900">{t("title")}</h1>
+
+      {/* ── Generate Evaluation Set ───────────────────────────────── */}
+      <section className="rounded-lg border border-gray-200 p-6 space-y-5">
+        <div>
+          <h2 className="text-base font-semibold text-gray-800">{t("generateSection.heading")}</h2>
+          <p className="mt-1 text-sm text-gray-500">
+            {t("generateSection.description")}
+          </p>
+        </div>
+        <div className="grid grid-cols-2 gap-4">
+          <div>
+            <label className={labelClass}>{t("generateSection.oldSetLabel")}</label>
+            <select value={oldSetId} onChange={(e) => setOldSetId(e.target.value)} className={selectClass}>
+              <option value="">{t("generateSection.selectSet")}</option>
+              {goaSets.map((s) => (
+                <option key={s.id} value={s.id}>{setLabel(s)}</option>
+              ))}
+            </select>
+          </div>
+          <div>
+            <label className={labelClass}>{t("generateSection.newSetLabel")}</label>
+            <select value={newSetId} onChange={(e) => setNewSetId(e.target.value)} className={selectClass}>
+              <option value="">{t("generateSection.selectSet")}</option>
+              {goaSets.map((s) => (
+                <option key={s.id} value={s.id}>{setLabel(s)}</option>
+              ))}
+            </select>
+          </div>
+        </div>
+        {oldSetId && newSetId && oldSetId === newSetId && (
+          <p className="text-xs text-red-500">{t("generateSection.errorSameSets")}</p>
+        )}
+        {genError && (
+          <p className="rounded border border-red-200 bg-red-50 px-3 py-2 text-sm text-red-700">{genError}</p>
+        )}
+        <button onClick={handleGenerate} disabled={!canGenerate || generating} className={btnPrimary}>
+          {generating ? t("generateSection.generating") : t("generateSection.generateEvaluationSet")}
+        </button>
+      </section>
+
+      {/* ── Evaluation Sets ───────────────────────────────────────── */}
+      {evaluationSets.length > 0 && (
+        <section className="space-y-4">
+          <h2 className="text-base font-semibold text-gray-800">{t("evaluationSetsSection.heading")}</h2>
+          {evaluationSets.map((e) => (
+            <EvaluationSetCard
+              key={e.id}
+              e={e}
+              annotationSets={annotationSets}
+              predictionSets={predictionSets}
+              scoringConfigs={scoringConfigs}
+              isSelected={selectedEvalId === e.id}
+              onSelect={() => setSelectedEvalId(e.id === selectedEvalId ? "" : e.id)}
+              onDeleted={() => setEvaluationSets((prev) => prev.filter((x) => x.id !== e.id))}
+            />
+          ))}
+        </section>
+      )}
+
+      {/* ── Evaluator command reference ───────────────────────────── */}
+      <section className="rounded-lg border border-gray-100 bg-gray-50 p-5">
+        <h2 className="text-sm font-semibold text-gray-700 mb-2">{t("manualEvaluatorSection.heading")}</h2>
+        <pre className="text-xs text-gray-600 overflow-x-auto whitespace-pre-wrap leading-relaxed">
+{`python -m cafaeval go-basic.obo predictions/ ground_truth_NK.tsv -out_dir results/NK
+python -m cafaeval go-basic.obo predictions/ ground_truth_LK.tsv -out_dir results/LK
+python -m cafaeval go-basic.obo predictions/ ground_truth_PK.tsv -known known_terms.tsv -out_dir results/PK`}
+        </pre>
+      </section>
+    </div>
+  );
+}
diff --git a/apps/web/app/functional-annotation/[id]/page.tsx b/apps/web/app/[locale]/functional-annotation/[id]/page.tsx
similarity index 64%
rename from apps/web/app/functional-annotation/[id]/page.tsx
rename to apps/web/app/[locale]/functional-annotation/[id]/page.tsx
index 2056b36..28c0ff7 100644
--- a/apps/web/app/functional-annotation/[id]/page.tsx
+++ b/apps/web/app/[locale]/functional-annotation/[id]/page.tsx
@@ -11,10 +11,234 @@ import {
   getProteinAnnotations,
   getGoSubgraph,
   getGoTermDistribution,
+  listScoringConfigs,
+  getScoredTsvUrl,
+  createScoringConfig,
   Prediction,
   ProteinAnnotation,
   GoSubgraph,
+  ScoringConfig,
 } from "@/lib/api";
+
+// ── Scoring engine (mirrors protea/core/scoring.py) ──────────────────────────
+//
+// Evidence-code quality weights: same default table as the Python backend.
+// When a ScoringConfig carries custom `evidence_weights`, those overrides take
+// precedence; codes absent from the override still resolve via this table.
+
+const DEFAULT_EVIDENCE_WEIGHTS: Record<string, number> = {
+  // Experimental — direct biological evidence
+  EXP: 1.0, IDA: 1.0, IPI: 1.0, IMP: 1.0, IGI: 1.0, IEP: 1.0,
+  HTP: 1.0, HDA: 1.0, HMP: 1.0, HGI: 1.0, HEP: 1.0,
+  IC:  1.0, TAS: 1.0,
+  // Computational / Phylogenetic
+  ISS: 0.7, ISO: 0.7, ISA: 0.7, ISM: 0.7, IGC: 0.7,
+  IBA: 0.7, IBD: 0.7, IKR: 0.7, IRD: 0.7, RCA: 0.7,
+  // Electronic / author statement
+  NAS: 0.5,
+  IEA: 0.3,
+  // No biological data
+  ND: 0.1,
+};
+
+/** Fallback weight for codes not found in any lookup table. */
+const DEFAULT_EVIDENCE_WEIGHT_FALLBACK = 0.5;
+
+/**
+ * Resolve the quality weight for a single GO evidence code.
+ *
+ * Resolution order:
+ * 1. Config-level override (config.evidence_weights), if present.
+ * 2. Module-level DEFAULT_EVIDENCE_WEIGHTS table.
+ * 3. DEFAULT_EVIDENCE_WEIGHT_FALLBACK (0.5).
+ */
+function resolveEvidenceWeight(
+  code: string | null | undefined,
+  overrides: Record<string, number> | null | undefined,
+): number {
+  if (!code) return DEFAULT_EVIDENCE_WEIGHT_FALLBACK;
+  if (overrides && code in overrides) return overrides[code];
+  return DEFAULT_EVIDENCE_WEIGHTS[code] ?? DEFAULT_EVIDENCE_WEIGHT_FALLBACK;
+}
+
+/**
+ * Compute a [0, 1] confidence score for a prediction row.
+ *
+ * Mirrors the logic in `protea/core/scoring.py::compute_score()`, including
+ * the two-level evidence weight resolution so the UI score always matches
+ * the backend TSV export exactly.
+ */
+function computeScore(pred: Prediction, config: ScoringConfig): number {
+  const evWeight = resolveEvidenceWeight(pred.evidence_code, config.evidence_weights);
+
+  const signals: Record<string, number | null> = {
+    embedding_similarity: 1 - pred.distance / 2,
+    identity_nw: pred.identity_nw,
+    identity_sw: pred.identity_sw,
+    evidence_weight: pred.evidence_code != null ? evWeight : null,
+    taxonomic_proximity:
+      pred.taxonomic_distance != null ? 1 / (1 + pred.taxonomic_distance) : null,
+  };
+
+  let weightedSum = 0;
+  let totalWeight = 0;
+  for (const [signal, weight] of Object.entries(config.weights)) {
+    if (weight <= 0) continue;
+    const val = signals[signal];
+    if (val == null) continue;
+    weightedSum += weight * Math.max(0, Math.min(1, val));
+    totalWeight += weight;
+  }
+
+  if (totalWeight === 0) return 0;
+  let score = weightedSum / totalWeight;
+
+  if (config.formula === "evidence_weighted") {
+    score *= evWeight;
+  }
+
+  return score;
+}
+// ── Scoring signals ───────────────────────────────────────────────────────────
+
+const SIGNALS: { key: string; label: string; hint: string }[] = [
+  { key: "embedding_similarity", label: "Emb. similarity", hint: "1 − cosine_distance / 2, always available" },
+  { key: "identity_nw",          label: "Identity NW",     hint: "Global identity Needleman-Wunsch (requires compute_alignments)" },
+  { key: "identity_sw",          label: "Identity SW",     hint: "Local identity Smith-Waterman (requires compute_alignments)" },
+  { key: "evidence_weight",      label: "Evidence",        hint: "GO evidence code quality (EXP→1.0, IEA→0.3)" },
+  { key: "taxonomic_proximity",  label: "Tax. proximity",  hint: "1/(1+tax_dist) (requires compute_taxonomy)" },
+];
+
+const DEFAULT_CUSTOM_WEIGHTS: Record<string, number> = {
+  embedding_similarity: 1.0,
+  identity_nw: 0.0,
+  identity_sw: 0.0,
+  evidence_weight: 0.0,
+  taxonomic_proximity: 0.0,
+};
+
+export const CUSTOM_ID = "__custom__";
+
+// ── WeightPanel ───────────────────────────────────────────────────────────────
+
+function WeightPanel({
+  config,
+  isCustom,
+  customWeights,
+  customFormula,
+  onWeightChange,
+  onFormulaChange,
+  onSave,
+  saving,
+}: {
+  config?: ScoringConfig;
+  isCustom: boolean;
+  customWeights: Record<string, number>;
+  customFormula: string;
+  onWeightChange: (key: string, val: number) => void;
+  onFormulaChange: (f: string) => void;
+  onSave: (name: string) => void;
+  saving: boolean;
+}) {
+  const [saveName, setSaveName] = useState("");
+  const [showSaveForm, setShowSaveForm] = useState(false);
+
+  if (!isCustom && config) {
+    return (
+      <div className="flex flex-wrap items-center gap-1.5 text-xs max-w-sm">
+        <span className="rounded bg-blue-50 px-1.5 py-0.5 font-mono text-blue-700 border border-blue-100">
+          {config.formula}
+        </span>
+        {SIGNALS.map(({ key, label }) => {
+          const w = config.weights[key] ?? 0;
+          return (
+            <span
+              key={key}
+              title={`${label}: ${w}`}
+              className={`rounded px-1.5 py-0.5 font-mono ${w > 0 ? "bg-gray-100 text-gray-700" : "text-gray-300"}`}
+            >
+              {label} {w}
+            </span>
+          );
+        })}
+        {config.description && (
+          <span className="text-gray-400 italic ml-1">{config.description}</span>
+        )}
+      </div>
+    );
+  }
+
+  if (isCustom) {
+    return (
+      <div className="rounded-lg border bg-white p-3 shadow-sm w-72">
+        <div className="flex items-center justify-between mb-3">
+          <span className="text-xs font-semibold text-gray-500 uppercase tracking-wide">Custom weights</span>
+          <select
+            value={customFormula}
+            onChange={(e) => onFormulaChange(e.target.value)}
+            className="rounded border px-1.5 py-0.5 text-xs focus:outline-none focus:ring-1 focus:ring-blue-500"
+          >
+            <option value="linear">linear</option>
+            <option value="evidence_weighted">evidence_weighted</option>
+          </select>
+        </div>
+        <div className="space-y-2">
+          {SIGNALS.map(({ key, label, hint }) => {
+            const val = customWeights[key] ?? 0;
+            return (
+              <div key={key} className="flex items-center gap-2" title={hint}>
+                <span className="text-xs text-gray-600 w-32 shrink-0">{label}</span>
+                <input
+                  type="range" min="0" max="1" step="0.05"
+                  value={val}
+                  onChange={(e) => onWeightChange(key, parseFloat(e.target.value))}
+                  className="flex-1 accent-blue-500"
+                />
+                <span className="font-mono text-xs text-gray-700 w-8 text-right">{val.toFixed(2)}</span>
+              </div>
+            );
+          })}
+        </div>
+        <div className="mt-3 border-t pt-2.5">
+          {!showSaveForm ? (
+            <button
+              onClick={() => setShowSaveForm(true)}
+              className="text-xs text-blue-600 hover:underline"
+            >
+              Save as named config…
+            </button>
+          ) : (
+            <div className="flex gap-1">
+              <input
+                type="text"
+                value={saveName}
+                onChange={(e) => setSaveName(e.target.value)}
+                placeholder="Config name"
+                className="flex-1 rounded border px-2 py-1 text-xs focus:outline-none focus:ring-1 focus:ring-blue-500"
+              />
+              <button
+                onClick={() => { onSave(saveName); setShowSaveForm(false); setSaveName(""); }}
+                disabled={!saveName.trim() || saving}
+                className="rounded bg-blue-600 px-2 py-1 text-xs text-white hover:bg-blue-700 disabled:opacity-40"
+              >
+                {saving ? "…" : "OK"}
+              </button>
+              <button
+                onClick={() => { setShowSaveForm(false); setSaveName(""); }}
+                className="text-xs text-gray-400 hover:text-gray-600 px-1"
+              >
+                ✕
+              </button>
+            </div>
+          )}
+        </div>
+      </div>
+    );
+  }
+
+  return null;
+}
+
 import dynamic from "next/dynamic";
 const GoGraph = dynamic(() => import("@/components/GoGraph"), { ssr: false });
 
@@ -64,19 +288,37 @@ function pct(v: number | null) {
   return `${(v * 100).toFixed(1)}%`;
 }
 
-function PredictionTable({ preds, annotatedGoIds }: { preds: Prediction[]; annotatedGoIds: Set<string> }) {
+function scoreColor(score: number): string {
+  // green for high scores, yellow for mid, red for low
+  if (score >= 0.75) return "text-green-700 font-semibold";
+  if (score >= 0.5) return "text-yellow-700";
+  return "text-red-500";
+}
+
+function PredictionTable({ preds, annotatedGoIds, scoringConfig }: { preds: Prediction[]; annotatedGoIds: Set<string>; scoringConfig?: ScoringConfig }) {
   const hasAlignment = preds.some((p) => p.identity_nw != null);
   const hasTaxonomy = preds.some((p) => p.taxonomic_relation != null);
+  const hasScore = !!scoringConfig;
   const [expanded, setExpanded] = useState<number | null>(null);
 
+  // Sort by score descending when a config is active
+  const sortedPreds = hasScore
+    ? [...preds].sort((a, b) => computeScore(b, scoringConfig!) - computeScore(a, scoringConfig!))
+    : preds;
+
   // Column layout adapts to available features
-  const baseGrid = hasAlignment || hasTaxonomy
-    ? "grid-cols-[80px_1fr_100px_65px_70px_70px]"
-    : "grid-cols-[90px_1fr_110px_75px]";
+  const baseGrid = hasScore
+    ? (hasAlignment || hasTaxonomy
+        ? "grid-cols-[60px_80px_1fr_100px_65px_70px_70px]"
+        : "grid-cols-[60px_90px_1fr_110px_75px]")
+    : (hasAlignment || hasTaxonomy
+        ? "grid-cols-[80px_1fr_100px_65px_70px_70px]"
+        : "grid-cols-[90px_1fr_110px_75px]");
 
   return (
     <div className="overflow-hidden rounded-md border bg-white">
       <div className={`grid ${baseGrid} gap-1 border-b bg-gray-50 px-3 py-1.5 text-xs font-semibold uppercase tracking-wide text-gray-400`}>
+        {hasScore && <div>Score</div>}
         <div>GO ID</div>
         <div>Name</div>
         <div>Ref. Protein</div>
@@ -84,9 +326,9 @@ function PredictionTable({ preds, annotatedGoIds }: { preds: Prediction[]; annot
         {hasAlignment && <><div>NW id%</div><div>SW id%</div></>}
         {hasTaxonomy && !hasAlignment && <><div>Relation</div><div>Tax dist</div></>}
       </div>
-      {preds.length === 0 ? (
+      {sortedPreds.length === 0 ? (
         <p className="px-3 py-3 text-xs text-gray-300">—</p>
-      ) : preds.map((pred, i) => (
+      ) : sortedPreds.map((pred, i) => (
         <div key={i}>
           <div
             className={`grid ${baseGrid} gap-1 border-b px-3 py-2 text-xs last:border-0 items-center
@@ -94,6 +336,11 @@ function PredictionTable({ preds, annotatedGoIds }: { preds: Prediction[]; annot
               ${(hasAlignment || hasTaxonomy) ? "cursor-pointer hover:bg-blue-50/40" : ""}`}
             onClick={() => (hasAlignment || hasTaxonomy) ? setExpanded(expanded === i ? null : i) : undefined}
           >
+            {hasScore && (
+              <span className={`font-mono ${scoreColor(computeScore(pred, scoringConfig!))}`}>
+                {computeScore(pred, scoringConfig!).toFixed(3)}
+              </span>
+            )}
             <span className="font-mono text-blue-600">{pred.go_id}</span>
             <span className="text-gray-700 truncate" title={pred.name ?? ""}>{pred.name ?? "—"}</span>
             <Link
@@ -193,6 +440,7 @@ function ProteinDetail({
   loading,
   onClose,
   ontologySnapshotId,
+  scoringConfig,
 }: {
   accession: string;
   inDb: boolean;
@@ -201,6 +449,7 @@ function ProteinDetail({
   loading: boolean;
   onClose: () => void;
   ontologySnapshotId: string | null;
+  scoringConfig?: ScoringConfig;
 }) {
   const [subgraph, setSubgraph] = useState<GoSubgraph | null>(null);
   const [loadingGraph, setLoadingGraph] = useState(false);
@@ -294,7 +543,7 @@ function ProteinDetail({
             </div>
             <div className="grid grid-cols-2 gap-3">
               {/* Predictions */}
-              <PredictionTable preds={preds} annotatedGoIds={annotatedGoIds} />
+              <PredictionTable preds={preds} annotatedGoIds={annotatedGoIds} scoringConfig={scoringConfig} />
 
 
               {/* Known annotations */}
@@ -320,14 +569,15 @@ function ProteinDetail({
   );
 }
 
-function DownloadButton({ setId }: { setId: string }) {
+function DownloadButton({ setId, scoringConfigId, customBlocked }: { setId: string; scoringConfigId?: string; customBlocked?: boolean }) {
   const [open, setOpen] = useState(false);
   const [aspect, setAspect] = useState("");
   const [maxDist, setMaxDist] = useState("");
+  const [minScore, setMinScore] = useState("");
 
   const apiBase = process.env.NEXT_PUBLIC_API_URL ?? "";
 
-  function buildUrl() {
+  function buildRawUrl() {
     const params = new URLSearchParams();
     if (aspect) params.set("aspect", aspect);
     if (maxDist) params.set("max_distance", maxDist);
@@ -335,10 +585,17 @@ function DownloadButton({ setId }: { setId: string }) {
     return `${apiBase}/embeddings/prediction-sets/${setId}/predictions.tsv${qs ? `?${qs}` : ""}`;
   }
 
+  function buildScoredUrl() {
+    return getScoredTsvUrl(setId, scoringConfigId!, minScore ? { minScore: parseFloat(minScore) } : undefined);
+  }
+
+  const isScored = !!scoringConfigId;
+
   return (
     <div className="relative">
       <button
         onClick={() => setOpen((v) => !v)}
+        title={customBlocked ? "Guarda el config custom para descargar scored TSV" : undefined}
         className="flex items-center gap-1.5 rounded-md border bg-white px-3 py-1.5 text-sm font-medium text-gray-700 shadow-sm hover:bg-gray-50"
       >
         ↓ Download TSV
@@ -348,33 +605,62 @@ function DownloadButton({ setId }: { setId: string }) {
         <div className="absolute right-0 top-10 z-20 w-64 rounded-lg border bg-white p-4 shadow-lg">
           <p className="text-xs font-semibold uppercase tracking-wide text-gray-400 mb-3">Download options</p>
 
-          <label className="block text-xs text-gray-600 mb-1">GO Aspect</label>
-          <select
-            value={aspect}
-            onChange={(e) => setAspect(e.target.value)}
-            className="w-full rounded border px-2 py-1.5 text-sm mb-3 focus:outline-none focus:ring-2 focus:ring-blue-500"
-          >
-            <option value="">All aspects</option>
-            <option value="F">F — Molecular Function</option>
-            <option value="P">P — Biological Process</option>
-            <option value="C">C — Cellular Component</option>
-          </select>
+          {!isScored && (
+            <>
+              <label className="block text-xs text-gray-600 mb-1">GO Aspect</label>
+              <select
+                value={aspect}
+                onChange={(e) => setAspect(e.target.value)}
+                className="w-full rounded border px-2 py-1.5 text-sm mb-3 focus:outline-none focus:ring-2 focus:ring-blue-500"
+              >
+                <option value="">All aspects</option>
+                <option value="F">F — Molecular Function</option>
+                <option value="P">P — Biological Process</option>
+                <option value="C">C — Cellular Component</option>
+              </select>
 
-          <label className="block text-xs text-gray-600 mb-1">Max distance</label>
-          <input
-            type="number"
-            min="0"
-            max="2"
-            step="0.01"
-            placeholder="e.g. 0.3 (no limit)"
-            value={maxDist}
-            onChange={(e) => setMaxDist(e.target.value)}
-            className="w-full rounded border px-2 py-1.5 text-sm mb-4 focus:outline-none focus:ring-2 focus:ring-blue-500"
-          />
+              <label className="block text-xs text-gray-600 mb-1">Max distance</label>
+              <input
+                type="number"
+                min="0"
+                max="2"
+                step="0.01"
+                placeholder="e.g. 0.3 (no limit)"
+                value={maxDist}
+                onChange={(e) => setMaxDist(e.target.value)}
+                className="w-full rounded border px-2 py-1.5 text-sm mb-4 focus:outline-none focus:ring-2 focus:ring-blue-500"
+              />
+            </>
+          )}
+
+          {customBlocked && (
+            <p className="text-xs text-amber-700 bg-amber-50 rounded px-2 py-1.5 mb-3">
+              Guarda el config custom para habilitar scored TSV
+            </p>
+          )}
+
+          {isScored && (
+            <>
+              <p className="text-xs text-blue-700 bg-blue-50 rounded px-2 py-1.5 mb-3">
+                Scored TSV — includes computed score column
+              </p>
+              <label className="block text-xs text-gray-600 mb-1">Min score</label>
+              <input
+                type="number"
+                min="0"
+                max="1"
+                step="0.01"
+                placeholder="e.g. 0.5 (no limit)"
+                value={minScore}
+                onChange={(e) => setMinScore(e.target.value)}
+                className="w-full rounded border px-2 py-1.5 text-sm mb-4 focus:outline-none focus:ring-2 focus:ring-blue-500"
+              />
+            </>
+          )}
 
           <a
-            href={buildUrl()}
-            download={`predictions_${setId.slice(0, 8)}.tsv`}
+            href={isScored ? buildScoredUrl() : buildRawUrl()}
+            download={isScored ? `scored_${setId.slice(0, 8)}.tsv` : `predictions_${setId.slice(0, 8)}.tsv`}
             onClick={() => setOpen(false)}
             className="block w-full rounded-md bg-blue-600 px-3 py-2 text-center text-sm font-medium text-white hover:bg-blue-700"
           >
@@ -394,6 +680,13 @@ export default function PredictionSetDetailPage({ params }: { params: Promise<{
   const [annotationSetId, setAnnotationSetId] = useState<string | null>(null);
   const [ontologySnapshotId, setOntologySnapshotId] = useState<string | null>(null);
 
+  // Scoring
+  const [scoringConfigs, setScoringConfigs] = useState<ScoringConfig[]>([]);
+  const [selectedConfigId, setSelectedConfigId] = useState<string>("");
+  const [customWeights, setCustomWeights] = useState<Record<string, number>>(DEFAULT_CUSTOM_WEIGHTS);
+  const [customFormula, setCustomFormula] = useState("linear");
+  const [savingCustom, setSavingCustom] = useState(false);
+
   // Proteins tab
   const [proteins, setProteins] = useState<{ accession: string; go_count: number; min_distance: number | null; annotation_count: number; match_count: number; in_db: boolean }[]>([]);
   const [proteinTotal, setProteinTotal] = useState(0);
@@ -424,8 +717,16 @@ export default function PredictionSetDetailPage({ params }: { params: Promise<{
         setOntologySnapshotId(ps.ontology_snapshot_id);
       })
       .catch(() => {});
+    listScoringConfigs()
+      .then(setScoringConfigs)
+      .catch(() => {});
   }, [setId]);
 
+  const selectedConfig: ScoringConfig | undefined =
+    selectedConfigId === CUSTOM_ID
+      ? { id: CUSTOM_ID, name: "Custom", formula: customFormula, weights: customWeights, evidence_weights: null, created_at: "" }
+      : scoringConfigs.find((c) => c.id === selectedConfigId);
+
   async function loadProteins(offset = 0, search = proteinSearch) {
     setLoadingProteins(true);
     try {
@@ -503,10 +804,55 @@ export default function PredictionSetDetailPage({ params }: { params: Promise<{
             Prediction Set <span className="font-mono text-base text-gray-500">{shortId(setId)}…</span>
           </h1>
         </div>
-        <DownloadButton setId={setId} />
+        <div className="flex flex-col items-end gap-2">
+          <div className="flex items-center gap-2">
+            <div className="flex items-center gap-1.5">
+              <label className="text-xs text-gray-500 whitespace-nowrap">Scoring</label>
+              <select
+                value={selectedConfigId}
+                onChange={(e) => setSelectedConfigId(e.target.value)}
+                className="rounded-md border bg-white px-2 py-1.5 text-sm text-gray-700 shadow-sm focus:outline-none focus:ring-2 focus:ring-blue-500"
+              >
+                <option value="">Raw distance</option>
+                {scoringConfigs.map((c) => (
+                  <option key={c.id} value={c.id}>{c.name}</option>
+                ))}
+                <option value={CUSTOM_ID}>Custom…</option>
+              </select>
+            </div>
+            <DownloadButton
+              setId={setId}
+              scoringConfigId={
+                selectedConfigId && selectedConfigId !== CUSTOM_ID ? selectedConfigId : undefined
+              }
+              customBlocked={selectedConfigId === CUSTOM_ID}
+            />
+          </div>
+          {selectedConfigId && (
+            <WeightPanel
+              config={selectedConfigId !== CUSTOM_ID ? selectedConfig : undefined}
+              isCustom={selectedConfigId === CUSTOM_ID}
+              customWeights={customWeights}
+              customFormula={customFormula}
+              onWeightChange={(key, val) => setCustomWeights((prev) => ({ ...prev, [key]: val }))}
+              onFormulaChange={setCustomFormula}
+              onSave={async (name) => {
+                setSavingCustom(true);
+                try {
+                  const saved = await createScoringConfig({ name, formula: customFormula, weights: customWeights });
+                  setScoringConfigs((prev) => [...prev, saved]);
+                  setSelectedConfigId(saved.id);
+                } finally {
+                  setSavingCustom(false);
+                }
+              }}
+              saving={savingCustom}
+            />
+          )}
+        </div>
       </div>
 
-      <div className="flex gap-1 border-b mb-6">
+      <div className="flex gap-1 border-b mb-6 overflow-x-auto">
         {tabs.map((t) => (
           <button
             key={t.key}
@@ -547,7 +893,7 @@ export default function PredictionSetDetailPage({ params }: { params: Promise<{
             <span className="ml-auto text-sm text-gray-400">{proteinTotal.toLocaleString()} proteins</span>
           </div>
 
-          <div className="overflow-hidden rounded-lg border bg-white shadow-sm">
+          <div className="overflow-x-auto rounded-lg border bg-white shadow-sm">
             <div className="grid grid-cols-[160px_90px_120px_90px_90px] gap-2 border-b bg-gray-50 px-4 py-2.5 text-xs font-semibold uppercase tracking-wide text-gray-500">
               <div>Accession</div>
               <div>Predicted</div>
@@ -603,6 +949,7 @@ export default function PredictionSetDetailPage({ params }: { params: Promise<{
                       loading={loadingDetail}
                       onClose={() => setSelectedAccession(null)}
                       ontologySnapshotId={ontologySnapshotId}
+                      scoringConfig={selectedConfig}
                     />
                   </div>
                 )}
@@ -653,7 +1000,7 @@ export default function PredictionSetDetailPage({ params }: { params: Promise<{
                       {ASPECT_LABELS[asp]}
                       <span className="ml-2 text-xs font-normal text-gray-400">top {terms.length} terms</span>
                     </p>
-                    <div className="overflow-hidden rounded-lg border bg-white shadow-sm">
+                    <div className="overflow-x-auto rounded-lg border bg-white shadow-sm">
                       {terms.map((t) => (
                         <div key={t.go_id} className="flex items-center gap-3 border-b px-4 py-2.5 last:border-0">
                           <span className="font-mono text-xs text-blue-600 w-24 shrink-0">{t.go_id}</span>
diff --git a/apps/web/app/functional-annotation/page.tsx b/apps/web/app/[locale]/functional-annotation/page.tsx
similarity index 71%
rename from apps/web/app/functional-annotation/page.tsx
rename to apps/web/app/[locale]/functional-annotation/page.tsx
index d23feec..6aca0b0 100644
--- a/apps/web/app/functional-annotation/page.tsx
+++ b/apps/web/app/[locale]/functional-annotation/page.tsx
@@ -2,6 +2,7 @@
 
 import { useEffect, useState } from "react";
 import Link from "next/link";
+import { useTranslations } from "next-intl";
 import { useToast } from "@/components/Toast";
 import { SkeletonTableRow } from "@/components/Skeleton";
 import {
@@ -34,10 +35,10 @@ const inputClass = "w-full rounded-md border px-3 py-2 text-sm focus:outline-non
 const labelClass = "block text-sm font-medium text-gray-700 mb-1";
 
 export default function FunctionalAnnotationPage() {
+  const t = useTranslations("functionalAnnotation");
   const toast = useToast();
   const [activeTab, setActiveTab] = useState<Tab>("predict");
 
-  // Shared data
   const [configs, setConfigs] = useState<EmbeddingConfig[]>([]);
   const [annotationSets, setAnnotationSets] = useState<AnnotationSet[]>([]);
   const [ontologySnapshots, setOntologySnapshots] = useState<OntologySnapshot[]>([]);
@@ -45,7 +46,6 @@ export default function FunctionalAnnotationPage() {
   const [predictionSets, setPredictionSets] = useState<PredictionSet[]>([]);
   const [loading, setLoading] = useState(true);
 
-  // Predict form
   const [predConfigId, setPredConfigId] = useState("");
   const [predQuerySetId, setPredQuerySetId] = useState("");
   const [predAnnotationSetId, setPredAnnotationSetId] = useState("");
@@ -60,6 +60,7 @@ export default function FunctionalAnnotationPage() {
   const [predFaissNprobe, setPredFaissNprobe] = useState(10);
   const [predFaissHnswM, setPredFaissHnswM] = useState(32);
   const [predFaissHnswEf, setPredFaissHnswEf] = useState(64);
+  const [predAspectSeparatedKnn, setPredAspectSeparatedKnn] = useState(true);
   const [predComputeAlignments, setPredComputeAlignments] = useState(false);
   const [predComputeTaxonomy, setPredComputeTaxonomy] = useState(false);
   const [predResult, setPredResult] = useState<{ id: string; status: string } | null>(null);
@@ -97,13 +98,8 @@ export default function FunctionalAnnotationPage() {
     }
   }
 
-  useEffect(() => {
-    loadAll();
-  }, []);
-
-  useEffect(() => {
-    if (activeTab === "results") loadResults();
-  }, [activeTab]);
+  useEffect(() => { loadAll(); }, []);
+  useEffect(() => { if (activeTab === "results") loadResults(); }, [activeTab]);
 
   async function handlePredictSubmit(e: React.FormEvent) {
     e.preventDefault();
@@ -126,11 +122,12 @@ export default function FunctionalAnnotationPage() {
         faiss_nprobe: predFaissNprobe,
         faiss_hnsw_m: predFaissHnswM,
         faiss_hnsw_ef_search: predFaissHnswEf,
+        aspect_separated_knn: predAspectSeparatedKnn,
         compute_alignments: predComputeAlignments,
         compute_taxonomy: predComputeTaxonomy,
       });
       setPredResult(result);
-      toast("Annotation job queued", "success");
+      toast(t("predictTab.launchAnnotationJob"), "success");
     } catch (err: any) {
       setPredError(String(err));
       toast(String(err), "error");
@@ -143,8 +140,8 @@ export default function FunctionalAnnotationPage() {
     const ps = predictionSets.find((p) => p.id === id);
     const count = ps?.prediction_count ?? 0;
     const msg = count > 0
-      ? `Delete this annotation set and its ${count.toLocaleString()} GO term assignments? This cannot be undone.`
-      : "Delete this annotation set?";
+      ? t("resultsTab.deleteConfirm", { count: count.toLocaleString() })
+      : t("resultsTab.deleteConfirmNoAssignments");
     if (!confirm(msg)) return;
     try {
       const r = await deletePredictionSet(id);
@@ -156,45 +153,45 @@ export default function FunctionalAnnotationPage() {
   }
 
   const tabs: { key: Tab; label: string }[] = [
-    { key: "predict", label: "Run Annotation" },
-    { key: "results", label: "Results" },
+    { key: "predict", label: t("tabs.predict") },
+    { key: "results", label: t("tabs.results") },
   ];
 
   return (
     <>
       <div className="flex items-center justify-between mb-4">
-        <h1 className="text-xl font-semibold">Functional Annotation</h1>
+        <h1 className="text-xl font-semibold">{t("title")}</h1>
       </div>
 
-      <div className="flex gap-1 border-b mb-6">
-        {tabs.map((t) => (
+      <div className="flex gap-1 border-b mb-6 overflow-x-auto">
+        {tabs.map((tab) => (
           <button
-            key={t.key}
-            onClick={() => setActiveTab(t.key)}
+            key={tab.key}
+            onClick={() => setActiveTab(tab.key)}
             className={`px-4 py-2 text-sm font-medium border-b-2 transition-colors ${
-              activeTab === t.key
+              activeTab === tab.key
                 ? "border-blue-600 text-blue-600"
                 : "border-transparent text-gray-500 hover:text-gray-700"
             }`}
           >
-            {t.label}
+            {tab.label}
           </button>
         ))}
       </div>
 
       {/* ── Run Annotation ── */}
       {activeTab === "predict" && (
-        <div className="max-w-lg">
+        <div className="max-w-2xl">
           <div className="rounded-lg border bg-white p-6 shadow-sm">
-            <h2 className="text-base font-semibold mb-4">GO Term Annotation by Embedding Similarity</h2>
+            <h2 className="text-base font-semibold mb-4">{t("predictTab.title")}</h2>
             {loading ? (
-              <p className="text-sm text-gray-400">Loading…</p>
+              <p className="text-sm text-gray-400">{t("predictTab.loading")}</p>
             ) : (
               <form onSubmit={handlePredictSubmit} className="space-y-4">
                 <div>
-                  <label className={labelClass}>Embedding Config</label>
+                  <label className={labelClass}>{t("predictTab.configLabel")}</label>
                   <select value={predConfigId} onChange={(e) => setPredConfigId(e.target.value)} required className={inputClass}>
-                    {configs.length === 0 && <option value="">— no configs available —</option>}
+                    {configs.length === 0 && <option value="">{t("predictTab.noConfigs")}</option>}
                     {configs.map((c) => (
                       <option key={c.id} value={c.id}>
                         {c.description || c.model_name} ({shortId(c.id)})
@@ -205,10 +202,10 @@ export default function FunctionalAnnotationPage() {
 
                 <div>
                   <label className={labelClass}>
-                    Query Set <span className="font-normal text-gray-400">(optional — leave empty to annotate all)</span>
+                    {t("predictTab.querySetLabel")} <span className="font-normal text-gray-400">{t("predictTab.querySetHelper")}</span>
                   </label>
                   <select value={predQuerySetId} onChange={(e) => setPredQuerySetId(e.target.value)} className={inputClass}>
-                    <option value="">— all sequences —</option>
+                    <option value="">{t("predictTab.allSequences")}</option>
                     {querySets.map((qs) => (
                       <option key={qs.id} value={qs.id}>
                         {qs.name} ({qs.entry_count} seqs)
@@ -218,9 +215,9 @@ export default function FunctionalAnnotationPage() {
                 </div>
 
                 <div>
-                  <label className={labelClass}>Annotation Set</label>
+                  <label className={labelClass}>{t("predictTab.annotationSetLabel")}</label>
                   <select value={predAnnotationSetId} onChange={(e) => setPredAnnotationSetId(e.target.value)} required className={inputClass}>
-                    {annotationSets.length === 0 && <option value="">— no annotation sets available —</option>}
+                    {annotationSets.length === 0 && <option value="">{t("predictTab.noAnnotationSets")}</option>}
                     {annotationSets.map((a) => (
                       <option key={a.id} value={a.id}>
                         {a.source} {a.source_version ? `(${a.source_version})` : ""} — {shortId(a.id)}
@@ -230,9 +227,9 @@ export default function FunctionalAnnotationPage() {
                 </div>
 
                 <div>
-                  <label className={labelClass}>Ontology Snapshot</label>
+                  <label className={labelClass}>{t("predictTab.snapshotLabel")}</label>
                   <select value={predSnapshotId} onChange={(e) => setPredSnapshotId(e.target.value)} required className={inputClass}>
-                    {ontologySnapshots.length === 0 && <option value="">— no snapshots available —</option>}
+                    {ontologySnapshots.length === 0 && <option value="">{t("predictTab.noSnapshots")}</option>}
                     {ontologySnapshots.map((s) => (
                       <option key={s.id} value={s.id}>
                         {s.obo_version} — {shortId(s.id)}
@@ -243,17 +240,17 @@ export default function FunctionalAnnotationPage() {
 
                 <div className="grid grid-cols-3 gap-3">
                   <div>
-                    <label className={labelClass}>Limit per Entry</label>
+                    <label className={labelClass}>{t("predictTab.limitPerEntryLabel")}</label>
                     <input
                       type="number"
                       value={predLimitPerEntry}
-                      onChange={(e) => setPredLimitPerEntry(parseInt(e.target.value, 10))}
+                      onChange={(e) => { const v = parseInt(e.target.value, 10); if (!isNaN(v)) setPredLimitPerEntry(v); }}
                       min={1}
                       className={inputClass}
                     />
                   </div>
                   <div>
-                    <label className={labelClass}>Batch Size</label>
+                    <label className={labelClass}>{t("predictTab.batchSizeLabel")}</label>
                     <input
                       type="number"
                       value={predBatchSize}
@@ -263,7 +260,7 @@ export default function FunctionalAnnotationPage() {
                     />
                   </div>
                   <div>
-                    <label className={labelClass}>Distance Threshold <span className="font-normal text-gray-400">(optional)</span></label>
+                    <label className={labelClass}>{t("predictTab.distanceThresholdLabel")} <span className="font-normal text-gray-400">{t("predictTab.distanceThresholdHelper")}</span></label>
                     <input
                       type="number"
                       step="any"
@@ -275,9 +272,27 @@ export default function FunctionalAnnotationPage() {
                   </div>
                 </div>
 
-                {/* Feature engineering */}
+                {/* KNN Strategy + Feature Engineering */}
                 <div className="rounded-md border border-gray-200 bg-gray-50 p-4 space-y-3">
-                  <p className="text-xs font-semibold uppercase tracking-wide text-gray-500">Feature Engineering <span className="font-normal normal-case text-gray-400">(opt-in — adds compute time)</span></p>
+                  <p className="text-xs font-semibold uppercase tracking-wide text-gray-500">{t("predictTab.knnStrategy")}</p>
+                  <div className="flex flex-col gap-2 mb-4">
+                    <label className="flex items-start gap-2 cursor-pointer">
+                      <input
+                        type="checkbox"
+                        checked={predAspectSeparatedKnn}
+                        onChange={(e) => setPredAspectSeparatedKnn(e.target.checked)}
+                        className="mt-0.5 rounded"
+                      />
+                      <span className="text-sm text-gray-700">
+                        {t("predictTab.aspectSeparatedKnn")}
+                        <span className="ml-1.5 text-xs text-gray-400">{t("predictTab.aspectSeparatedKnnHelper")}</span>
+                      </span>
+                    </label>
+                  </div>
+
+                  <p className="text-xs font-semibold uppercase tracking-wide text-gray-500">
+                    {t("predictTab.featureEngineering")} <span className="font-normal normal-case text-gray-400">{t("predictTab.featureEngineeringHelper")}</span>
+                  </p>
                   <div className="flex flex-col gap-2">
                     <label className="flex items-start gap-2 cursor-pointer">
                       <input
@@ -287,8 +302,8 @@ export default function FunctionalAnnotationPage() {
                         className="mt-0.5 rounded"
                       />
                       <span className="text-sm text-gray-700">
-                        Sequence alignments
-                        <span className="ml-1.5 text-xs text-gray-400">NW (global) + SW (local) via parasail/BLOSUM62</span>
+                        {t("predictTab.sequenceAlignments")}
+                        <span className="ml-1.5 text-xs text-gray-400">{t("predictTab.sequenceAlignmentsHelper")}</span>
                       </span>
                     </label>
                     <label className="flex items-start gap-2 cursor-pointer">
@@ -299,29 +314,29 @@ export default function FunctionalAnnotationPage() {
                         className="mt-0.5 rounded"
                       />
                       <span className="text-sm text-gray-700">
-                        Taxonomic distance
-                        <span className="ml-1.5 text-xs text-gray-400">LCA, distance and relation via NCBI taxonomy</span>
+                        {t("predictTab.taxonomicDistance")}
+                        <span className="ml-1.5 text-xs text-gray-400">{t("predictTab.taxonomicDistanceHelper")}</span>
                       </span>
                     </label>
                   </div>
                 </div>
 
-                {/* Search backend */}
+                {/* Search Backend */}
                 <div className="rounded-md border border-gray-200 bg-gray-50 p-4 space-y-3">
-                  <p className="text-xs font-semibold uppercase tracking-wide text-gray-500">Search Backend</p>
+                  <p className="text-xs font-semibold uppercase tracking-wide text-gray-500">{t("predictTab.searchBackend")}</p>
                   <div className="grid grid-cols-2 gap-3">
                     <div>
-                      <label className={labelClass}>Backend</label>
+                      <label className={labelClass}>{t("predictTab.searchBackendLabel")}</label>
                       <select value={predSearchBackend} onChange={(e) => setPredSearchBackend(e.target.value)} className={inputClass}>
-                        <option value="numpy">numpy — exact</option>
-                        <option value="faiss">faiss — indexed</option>
+                        <option value="numpy">{t("predictTab.numpyBackend")}</option>
+                        <option value="faiss">{t("predictTab.faissBackend")}</option>
                       </select>
                     </div>
                     <div>
-                      <label className={labelClass}>Metric</label>
+                      <label className={labelClass}>{t("predictTab.metricLabel")}</label>
                       <select value={predMetric} onChange={(e) => setPredMetric(e.target.value)} className={inputClass}>
-                        <option value="cosine">cosine</option>
-                        <option value="l2">L2 (Euclidean²)</option>
+                        <option value="cosine">{t("predictTab.cosineSimilarity")}</option>
+                        <option value="l2">{t("predictTab.euclideanDistance")}</option>
                       </select>
                     </div>
                   </div>
@@ -329,21 +344,21 @@ export default function FunctionalAnnotationPage() {
                   {predSearchBackend === "faiss" && (
                     <>
                       <div>
-                        <label className={labelClass}>Index Type</label>
+                        <label className={labelClass}>{t("predictTab.indexTypeLabel")}</label>
                         <select value={predFaissIndex} onChange={(e) => setPredFaissIndex(e.target.value)} className={inputClass}>
-                          <option value="Flat">Flat — exact</option>
-                          <option value="IVFFlat">IVFFlat — approximate (&gt;100K refs)</option>
-                          <option value="HNSW">HNSW — approximate, graph-based</option>
+                          <option value="Flat">{t("predictTab.flatIndex")}</option>
+                          <option value="IVFFlat">{t("predictTab.ivfflatIndex")}</option>
+                          <option value="HNSW">{t("predictTab.hnswIndex")}</option>
                         </select>
                       </div>
                       {predFaissIndex === "IVFFlat" && (
                         <div className="grid grid-cols-2 gap-3">
                           <div>
-                            <label className={labelClass}>nlist</label>
+                            <label className={labelClass}>{t("predictTab.nlistLabel")}</label>
                             <input type="number" value={predFaissNlist} onChange={(e) => setPredFaissNlist(parseInt(e.target.value, 10))} min={1} className={inputClass} />
                           </div>
                           <div>
-                            <label className={labelClass}>nprobe</label>
+                            <label className={labelClass}>{t("predictTab.nprobeLabel")}</label>
                             <input type="number" value={predFaissNprobe} onChange={(e) => setPredFaissNprobe(parseInt(e.target.value, 10))} min={1} className={inputClass} />
                           </div>
                         </div>
@@ -351,11 +366,11 @@ export default function FunctionalAnnotationPage() {
                       {predFaissIndex === "HNSW" && (
                         <div className="grid grid-cols-2 gap-3">
                           <div>
-                            <label className={labelClass}>M</label>
+                            <label className={labelClass}>{t("predictTab.mLabel")}</label>
                             <input type="number" value={predFaissHnswM} onChange={(e) => setPredFaissHnswM(parseInt(e.target.value, 10))} min={2} className={inputClass} />
                           </div>
                           <div>
-                            <label className={labelClass}>efSearch</label>
+                            <label className={labelClass}>{t("predictTab.efSearchLabel")}</label>
                             <input type="number" value={predFaissHnswEf} onChange={(e) => setPredFaissHnswEf(parseInt(e.target.value, 10))} min={1} className={inputClass} />
                           </div>
                         </div>
@@ -385,7 +400,7 @@ export default function FunctionalAnnotationPage() {
                     disabled={predSubmitting || configs.length === 0}
                     className="rounded-md bg-blue-600 px-4 py-2 text-sm text-white hover:bg-blue-700 disabled:opacity-50"
                   >
-                    {predSubmitting ? "Launching…" : "Launch Annotation Job"}
+                    {predSubmitting ? t("predictTab.launching") : t("predictTab.launchAnnotationJob")}
                   </button>
                 </div>
               </form>
@@ -402,19 +417,19 @@ export default function FunctionalAnnotationPage() {
               {predictionSets.length} annotation result{predictionSets.length !== 1 ? "s" : ""}
             </p>
             <button onClick={loadResults} className="rounded-md border bg-white px-3 py-1.5 text-sm hover:bg-gray-50">
-              Refresh
+              {t("resultsTab.refresh")}
             </button>
           </div>
 
-          <div className="overflow-hidden rounded-lg border bg-white shadow-sm">
+          <div className="overflow-x-auto rounded-lg border bg-white shadow-sm">
             <div className="grid grid-cols-[80px_100px_100px_100px_90px_120px_160px_60px] gap-2 border-b bg-gray-50 px-4 py-2.5 text-xs font-semibold uppercase tracking-wide text-gray-500">
-              <div>ID</div>
-              <div>Config</div>
-              <div>Annotation Set</div>
-              <div>Snapshot</div>
-              <div>GO Terms</div>
-              <div>Dist. Threshold</div>
-              <div>Created</div>
+              <div>{t("resultsTab.tableHeaders.id")}</div>
+              <div>{t("resultsTab.tableHeaders.config")}</div>
+              <div>{t("resultsTab.tableHeaders.annotationSet")}</div>
+              <div>{t("resultsTab.tableHeaders.snapshot")}</div>
+              <div>{t("resultsTab.tableHeaders.goTerms")}</div>
+              <div>{t("resultsTab.tableHeaders.distanceThreshold")}</div>
+              <div>{t("resultsTab.tableHeaders.created")}</div>
               <div></div>
             </div>
 
@@ -428,9 +443,9 @@ export default function FunctionalAnnotationPage() {
                     {shortId(ps.id)}…
                   </Link>
                 </div>
-                <div className="font-mono text-xs text-gray-500" title={ps.embedding_config_id}>{shortId(ps.embedding_config_id)}</div>
-                <div className="font-mono text-xs text-gray-500" title={ps.annotation_set_id}>{shortId(ps.annotation_set_id)}</div>
-                <div className="font-mono text-xs text-gray-500" title={ps.ontology_snapshot_id}>{shortId(ps.ontology_snapshot_id)}</div>
+                <div className="text-xs text-gray-700" title={ps.embedding_config_id}>{ps.embedding_config_name ?? shortId(ps.embedding_config_id)}</div>
+                <div className="text-xs text-gray-700" title={ps.annotation_set_id}>{ps.annotation_set_label ?? shortId(ps.annotation_set_id)}</div>
+                <div className="text-xs text-gray-700" title={ps.ontology_snapshot_id}>{ps.ontology_snapshot_version ?? shortId(ps.ontology_snapshot_id)}</div>
                 <div className="text-gray-700">{ps.prediction_count ?? 0}</div>
                 <div className="text-gray-600">
                   {ps.distance_threshold != null ? ps.distance_threshold : <span className="text-gray-400">—</span>}
@@ -441,7 +456,7 @@ export default function FunctionalAnnotationPage() {
                     onClick={() => handleDeleteResult(ps.id)}
                     className="rounded border border-red-200 px-2 py-1 text-xs text-red-600 hover:bg-red-50 transition-colors"
                   >
-                    Delete
+                    {t("resultsTab.delete")}
                   </button>
                 </div>
               </div>
@@ -449,7 +464,7 @@ export default function FunctionalAnnotationPage() {
 
             {predictionSets.length === 0 && (
               <div className="px-4 py-8 text-center text-sm text-gray-400">
-                No annotation results yet. Run an annotation job from the Run Annotation tab.
+                {t("resultsTab.noResults")}
               </div>
             )}
           </div>
diff --git a/apps/web/app/jobs/[id]/page.tsx b/apps/web/app/[locale]/jobs/[id]/page.tsx
similarity index 85%
rename from apps/web/app/jobs/[id]/page.tsx
rename to apps/web/app/[locale]/jobs/[id]/page.tsx
index bbc986f..9de6abf 100644
--- a/apps/web/app/jobs/[id]/page.tsx
+++ b/apps/web/app/[locale]/jobs/[id]/page.tsx
@@ -7,6 +7,7 @@ import { cancelJob, deleteJob, getJob, getJobEvents, listJobs, JobEvent, Job } f
 import { StatusBadge } from "@/components/StatusBadge";
 import { EventTimeline } from "@/components/EventTimeline";
 import { useToast } from "@/components/Toast";
+import { useTranslations } from "next-intl";
 
 const TERMINAL = ["succeeded", "failed", "cancelled"];
 
@@ -59,6 +60,7 @@ function ProgressBar({
 
 export default function JobDetail({ params }: { params: Promise<{ id: string }> }) {
   const { id: jobId } = use(params);
+  const t = useTranslations("jobs");
   const [job, setJob] = useState<any>(null);
   const [events, setEvents] = useState<JobEvent[]>([]);
   const [children, setChildren] = useState<Job[]>([]);
@@ -108,7 +110,7 @@ export default function JobDetail({ params }: { params: Promise<{ id: string }>
   }, [job?.status]);
 
   async function onDelete() {
-    if (!confirm("Delete this job?")) return;
+    if (!confirm(t("jobDetail.deleteConfirm"))) return;
     try {
       setError("");
       await deleteJob(jobId);
@@ -139,31 +141,31 @@ export default function JobDetail({ params }: { params: Promise<{ id: string }>
     <div>
       {/* Header */}
       <div className="flex flex-wrap items-center gap-3">
-        <Link href="/jobs" className="text-sm text-gray-500 hover:text-gray-800">← Jobs</Link>
-        <h1 className="text-xl font-semibold">Job Detail</h1>
+        <Link href="/jobs" className="text-sm text-gray-500 hover:text-gray-800">{t("jobDetail.backToJobs")}</Link>
+        <h1 className="text-xl font-semibold">{t("jobDetail.title")}</h1>
         {isLive && (
           <span className="flex items-center gap-1 text-xs text-blue-600">
             <span className="inline-block h-2 w-2 animate-pulse rounded-full bg-blue-500" />
-            Live
+            {t("jobDetail.live")}
           </span>
         )}
         <div className="ml-auto flex gap-2">
           <button onClick={refresh} className="rounded-md border bg-white px-3 py-1.5 text-sm hover:bg-gray-50">
-            Refresh
+            {t("refresh")}
           </button>
           <button
             onClick={onCancel}
             disabled={isTerminal}
             className="rounded-md border px-3 py-1.5 text-sm hover:bg-gray-50 disabled:opacity-40"
           >
-            Cancel
+            {t("jobDetail.cancel")}
           </button>
           <button
             onClick={onDelete}
             disabled={isLive}
             className="rounded-md border border-red-200 px-3 py-1.5 text-sm text-red-600 hover:bg-red-50 disabled:opacity-40"
           >
-            Delete
+            {t("jobDetail.delete")}
           </button>
         </div>
       </div>
@@ -184,10 +186,10 @@ export default function JobDetail({ params }: { params: Promise<{ id: string }>
           </div>
 
           <div className="grid grid-cols-2 gap-x-6 gap-y-1 text-sm">
-            <div><span className="text-gray-500">Queue:</span> <span className="font-mono text-xs">{job.queue_name}</span></div>
-            <div><span className="text-gray-500">Created:</span> {formatDate(job.created_at)}</div>
-            <div><span className="text-gray-500">Started:</span> {formatDate(job.started_at)}</div>
-            <div><span className="text-gray-500">Finished:</span> {formatDate(job.finished_at)}</div>
+            <div><span className="text-gray-500">{t("jobDetail.queue")}</span> <span className="font-mono text-xs">{job.queue_name}</span></div>
+            <div><span className="text-gray-500">{t("jobDetail.created")}</span> {formatDate(job.created_at)}</div>
+            <div><span className="text-gray-500">{t("jobDetail.started")}</span> {formatDate(job.started_at)}</div>
+            <div><span className="text-gray-500">{t("jobDetail.finished")}</span> {formatDate(job.finished_at)}</div>
             {job.error_code && (
               <div className="col-span-2 text-red-600">
                 <span className="font-medium">{job.error_code}:</span> {job.error_message}
@@ -209,7 +211,7 @@ export default function JobDetail({ params }: { params: Promise<{ id: string }>
 
           {job.payload && Object.keys(job.payload).length > 0 && (
             <details className="text-sm">
-              <summary className="cursor-pointer text-gray-500 hover:text-gray-700">Payload</summary>
+              <summary className="cursor-pointer text-gray-500 hover:text-gray-700">{t("jobDetail.payloadLabel")}</summary>
               <pre className="mt-1 rounded bg-gray-50 p-2 text-xs overflow-auto">{JSON.stringify(job.payload, null, 2)}</pre>
             </details>
           )}
@@ -221,7 +223,7 @@ export default function JobDetail({ params }: { params: Promise<{ id: string }>
         <div className="mt-6">
           <div className="mb-3 flex items-center gap-4 flex-wrap">
             <h2 className="text-base font-semibold">
-              Child Jobs <span className="text-xs font-normal text-gray-400">({children.length})</span>
+              {t("jobDetail.childJobsTitle")} <span className="text-xs font-normal text-gray-400">{t("jobDetail.childJobsCount", { count: children.length })}</span>
             </h2>
             {(["running", "queued", "succeeded", "failed", "cancelled"] as const).map((s) => {
               const n = children.filter((c) => c.status === s).length;
@@ -229,11 +231,11 @@ export default function JobDetail({ params }: { params: Promise<{ id: string }>
               return <span key={s} className="text-xs text-gray-500">{s}: <strong>{n}</strong></span>;
             })}
           </div>
-          <div className="overflow-hidden rounded-lg border bg-white shadow-sm">
+          <div className="overflow-x-auto rounded-lg border bg-white shadow-sm">
             <div className="grid grid-cols-[120px_1fr_160px] gap-2 border-b bg-gray-50 px-4 py-2 text-xs font-semibold uppercase tracking-wide text-gray-500">
-              <div>Status</div>
-              <div>Job ID</div>
-              <div>Finished</div>
+              <div>{t("status")}</div>
+              <div>{t("jobId")}</div>
+              <div>{t("jobDetail.finished")}</div>
             </div>
             {[...children]
               .sort((a, b) => {
@@ -258,7 +260,7 @@ export default function JobDetail({ params }: { params: Promise<{ id: string }>
       {/* Events */}
       <div className="mt-6">
         <h2 className="mb-3 text-base font-semibold">
-          Events <span className="text-xs font-normal text-gray-400">({events.length})</span>
+          {t("jobDetail.eventsTitle")} <span className="text-xs font-normal text-gray-400">{t("jobDetail.eventsCount", { count: events.length })}</span>
         </h2>
         <EventTimeline events={events} />
       </div>
diff --git a/apps/web/app/jobs/page.tsx b/apps/web/app/[locale]/jobs/page.tsx
similarity index 76%
rename from apps/web/app/jobs/page.tsx
rename to apps/web/app/[locale]/jobs/page.tsx
index 08f6655..7eff46a 100644
--- a/apps/web/app/jobs/page.tsx
+++ b/apps/web/app/[locale]/jobs/page.tsx
@@ -6,6 +6,8 @@ import { listJobs, Job } from "@/lib/api";
 import { StatusBadge } from "@/components/StatusBadge";
 import { SkeletonTableRow } from "@/components/Skeleton";
 import { useToast } from "@/components/Toast";
+import { useTranslations } from "next-intl";
+
 const STATUS_OPTIONS = ["", "queued", "running", "succeeded", "failed", "cancelled"];
 
 function formatDate(iso?: string | null) {
@@ -45,6 +47,7 @@ function InlineProgress({
 }
 
 export default function JobsPage() {
+  const t = useTranslations("jobs");
   const toast = useToast();
   const [jobs, setJobs] = useState<Job[]>([]);
   const [loading, setLoading] = useState(true);
@@ -91,11 +94,11 @@ export default function JobsPage() {
   return (
     <>
       <div className="flex flex-wrap items-center gap-3">
-        <h1 className="text-xl font-semibold">Jobs</h1>
+        <h1 className="text-xl font-semibold">{t("title")}</h1>
         {activeCount > 0 && (
           <span className="flex items-center gap-1.5 rounded-full bg-blue-50 px-2.5 py-0.5 text-xs font-medium text-blue-700 border border-blue-100">
             <span className="inline-block h-1.5 w-1.5 animate-pulse rounded-full bg-blue-500" />
-            {activeCount} active
+            {t("activeJobs", { count: activeCount })}
           </span>
         )}
 
@@ -106,7 +109,7 @@ export default function JobsPage() {
             className="rounded-md border bg-white px-3 py-1.5 text-sm focus:outline-none focus:ring-2 focus:ring-blue-500"
           >
             {STATUS_OPTIONS.map((s) => (
-              <option key={s} value={s}>{s ? s.charAt(0).toUpperCase() + s.slice(1) : "All statuses"}</option>
+              <option key={s} value={s}>{s ? s.charAt(0).toUpperCase() + s.slice(1) : t("allStatuses")}</option>
             ))}
           </select>
 
@@ -117,7 +120,7 @@ export default function JobsPage() {
               onChange={(e) => setAutoRefresh(e.target.checked)}
               className="rounded"
             />
-            Auto-refresh
+            {t("autoRefresh")}
             {autoRefresh && <span className="inline-block h-2 w-2 animate-pulse rounded-full bg-green-500" />}
           </label>
 
@@ -125,7 +128,7 @@ export default function JobsPage() {
             onClick={() => refresh(statusFilter)}
             className="rounded-md border bg-white px-3 py-1.5 text-sm hover:bg-gray-50"
           >
-            Refresh
+            {t("refresh")}
           </button>
         </div>
       </div>
@@ -136,12 +139,37 @@ export default function JobsPage() {
         </pre>
       )}
 
-      <div className="mt-4 overflow-hidden rounded-lg border bg-white shadow-sm">
+      {/* Mobile card list */}
+      <div className="mt-4 lg:hidden space-y-2">
+        {loading && Array.from({ length: 5 }).map((_, i) => (
+          <div key={i} className="rounded-lg border bg-white p-3 shadow-sm animate-pulse space-y-2">
+            <div className="h-4 bg-gray-200 rounded w-24" />
+            <div className="h-3 bg-gray-100 rounded w-40" />
+          </div>
+        ))}
+        {!loading && jobs.length === 0 && (
+          <div className="rounded-lg border bg-white px-4 py-8 text-center text-sm text-gray-400 shadow-sm">{t("noJobsFound")}</div>
+        )}
+        {!loading && jobs.map((j) => (
+          <Link key={j.id} href={`/jobs/${j.id}`} className="block rounded-lg border bg-white p-3 shadow-sm hover:border-blue-200 hover:bg-blue-50 transition-colors">
+            <div className="flex items-start justify-between gap-2">
+              <StatusBadge status={j.status} />
+              <span className="text-xs text-gray-400">{formatDate(j.created_at)}</span>
+            </div>
+            <p className="mt-1.5 text-sm font-medium text-gray-800">{j.operation}</p>
+            <InlineProgress current={j.progress_current} total={j.progress_total} />
+            <p className="mt-1 font-mono text-xs text-gray-400 truncate">{j.id}</p>
+          </Link>
+        ))}
+      </div>
+
+      {/* Desktop table */}
+      <div className="mt-4 hidden lg:block overflow-hidden rounded-lg border bg-white shadow-sm">
         <div className="grid grid-cols-[140px_180px_1fr_180px] gap-2 border-b bg-gray-50 px-4 py-2.5 text-xs font-semibold uppercase tracking-wide text-gray-500">
-          <div>Status</div>
-          <div>Operation</div>
-          <div>Job ID</div>
-          <div>Created</div>
+          <div>{t("status")}</div>
+          <div>{t("operation")}</div>
+          <div>{t("jobId")}</div>
+          <div>{t("created")}</div>
         </div>
 
         {loading && Array.from({ length: 5 }).map((_, i) => (
@@ -166,7 +194,7 @@ export default function JobsPage() {
 
         {!loading && jobs.length === 0 && (
           <div className="px-4 py-8 text-center text-sm text-gray-400">
-            No jobs found.
+            {t("noJobsFound")}
           </div>
         )}
       </div>
diff --git a/apps/web/app/[locale]/layout.tsx b/apps/web/app/[locale]/layout.tsx
new file mode 100644
index 0000000..0b99bd9
--- /dev/null
+++ b/apps/web/app/[locale]/layout.tsx
@@ -0,0 +1,54 @@
+import type { Metadata } from "next";
+import { Geist, Geist_Mono } from "next/font/google";
+import "../globals.css";
+import { ResetDbButton } from "@/components/ResetDbButton";
+import { NavLinks } from "@/components/NavLinks";
+import { SupportButton } from "@/components/SupportButton";
+import { ToastProvider } from "@/components/Toast";
+import { UsagePolicyModal } from "@/components/UsagePolicyModal";
+import { NextIntlClientProvider } from "next-intl";
+import { getMessages } from "next-intl/server";
+import { LanguageSwitcher } from "@/components/LanguageSwitcher";
+
+const geistSans = Geist({ variable: "--font-geist-sans", subsets: ["latin"] });
+const geistMono = Geist_Mono({ variable: "--font-geist-mono", subsets: ["latin"] });
+
+export const metadata: Metadata = {
+  title: "PROTEA",
+  description: "Protein data platform — job queue and pipeline management",
+};
+
+export default async function LocaleLayout({
+  children,
+  params,
+}: {
+  children: React.ReactNode;
+  params: Promise<{ locale: string }>;
+}) {
+  const { locale } = await params;
+  const messages = await getMessages();
+  return (
+    <html lang={locale}>
+      <body className={`${geistSans.variable} ${geistMono.variable} antialiased min-h-screen bg-gray-50`}>
+        <NextIntlClientProvider messages={messages}>
+          <UsagePolicyModal />
+          <ToastProvider>
+            <header className="relative border-b bg-white px-4 sm:px-6 py-3 flex items-center gap-3">
+              <span className="text-lg font-bold tracking-tight text-blue-700">PROTEA</span>
+              <span className="hidden lg:inline text-gray-300">|</span>
+              <NavLinks />
+              <div className="ml-auto flex items-center gap-2 sm:gap-3">
+                <LanguageSwitcher />
+                <SupportButton />
+                <ResetDbButton />
+              </div>
+            </header>
+            <main className="mx-auto max-w-5xl px-4 sm:px-6 py-4 sm:py-6">
+              {children}
+            </main>
+          </ToastProvider>
+        </NextIntlClientProvider>
+      </body>
+    </html>
+  );
+}
diff --git a/apps/web/app/maintenance/page.tsx b/apps/web/app/[locale]/maintenance/page.tsx
similarity index 72%
rename from apps/web/app/maintenance/page.tsx
rename to apps/web/app/[locale]/maintenance/page.tsx
index b7547f5..d573395 100644
--- a/apps/web/app/maintenance/page.tsx
+++ b/apps/web/app/[locale]/maintenance/page.tsx
@@ -10,6 +10,7 @@ import {
   type VacuumEmbeddingsPreview,
 } from "@/lib/api";
 import { useToast } from "@/components/Toast";
+import { useTranslations } from "next-intl";
 
 function StatRow({ label, value, highlight }: { label: string; value: number | null; highlight?: boolean }) {
   return (
@@ -33,6 +34,11 @@ function VacuumCard({
   onVacuum,
   loading,
   vacuuming,
+  labelClean,
+  labelToClean,
+  labelRefresh,
+  labelVacuum,
+  labelCleaning,
 }: {
   title: string;
   description: string;
@@ -44,6 +50,11 @@ function VacuumCard({
   onVacuum: () => void;
   loading: boolean;
   vacuuming: boolean;
+  labelClean: string;
+  labelToClean: string;
+  labelRefresh: string;
+  labelVacuum: string;
+  labelCleaning: string;
 }) {
   const hasOrphans = orphanValue !== null && orphanValue > 0;
   const pct = totalValue ? Math.round(((orphanValue ?? 0) / totalValue) * 100) : 0;
@@ -61,7 +72,7 @@ function VacuumCard({
               hasOrphans ? "bg-amber-100 text-amber-700" : "bg-green-100 text-green-700"
             }`}
           >
-            {hasOrphans ? `${orphanValue.toLocaleString()} to clean` : "Clean"}
+            {hasOrphans ? labelToClean : labelClean}
           </span>
         )}
       </div>
@@ -89,14 +100,14 @@ function VacuumCard({
           disabled={loading}
           className="px-3 py-1.5 text-sm border border-gray-300 rounded hover:bg-gray-50 disabled:opacity-50 transition-colors"
         >
-          {loading ? "Loading…" : "Refresh"}
+          {loading ? "Loading…" : labelRefresh}
         </button>
         <button
           onClick={onVacuum}
           disabled={vacuuming || !hasOrphans}
           className="px-3 py-1.5 text-sm bg-amber-500 text-white rounded hover:bg-amber-600 disabled:opacity-40 disabled:cursor-not-allowed transition-colors"
         >
-          {vacuuming ? "Cleaning…" : `Vacuum ${orphanValue !== null ? `(${orphanValue.toLocaleString()})` : ""}`}
+          {vacuuming ? labelCleaning : `${labelVacuum} ${orphanValue !== null ? `(${orphanValue.toLocaleString()})` : ""}`}
         </button>
       </div>
     </div>
@@ -104,6 +115,7 @@ function VacuumCard({
 }
 
 export default function MaintenancePage() {
+  const t = useTranslations("maintenance");
   const toast = useToast();
 
   const [seqPreview, setSeqPreview] = useState<VacuumSequencesPreview | null>(null);
@@ -188,23 +200,26 @@ export default function MaintenancePage() {
   return (
     <main className="max-w-2xl mx-auto px-4 py-8 space-y-6">
       <div>
-        <h1 className="text-2xl font-bold text-gray-900">Maintenance</h1>
-        <p className="text-sm text-gray-500 mt-1">
-          Clean up orphaned data that accumulates over time. All operations are safe to run while the system is active.
-        </p>
+        <h1 className="text-2xl font-bold text-gray-900">{t("title")}</h1>
+        <p className="text-sm text-gray-500 mt-1">{t("description")}</p>
       </div>
 
       <VacuumCard
-        title="Orphan Sequences"
-        description="Sequences with no Protein and no QuerySet entry pointing to them. Deleting them also cascades to their embeddings."
-        orphanLabel="Orphan sequences"
+        title={t("orphanSequences.title")}
+        description={t("orphanSequences.description")}
+        orphanLabel={t("orphanSequences.orphanLabel")}
         orphanValue={seqPreview?.orphan_sequences ?? null}
         totalValue={seqPreview?.total_sequences ?? null}
+        labelClean={t("orphanSequences.clean")}
+        labelToClean={t("orphanSequences.toClean", { count: seqPreview?.orphan_sequences ?? 0 })}
+        labelRefresh={t("orphanSequences.refresh")}
+        labelVacuum={t("orphanSequences.vacuum")}
+        labelCleaning={t("unindexedEmbeddings.cleaning")}
         stats={
           <>
-            <StatRow label="Total sequences" value={seqPreview?.total_sequences ?? null} />
-            <StatRow label="Referenced sequences" value={seqPreview?.referenced_sequences ?? null} />
-            <StatRow label="Orphan sequences" value={seqPreview?.orphan_sequences ?? null} highlight />
+            <StatRow label={t("orphanSequences.totalSequences")} value={seqPreview?.total_sequences ?? null} />
+            <StatRow label={t("orphanSequences.referencedSequences")} value={seqPreview?.referenced_sequences ?? null} />
+            <StatRow label={t("orphanSequences.orphanLabel")} value={seqPreview?.orphan_sequences ?? null} highlight />
           </>
         }
         onPreview={loadSeqPreview}
@@ -214,16 +229,21 @@ export default function MaintenancePage() {
       />
 
       <VacuumCard
-        title="Unindexed Embeddings"
-        description="Embeddings for sequences not in the protein reference database (e.g. query proteins after prediction). Safe to delete once predictions have been generated."
-        orphanLabel="Unindexed embeddings"
+        title={t("unindexedEmbeddings.title")}
+        description={t("unindexedEmbeddings.description")}
+        orphanLabel={t("unindexedEmbeddings.orphanLabel")}
         orphanValue={embPreview?.unindexed_embeddings ?? null}
         totalValue={embPreview?.total_embeddings ?? null}
+        labelClean={t("unindexedEmbeddings.clean")}
+        labelToClean={t("unindexedEmbeddings.toClean", { count: embPreview?.unindexed_embeddings ?? 0 })}
+        labelRefresh={t("unindexedEmbeddings.refresh")}
+        labelVacuum={t("unindexedEmbeddings.vacuum")}
+        labelCleaning={t("unindexedEmbeddings.cleaning")}
         stats={
           <>
-            <StatRow label="Total embeddings" value={embPreview?.total_embeddings ?? null} />
-            <StatRow label="Indexed embeddings" value={embPreview?.indexed_embeddings ?? null} />
-            <StatRow label="Unindexed embeddings" value={embPreview?.unindexed_embeddings ?? null} highlight />
+            <StatRow label={t("unindexedEmbeddings.totalEmbeddings")} value={embPreview?.total_embeddings ?? null} />
+            <StatRow label={t("unindexedEmbeddings.indexedEmbeddings")} value={embPreview?.indexed_embeddings ?? null} />
+            <StatRow label={t("unindexedEmbeddings.orphanLabel")} value={embPreview?.unindexed_embeddings ?? null} highlight />
           </>
         }
         onPreview={loadEmbPreview}
diff --git a/apps/web/app/[locale]/page.tsx b/apps/web/app/[locale]/page.tsx
new file mode 100644
index 0000000..fe3f8ed
--- /dev/null
+++ b/apps/web/app/[locale]/page.tsx
@@ -0,0 +1,5 @@
+import { redirect } from "next/navigation";
+
+export default function Home({ params }: { params: Promise<{ locale: string }> }) {
+  redirect("/jobs");
+}
diff --git a/apps/web/app/proteins/[accession]/page.tsx b/apps/web/app/[locale]/proteins/[accession]/page.tsx
similarity index 77%
rename from apps/web/app/proteins/[accession]/page.tsx
rename to apps/web/app/[locale]/proteins/[accession]/page.tsx
index a08c286..2c5b4c0 100644
--- a/apps/web/app/proteins/[accession]/page.tsx
+++ b/apps/web/app/[locale]/proteins/[accession]/page.tsx
@@ -3,17 +3,13 @@
 import { use, useEffect, useState } from "react";
 import Link from "next/link";
 import { useToast } from "@/components/Toast";
+import { useTranslations } from "next-intl";
 import { getProtein, getProteinAnnotations, getGoSubgraph, listOntologySnapshots, ProteinDetail, ProteinAnnotation, GoSubgraph } from "@/lib/api";
 import dynamic from "next/dynamic";
 const GoGraph = dynamic(() => import("@/components/GoGraph"), { ssr: false });
 
 type Tab = "overview" | "annotations";
 
-const ASPECT_LABELS: Record<string, string> = {
-  F: "Molecular Function",
-  P: "Biological Process",
-  C: "Cellular Component",
-};
 const ASPECT_COLORS: Record<string, string> = {
   F: "bg-purple-50 text-purple-700 border-purple-100",
   P: "bg-green-50 text-green-700 border-green-100",
@@ -41,6 +37,7 @@ function Field({ label, value }: { label: string; value?: string | null }) {
 
 export default function ProteinDetailPage({ params }: { params: Promise<{ accession: string }> }) {
   const { accession } = use(params);
+  const t = useTranslations("proteinDetail");
   const toast = useToast();
   const [protein, setProtein] = useState<ProteinDetail | null>(null);
   const [loading, setLoading] = useState(true);
@@ -83,15 +80,15 @@ export default function ProteinDetailPage({ params }: { params: Promise<{ access
   }
 
   const tabs: { key: Tab; label: string }[] = [
-    { key: "overview", label: "Overview" },
-    { key: "annotations", label: `GO Annotations${protein.go_annotation_count > 0 ? ` (${protein.go_annotation_count.toLocaleString()})` : ""}` },
+    { key: "overview", label: t("tabs.overview") },
+    { key: "annotations", label: `${t("tabs.annotations")}${protein.go_annotation_count > 0 ? ` (${protein.go_annotation_count.toLocaleString()})` : ""}` },
   ];
 
   return (
     <>
       {/* Header */}
       <div className="mb-6">
-        <Link href="/proteins" className="text-sm text-gray-400 hover:text-gray-600">← Proteins</Link>
+        <Link href="/proteins" className="text-sm text-gray-400 hover:text-gray-600">{t("backToProteins")}</Link>
         <div className="flex items-start gap-4 mt-2">
           <div>
             <h1 className="text-2xl font-bold text-gray-900 font-mono">{protein.accession}</h1>
@@ -114,18 +111,18 @@ export default function ProteinDetailPage({ params }: { params: Promise<{ access
       </div>
 
       {/* Tabs */}
-      <div className="flex gap-1 border-b mb-6">
-        {tabs.map((t) => (
+      <div className="flex gap-1 border-b mb-6 overflow-x-auto">
+        {tabs.map((tab) => (
           <button
-            key={t.key}
-            onClick={() => setActiveTab(t.key)}
+            key={tab.key}
+            onClick={() => setActiveTab(tab.key)}
             className={`px-4 py-2 text-sm font-medium border-b-2 transition-colors ${
-              activeTab === t.key
+              activeTab === tab.key
                 ? "border-blue-600 text-blue-600"
                 : "border-transparent text-gray-500 hover:text-gray-700"
             }`}
           >
-            {t.label}
+            {tab.label}
           </button>
         ))}
       </div>
@@ -136,41 +133,41 @@ export default function ProteinDetailPage({ params }: { params: Promise<{ access
           {/* Left: core info + coverage */}
           <div className="space-y-4">
             <div className="rounded-lg border bg-white p-4 shadow-sm space-y-3">
-              <p className="text-xs font-semibold uppercase tracking-wide text-gray-400">Identity</p>
+              <p className="text-xs font-semibold uppercase tracking-wide text-gray-400">{t("overviewTab.identity")}</p>
               <div className="space-y-2 text-sm">
                 {protein.gene_name && (
                   <div className="flex justify-between">
-                    <span className="text-gray-500">Gene</span>
+                    <span className="text-gray-500">{t("overviewTab.gene")}</span>
                     <span className="font-medium text-gray-900">{protein.gene_name}</span>
                   </div>
                 )}
                 {protein.organism && (
                   <div className="flex justify-between gap-4">
-                    <span className="text-gray-500 shrink-0">Organism</span>
+                    <span className="text-gray-500 shrink-0">{t("overviewTab.organism")}</span>
                     <span className="text-gray-800 text-right">{protein.organism}</span>
                   </div>
                 )}
                 {protein.taxonomy_id && (
                   <div className="flex justify-between">
-                    <span className="text-gray-500">Taxon ID</span>
+                    <span className="text-gray-500">{t("overviewTab.taxonId")}</span>
                     <span className="font-mono text-xs text-gray-700">{protein.taxonomy_id}</span>
                   </div>
                 )}
                 {protein.length && (
                   <div className="flex justify-between">
-                    <span className="text-gray-500">Length</span>
-                    <span className="text-gray-800">{protein.length.toLocaleString()} aa</span>
+                    <span className="text-gray-500">{t("overviewTab.length")}</span>
+                    <span className="text-gray-800">{protein.length.toLocaleString()} {t("overviewTab.aa")}</span>
                   </div>
                 )}
                 {protein.sequence_id && (
                   <div className="flex justify-between">
-                    <span className="text-gray-500">Sequence ID</span>
+                    <span className="text-gray-500">{t("overviewTab.sequenceId")}</span>
                     <span className="font-mono text-xs text-gray-600">{protein.sequence_id}</span>
                   </div>
                 )}
                 {!protein.is_canonical && (
                   <div className="flex justify-between">
-                    <span className="text-gray-500">Canonical</span>
+                    <span className="text-gray-500">{t("overviewTab.canonical")}</span>
                     <Link href={`/proteins/${protein.canonical_accession}`} className="font-mono text-xs text-blue-600 hover:underline">
                       {protein.canonical_accession}
                     </Link>
@@ -180,33 +177,33 @@ export default function ProteinDetailPage({ params }: { params: Promise<{ access
             </div>
 
             <div className="rounded-lg border bg-white p-4 shadow-sm space-y-3">
-              <p className="text-xs font-semibold uppercase tracking-wide text-gray-400">Coverage</p>
+              <p className="text-xs font-semibold uppercase tracking-wide text-gray-400">{t("overviewTab.coverage")}</p>
               <div className="space-y-2 text-sm">
                 <div className="flex justify-between">
-                  <span className="text-gray-500">Embeddings</span>
+                  <span className="text-gray-500">{t("overviewTab.embeddings")}</span>
                   <span className={protein.embedding_count > 0 ? "text-green-600 font-medium" : "text-gray-400"}>
-                    {protein.embedding_count > 0 ? `${protein.embedding_count} config${protein.embedding_count !== 1 ? "s" : ""}` : "none"}
+                    {protein.embedding_count > 0 ? `${protein.embedding_count} config${protein.embedding_count !== 1 ? "s" : ""}` : t("overviewTab.none")}
                   </span>
                 </div>
                 <div className="flex justify-between">
-                  <span className="text-gray-500">GO Annotations</span>
+                  <span className="text-gray-500">{t("overviewTab.goAnnotations")}</span>
                   <button
                     onClick={() => setActiveTab("annotations")}
                     className={protein.go_annotation_count > 0 ? "text-green-600 font-medium hover:underline" : "text-gray-400 cursor-default"}
                   >
-                    {protein.go_annotation_count > 0 ? protein.go_annotation_count.toLocaleString() : "none"}
+                    {protein.go_annotation_count > 0 ? protein.go_annotation_count.toLocaleString() : t("overviewTab.none")}
                   </button>
                 </div>
                 <div className="flex justify-between">
-                  <span className="text-gray-500">Metadata</span>
-                  <span className={meta ? "text-green-600 font-medium" : "text-gray-400"}>{meta ? "yes" : "none"}</span>
+                  <span className="text-gray-500">{t("overviewTab.metadata")}</span>
+                  <span className={meta ? "text-green-600 font-medium" : "text-gray-400"}>{meta ? t("overviewTab.yes") : t("overviewTab.none")}</span>
                 </div>
               </div>
             </div>
 
             {protein.isoforms.length > 0 && (
               <div className="rounded-lg border bg-white p-4 shadow-sm">
-                <p className="text-xs font-semibold uppercase tracking-wide text-gray-400 mb-3">Isoforms</p>
+                <p className="text-xs font-semibold uppercase tracking-wide text-gray-400 mb-3">{t("overviewTab.isoforms")}</p>
                 <div className="space-y-1">
                   {protein.isoforms.map((iso) => (
                     <Link key={iso} href={`/proteins/${iso}`} className="block font-mono text-xs text-blue-600 hover:underline">
@@ -224,27 +221,27 @@ export default function ProteinDetailPage({ params }: { params: Promise<{ access
               <>
                 {meta.function_cc && (
                   <div className="rounded-lg border bg-white p-4 shadow-sm">
-                    <p className="text-xs font-semibold uppercase tracking-wide text-gray-400 mb-2">Function</p>
+                    <p className="text-xs font-semibold uppercase tracking-wide text-gray-400 mb-2">{t("overviewTab.function")}</p>
                     <p className="text-sm text-gray-800 whitespace-pre-wrap">{meta.function_cc}</p>
                   </div>
                 )}
                 <div className="rounded-lg border bg-white p-4 shadow-sm space-y-4">
-                  <p className="text-xs font-semibold uppercase tracking-wide text-gray-400">Biochemistry</p>
-                  <Field label="EC Number" value={meta.ec_number} />
-                  <Field label="Catalytic Activity" value={meta.catalytic_activity} />
-                  <Field label="Cofactor" value={meta.cofactor} />
-                  <Field label="Activity Regulation" value={meta.activity_regulation} />
-                  <Field label="Pathway" value={meta.pathway} />
-                  <Field label="Absorption" value={meta.absorption} />
-                  <Field label="Kinetics" value={meta.kinetics} />
-                  <Field label="pH Dependence" value={meta.ph_dependence} />
-                  <Field label="Redox Potential" value={meta.redox_potential} />
-                  <Field label="Temperature Dependence" value={meta.temperature_dependence} />
-                  <Field label="Rhea ID" value={meta.rhea_id} />
+                  <p className="text-xs font-semibold uppercase tracking-wide text-gray-400">{t("overviewTab.biochemistry")}</p>
+                  <Field label={t("overviewTab.ecNumber")} value={meta.ec_number} />
+                  <Field label={t("overviewTab.catalyticActivity")} value={meta.catalytic_activity} />
+                  <Field label={t("overviewTab.cofactor")} value={meta.cofactor} />
+                  <Field label={t("overviewTab.activityRegulation")} value={meta.activity_regulation} />
+                  <Field label={t("overviewTab.pathway")} value={meta.pathway} />
+                  <Field label={t("overviewTab.absorption")} value={meta.absorption} />
+                  <Field label={t("overviewTab.kinetics")} value={meta.kinetics} />
+                  <Field label={t("overviewTab.phDependence")} value={meta.ph_dependence} />
+                  <Field label={t("overviewTab.redoxPotential")} value={meta.redox_potential} />
+                  <Field label={t("overviewTab.temperatureDependence")} value={meta.temperature_dependence} />
+                  <Field label={t("overviewTab.rheaId")} value={meta.rhea_id} />
                 </div>
                 {meta.keywords && (
                   <div className="rounded-lg border bg-white p-4 shadow-sm">
-                    <p className="text-xs font-semibold uppercase tracking-wide text-gray-400 mb-2">Keywords</p>
+                    <p className="text-xs font-semibold uppercase tracking-wide text-gray-400 mb-2">{t("overviewTab.keywords")}</p>
                     <div className="flex flex-wrap gap-1.5">
                       {meta.keywords.split(";").map((kw) => kw.trim()).filter(Boolean).map((kw) => (
                         <span key={kw} className="rounded bg-gray-100 px-2 py-0.5 text-xs text-gray-600">{kw}</span>
@@ -255,7 +252,7 @@ export default function ProteinDetailPage({ params }: { params: Promise<{ access
               </>
             ) : (
               <div className="rounded-lg border bg-white p-8 shadow-sm text-center text-sm text-gray-400">
-                No functional metadata available. Use the Fetch Metadata tab to import from UniProt.
+                {t("overviewTab.noFunctionalMetadata")}
               </div>
             )}
           </div>
@@ -269,7 +266,7 @@ export default function ProteinDetailPage({ params }: { params: Promise<{ access
 
           {!loadingAnnotations && annotationsLoaded && annotations.length === 0 && (
             <div className="rounded-lg border bg-white p-8 text-center text-sm text-gray-400">
-              No GO annotations found for this protein.
+              {t("overviewTab.noGoAnnotations")}
             </div>
           )}
 
@@ -298,9 +295,9 @@ export default function ProteinDetailPage({ params }: { params: Promise<{ access
                   }}
                   className="rounded-md border bg-white px-3 py-1.5 text-sm hover:bg-gray-50"
                 >
-                  {showGraph ? "Hide GO Graph" : "Show GO Graph"}
+                  {showGraph ? t("overviewTab.hideGoGraph") : t("overviewTab.showGoGraph")}
                 </button>
-                {loadingGraph && <span className="text-xs text-gray-400">Loading graph…</span>}
+                {loadingGraph && <span className="text-xs text-gray-400">{t("overviewTab.loadingGraph")}</span>}
               </div>
 
               {showGraph && subgraph && <GoGraph subgraph={subgraph} />}
@@ -309,9 +306,9 @@ export default function ProteinDetailPage({ params }: { params: Promise<{ access
               <div className="grid grid-cols-3 gap-3">
                 {(["F", "P", "C"] as const).map((asp) => (
                   <div key={asp} className="rounded-lg border bg-white p-4 shadow-sm">
-                    <p className="text-xs font-semibold uppercase tracking-wide text-gray-400">{ASPECT_LABELS[asp]}</p>
+                    <p className="text-xs font-semibold uppercase tracking-wide text-gray-400">{t(`overviewTab.${asp === "F" ? "molecularFunction" : asp === "P" ? "biologicalProcess" : "cellularComponent"}`)}</p>
                     <p className="mt-1 text-2xl font-bold text-gray-900">{byAspect[asp].length}</p>
-                    <p className="text-xs text-gray-400 mt-0.5">annotations</p>
+                    <p className="text-xs text-gray-400 mt-0.5">{t("overviewTab.annotations")}</p>
                   </div>
                 ))}
               </div>
@@ -323,16 +320,16 @@ export default function ProteinDetailPage({ params }: { params: Promise<{ access
                 return (
                   <div key={asp}>
                     <p className="text-sm font-semibold text-gray-700 mb-3">
-                      {ASPECT_LABELS[asp]}
+                      {t(`overviewTab.${asp === "F" ? "molecularFunction" : asp === "P" ? "biologicalProcess" : "cellularComponent"}`)}
                       <span className="ml-2 text-xs font-normal text-gray-400">{terms.length} term{terms.length !== 1 ? "s" : ""}</span>
                     </p>
-                    <div className="overflow-hidden rounded-lg border bg-white shadow-sm">
+                    <div className="overflow-x-auto rounded-lg border bg-white shadow-sm">
                       <div className="grid grid-cols-[100px_1fr_80px_100px_100px] gap-2 border-b bg-gray-50 px-4 py-2 text-xs font-semibold uppercase tracking-wide text-gray-500">
-                        <div>GO ID</div>
-                        <div>Name</div>
-                        <div>Evidence</div>
-                        <div>Qualifier</div>
-                        <div>Source</div>
+                        <div>{t("overviewTab.goTableHeaders.goId")}</div>
+                        <div>{t("overviewTab.goTableHeaders.name")}</div>
+                        <div>{t("overviewTab.goTableHeaders.evidence")}</div>
+                        <div>{t("overviewTab.goTableHeaders.qualifier")}</div>
+                        <div>{t("overviewTab.goTableHeaders.source")}</div>
                       </div>
                       {terms.map((ann, i) => (
                         <div key={i} className="grid grid-cols-[100px_1fr_80px_100px_100px] gap-2 border-b px-4 py-2.5 text-sm last:border-0 items-center">
diff --git a/apps/web/app/proteins/page.tsx b/apps/web/app/[locale]/proteins/page.tsx
similarity index 74%
rename from apps/web/app/proteins/page.tsx
rename to apps/web/app/[locale]/proteins/page.tsx
index b4c5527..4360894 100644
--- a/apps/web/app/proteins/page.tsx
+++ b/apps/web/app/[locale]/proteins/page.tsx
@@ -4,6 +4,7 @@ import { useEffect, useState, useCallback } from "react";
 import Link from "next/link";
 import { useToast } from "@/components/Toast";
 import { SkeletonTableRow } from "@/components/Skeleton";
+import { useTranslations } from "next-intl";
 import {
   getProteinStats,
   listProteins,
@@ -19,10 +20,11 @@ const labelClass = "block text-sm font-medium text-gray-700 mb-1";
 const PAGE_SIZE = 50;
 
 function ReviewedBadge({ reviewed }: { reviewed?: boolean | null }) {
+  const t = useTranslations("proteins");
   if (reviewed === true)
-    return <span className="rounded-full bg-blue-50 px-2 py-0.5 text-xs font-medium text-blue-700 border border-blue-100">Swiss-Prot</span>;
+    return <span className="rounded-full bg-blue-50 px-2 py-0.5 text-xs font-medium text-blue-700 border border-blue-100">{t("sourceSwissProt")}</span>;
   if (reviewed === false)
-    return <span className="rounded-full bg-gray-50 px-2 py-0.5 text-xs font-medium text-gray-500 border border-gray-200">TrEMBL</span>;
+    return <span className="rounded-full bg-gray-50 px-2 py-0.5 text-xs font-medium text-gray-500 border border-gray-200">{t("sourceTrembl")}</span>;
   return <span className="text-gray-300 text-xs">—</span>;
 }
 
@@ -37,6 +39,7 @@ function StatCard({ label, value, sub }: { label: string; value: number; sub?: s
 }
 
 export default function ProteinsPage() {
+  const t = useTranslations("proteins");
   const toast = useToast();
   const [activeTab, setActiveTab] = useState<Tab>("browse");
 
@@ -155,30 +158,30 @@ export default function ProteinsPage() {
   const currentPage = Math.floor(offset / PAGE_SIZE) + 1;
 
   const tabs: { key: Tab; label: string }[] = [
-    { key: "browse", label: "Browse" },
-    { key: "stats", label: "Stats" },
-    { key: "insert", label: "Insert Proteins" },
-    { key: "metadata", label: "Fetch Metadata" },
+    { key: "browse", label: t("tabs.browse") },
+    { key: "stats", label: t("tabs.stats") },
+    { key: "insert", label: t("tabs.insert") },
+    { key: "metadata", label: t("tabs.metadata") },
   ];
 
   return (
     <>
       <div className="flex items-center justify-between mb-4">
-        <h1 className="text-xl font-semibold">Proteins</h1>
+        <h1 className="text-xl font-semibold">{t("title")}</h1>
       </div>
 
-      <div className="flex gap-1 border-b mb-6">
-        {tabs.map((t) => (
+      <div className="flex gap-1 border-b mb-6 overflow-x-auto">
+        {tabs.map((tab) => (
           <button
-            key={t.key}
-            onClick={() => setActiveTab(t.key)}
+            key={tab.key}
+            onClick={() => setActiveTab(tab.key)}
             className={`px-4 py-2 text-sm font-medium border-b-2 transition-colors ${
-              activeTab === t.key
+              activeTab === tab.key
                 ? "border-blue-600 text-blue-600"
                 : "border-transparent text-gray-500 hover:text-gray-700"
             }`}
           >
-            {t.label}
+            {tab.label}
           </button>
         ))}
       </div>
@@ -193,11 +196,11 @@ export default function ProteinsPage() {
                 type="text"
                 value={searchInput}
                 onChange={(e) => setSearchInput(e.target.value)}
-                placeholder="accession, gene, organism…"
-                className="rounded-md border px-3 py-1.5 text-sm focus:outline-none focus:ring-2 focus:ring-blue-500 w-64"
+                placeholder={t("browseTab.searchPlaceholder")}
+                className="rounded-md border px-3 py-1.5 text-sm focus:outline-none focus:ring-2 focus:ring-blue-500 w-full sm:w-64"
               />
               <button type="submit" className="rounded-md border bg-white px-3 py-1.5 text-sm hover:bg-gray-50">
-                Search
+                {t("browseTab.search")}
               </button>
               {search && (
                 <button
@@ -205,7 +208,7 @@ export default function ProteinsPage() {
                   onClick={() => { setSearchInput(""); setSearch(""); loadProteins(0, "", reviewedFilter, canonicalOnly); }}
                   className="rounded-md border bg-white px-3 py-1.5 text-sm hover:bg-gray-50 text-gray-500"
                 >
-                  Clear
+                  {t("browseTab.clear")}
                 </button>
               )}
             </form>
@@ -215,9 +218,9 @@ export default function ProteinsPage() {
               onChange={(e) => handleFilterChange(e.target.value as typeof reviewedFilter, canonicalOnly)}
               className="rounded-md border bg-white px-3 py-1.5 text-sm focus:outline-none"
             >
-              <option value="all">All proteins</option>
-              <option value="reviewed">Swiss-Prot only</option>
-              <option value="unreviewed">TrEMBL only</option>
+              <option value="all">{t("browseTab.allProteins")}</option>
+              <option value="reviewed">{t("browseTab.swissProt")}</option>
+              <option value="unreviewed">{t("browseTab.trembl")}</option>
             </select>
 
             <label className="flex items-center gap-1.5 text-sm text-gray-600 cursor-pointer select-none">
@@ -227,28 +230,28 @@ export default function ProteinsPage() {
                 onChange={(e) => handleFilterChange(reviewedFilter, e.target.checked)}
                 className="rounded"
               />
-              Canonical only
+              {t("browseTab.canonicalOnly")}
             </label>
 
-            <span className="ml-auto text-sm text-gray-400">{total.toLocaleString()} proteins</span>
+            <span className="ml-auto text-sm text-gray-400">{t("browseTab.totalProteins", { count: total.toLocaleString() })}</span>
           </div>
 
           {/* Table */}
-          <div className="overflow-hidden rounded-lg border bg-white shadow-sm">
+          <div className="overflow-x-auto rounded-lg border bg-white shadow-sm">
             <div className="grid grid-cols-[130px_140px_120px_1fr_80px_110px] gap-2 border-b bg-gray-50 px-4 py-2.5 text-xs font-semibold uppercase tracking-wide text-gray-500">
-              <div>Accession</div>
-              <div>Entry Name</div>
-              <div>Gene</div>
-              <div>Organism</div>
-              <div>Length</div>
-              <div>Source</div>
+              <div>{t("browseTab.tableHeaders.accession")}</div>
+              <div>{t("browseTab.tableHeaders.entryName")}</div>
+              <div>{t("browseTab.tableHeaders.gene")}</div>
+              <div>{t("browseTab.tableHeaders.organism")}</div>
+              <div>{t("browseTab.tableHeaders.length")}</div>
+              <div>{t("browseTab.tableHeaders.source")}</div>
             </div>
 
             {loadingBrowse && Array.from({ length: 8 }).map((_, i) => <SkeletonTableRow key={i} cols={6} />)}
 
             {!loadingBrowse && proteins.length === 0 && (
               <div className="px-4 py-12 text-center text-sm text-gray-400">
-                No proteins found. Use the Insert Proteins tab to import from UniProt.
+                {t("browseTab.noProteinsCta")}
               </div>
             )}
 
@@ -271,21 +274,21 @@ export default function ProteinsPage() {
           {/* Pagination */}
           {totalPages > 1 && (
             <div className="mt-4 flex items-center justify-between text-sm text-gray-500">
-              <span>Page {currentPage} of {totalPages}</span>
+              <span>{t("browseTab.pagination.page", { current: currentPage, total: totalPages })}</span>
               <div className="flex gap-2">
                 <button
                   onClick={() => loadProteins(offset - PAGE_SIZE)}
                   disabled={offset === 0}
                   className="rounded-md border bg-white px-3 py-1.5 hover:bg-gray-50 disabled:opacity-40"
                 >
-                  Previous
+                  {t("browseTab.pagination.previous")}
                 </button>
                 <button
                   onClick={() => loadProteins(offset + PAGE_SIZE)}
                   disabled={offset + PAGE_SIZE >= total}
                   className="rounded-md border bg-white px-3 py-1.5 hover:bg-gray-50 disabled:opacity-40"
                 >
-                  Next
+                  {t("browseTab.pagination.next")}
                 </button>
               </div>
             </div>
@@ -298,38 +301,38 @@ export default function ProteinsPage() {
         <div>
           <div className="flex justify-end mb-4">
             <button onClick={loadStats} className="rounded-md border bg-white px-3 py-1.5 text-sm hover:bg-gray-50">
-              Refresh
+              {t("statsTab.refresh")}
             </button>
           </div>
-          {loadingStats && <p className="text-sm text-gray-400">Loading…</p>}
+          {loadingStats && <p className="text-sm text-gray-400">{t("statsTab.loading")}</p>}
           {stats && (
             <div className="space-y-6">
               <div>
-                <p className="text-xs font-semibold uppercase tracking-wide text-gray-400 mb-3">Overview</p>
+                <p className="text-xs font-semibold uppercase tracking-wide text-gray-400 mb-3">{t("statsTab.overview")}</p>
                 <div className="grid grid-cols-2 sm:grid-cols-4 gap-3">
-                  <StatCard label="Total Proteins" value={stats.total} />
-                  <StatCard label="Canonical" value={stats.canonical} sub={`${stats.isoforms.toLocaleString()} isoforms`} />
-                  <StatCard label="Swiss-Prot" value={stats.reviewed} sub="reviewed" />
-                  <StatCard label="TrEMBL" value={stats.unreviewed} sub="unreviewed" />
+                  <StatCard label={t("statsTab.totalProteins")} value={stats.total} />
+                  <StatCard label={t("statsTab.canonical")} value={stats.canonical} sub={t("statsTab.isoforms", { count: stats.isoforms.toLocaleString() })} />
+                  <StatCard label={t("statsTab.reviewed")} value={stats.reviewed} sub={t("statsTab.reviewedSub")} />
+                  <StatCard label={t("statsTab.unreviewed")} value={stats.unreviewed} sub={t("statsTab.unreviewedSub")} />
                 </div>
               </div>
               <div>
-                <p className="text-xs font-semibold uppercase tracking-wide text-gray-400 mb-3">Coverage</p>
+                <p className="text-xs font-semibold uppercase tracking-wide text-gray-400 mb-3">{t("statsTab.coverage")}</p>
                 <div className="grid grid-cols-2 sm:grid-cols-3 gap-3">
                   <StatCard
-                    label="With Metadata"
+                    label={t("statsTab.withMetadata")}
                     value={stats.with_metadata}
-                    sub={stats.canonical > 0 ? `${Math.round((stats.with_metadata / stats.canonical) * 100)}% of canonical` : undefined}
+                    sub={stats.canonical > 0 ? t("statsTab.metadataSub", { percent: Math.round((stats.with_metadata / stats.canonical) * 100) }) : undefined}
                   />
                   <StatCard
-                    label="With Embeddings"
+                    label={t("statsTab.withEmbeddings")}
                     value={stats.with_embeddings}
-                    sub={stats.total > 0 ? `${Math.round((stats.with_embeddings / stats.total) * 100)}% of total` : undefined}
+                    sub={stats.total > 0 ? t("statsTab.embeddingsSub", { percent: Math.round((stats.with_embeddings / stats.total) * 100) }) : undefined}
                   />
                   <StatCard
-                    label="With GO Annotations"
+                    label={t("statsTab.withGoAnnotations")}
                     value={stats.with_go_annotations}
-                    sub={stats.total > 0 ? `${Math.round((stats.with_go_annotations / stats.total) * 100)}% of total` : undefined}
+                    sub={stats.total > 0 ? t("statsTab.goAnnotationsSub", { percent: Math.round((stats.with_go_annotations / stats.total) * 100) }) : undefined}
                   />
                 </div>
               </div>
@@ -340,39 +343,39 @@ export default function ProteinsPage() {
 
       {/* ── Insert Proteins ── */}
       {activeTab === "insert" && (
-        <div className="max-w-lg">
+        <div className="max-w-2xl">
           <div className="rounded-lg border bg-white p-6 shadow-sm">
-            <h2 className="text-base font-semibold mb-1">Insert Proteins from UniProt</h2>
-            <p className="text-sm text-gray-500 mb-4">Downloads FASTA sequences and upserts Protein + Sequence rows.</p>
+            <h2 className="text-base font-semibold mb-1">{t("insertTab.title")}</h2>
+            <p className="text-sm text-gray-500 mb-4">{t("insertTab.description")}</p>
             <form onSubmit={handleInsertSubmit} className="space-y-4">
               <div>
-                <label className={labelClass}>Search criteria</label>
+                <label className={labelClass}>{t("insertTab.searchCriteriaLabel")}</label>
                 <input type="text" value={searchCriteria} onChange={(e) => setSearchCriteria(e.target.value)} required className={inputClass} placeholder="organism_id:9606 AND reviewed:true" />
-                <p className="mt-1 text-xs text-gray-400">UniProt query — <code>reviewed:true</code> = Swiss-Prot only</p>
+                <p className="mt-1 text-xs text-gray-400">{t("insertTab.searchCriteriaHelper")}</p>
               </div>
               <div className="grid grid-cols-2 gap-3">
                 <div>
-                  <label className={labelClass}>Page size</label>
+                  <label className={labelClass}>{t("insertTab.pageSizeLabel")}</label>
                   <input type="number" value={pageSize} onChange={(e) => setPageSize(parseInt(e.target.value, 10))} min={1} className={inputClass} />
                 </div>
                 <div>
-                  <label className={labelClass}>Total limit <span className="font-normal text-gray-400">(optional)</span></label>
+                  <label className={labelClass}>{t("insertTab.totalLimitLabel")} <span className="font-normal text-gray-400">{t("insertTab.totalLimitOptional")}</span></label>
                   <input type="number" value={totalLimit} onChange={(e) => setTotalLimit(e.target.value)} placeholder="all" className={inputClass} />
                 </div>
               </div>
               <label className="flex items-center gap-2 text-sm cursor-pointer">
                 <input type="checkbox" checked={includeIsoforms} onChange={(e) => setIncludeIsoforms(e.target.checked)} className="rounded" />
-                Include isoforms
+                {t("insertTab.includeIsoforms")}
               </label>
               {insertResult && (
                 <div className="rounded-md border border-green-200 bg-green-50 px-3 py-2 text-sm text-green-700">
-                  Job queued:{" "}
+                  {t("insertTab.jobQueuedPrefix")}
                   <Link href={`/jobs/${insertResult.id}`} className="font-mono underline hover:text-green-900">{insertResult.id}</Link>
                 </div>
               )}
               <div className="flex justify-end">
                 <button type="submit" disabled={insertSubmitting} className="rounded-md bg-blue-600 px-4 py-2 text-sm text-white hover:bg-blue-700 disabled:opacity-50">
-                  {insertSubmitting ? "Launching…" : "Launch Job"}
+                  {insertSubmitting ? t("insertTab.launching") : t("insertTab.launchJob")}
                 </button>
               </div>
             </form>
@@ -382,35 +385,35 @@ export default function ProteinsPage() {
 
       {/* ── Fetch Metadata ── */}
       {activeTab === "metadata" && (
-        <div className="max-w-lg">
+        <div className="max-w-2xl">
           <div className="rounded-lg border bg-white p-6 shadow-sm">
-            <h2 className="text-base font-semibold mb-1">Fetch UniProt Metadata</h2>
-            <p className="text-sm text-gray-500 mb-4">Downloads TSV annotations and upserts ProteinUniProtMetadata rows.</p>
+            <h2 className="text-base font-semibold mb-1">{t("metadataTab.title")}</h2>
+            <p className="text-sm text-gray-500 mb-4">{t("metadataTab.description")}</p>
             <form onSubmit={handleMetaSubmit} className="space-y-4">
               <div>
-                <label className={labelClass}>Search criteria</label>
+                <label className={labelClass}>{t("metadataTab.searchCriteriaLabel")}</label>
                 <input type="text" value={metaCriteria} onChange={(e) => setMetaCriteria(e.target.value)} required className={inputClass} placeholder="organism_id:9606 AND reviewed:true" />
-                <p className="mt-1 text-xs text-gray-400">UniProt query — <code>reviewed:true</code> = Swiss-Prot only</p>
+                <p className="mt-1 text-xs text-gray-400">{t("metadataTab.searchCriteriaHelper")}</p>
               </div>
               <div className="grid grid-cols-2 gap-3">
                 <div>
-                  <label className={labelClass}>Page size</label>
+                  <label className={labelClass}>{t("metadataTab.pageSizeLabel")}</label>
                   <input type="number" value={metaPageSize} onChange={(e) => setMetaPageSize(parseInt(e.target.value, 10))} min={1} className={inputClass} />
                 </div>
                 <div>
-                  <label className={labelClass}>Total limit <span className="font-normal text-gray-400">(optional)</span></label>
+                  <label className={labelClass}>{t("metadataTab.totalLimitLabel")} <span className="font-normal text-gray-400">{t("metadataTab.totalLimitOptional")}</span></label>
                   <input type="number" value={metaLimit} onChange={(e) => setMetaLimit(e.target.value)} placeholder="all" className={inputClass} />
                 </div>
               </div>
               {metaResult && (
                 <div className="rounded-md border border-green-200 bg-green-50 px-3 py-2 text-sm text-green-700">
-                  Job queued:{" "}
+                  {t("insertTab.jobQueuedPrefix")}
                   <Link href={`/jobs/${metaResult.id}`} className="font-mono underline hover:text-green-900">{metaResult.id}</Link>
                 </div>
               )}
               <div className="flex justify-end">
                 <button type="submit" disabled={metaSubmitting} className="rounded-md bg-blue-600 px-4 py-2 text-sm text-white hover:bg-blue-700 disabled:opacity-50">
-                  {metaSubmitting ? "Launching…" : "Launch Job"}
+                  {metaSubmitting ? t("metadataTab.launching") : t("metadataTab.launchJob")}
                 </button>
               </div>
             </form>
diff --git a/apps/web/app/query-sets/page.tsx b/apps/web/app/[locale]/query-sets/page.tsx
similarity index 85%
rename from apps/web/app/query-sets/page.tsx
rename to apps/web/app/[locale]/query-sets/page.tsx
index 29d6c3a..fc1a63b 100644
--- a/apps/web/app/query-sets/page.tsx
+++ b/apps/web/app/[locale]/query-sets/page.tsx
@@ -4,6 +4,7 @@ import { useEffect, useRef, useState } from "react";
 import { createQuerySet, deleteQuerySet, listQuerySets, QuerySet } from "@/lib/api";
 import { SkeletonTableRow } from "@/components/Skeleton";
 import { useToast } from "@/components/Toast";
+import { useTranslations } from "next-intl";
 
 function formatDate(iso?: string | null) {
   if (!iso) return "—";
@@ -11,6 +12,7 @@ function formatDate(iso?: string | null) {
 }
 
 export default function QuerySetsPage() {
+  const t = useTranslations("querySets");
   const toast = useToast();
   const [sets, setSets] = useState<QuerySet[]>([]);
   const [loading, setLoading] = useState(true);
@@ -71,7 +73,7 @@ export default function QuerySetsPage() {
   }
 
   async function handleDelete(id: string, name: string) {
-    if (!confirm(`Delete query set "${name}"? This cannot be undone.`)) return;
+    if (!confirm(t("deleteConfirm", { name }))) return;
     try {
       await deleteQuerySet(id);
       setSets((prev) => prev.filter((s) => s.id !== id));
@@ -98,12 +100,12 @@ export default function QuerySetsPage() {
   return (
     <>
       <div className="flex items-center justify-between mb-4">
-        <h1 className="text-xl font-semibold">Query Sets</h1>
+        <h1 className="text-xl font-semibold">{t("title")}</h1>
         <button
           onClick={openModal}
           className="rounded-md bg-blue-600 px-3 py-1.5 text-sm text-white hover:bg-blue-700"
         >
-          + Upload FASTA
+          {t("uploadFasta")}
         </button>
       </div>
 
@@ -114,11 +116,11 @@ export default function QuerySetsPage() {
       )}
 
       {/* List */}
-      <div className="overflow-hidden rounded-lg border bg-white shadow-sm">
+      <div className="overflow-x-auto rounded-lg border bg-white shadow-sm">
         <div className="grid grid-cols-[1fr_100px_160px_80px] gap-2 border-b bg-gray-50 px-4 py-2.5 text-xs font-semibold uppercase tracking-wide text-gray-500">
-          <div>Name</div>
-          <div>Sequences</div>
-          <div>Created</div>
+          <div>{t("tableHeaders.name")}</div>
+          <div>{t("tableHeaders.sequences")}</div>
+          <div>{t("tableHeaders.created")}</div>
           <div></div>
         </div>
 
@@ -128,12 +130,12 @@ export default function QuerySetsPage() {
 
         {!loading && sets.length === 0 && (
           <div className="px-4 py-10 text-center">
-            <p className="text-sm text-gray-400 mb-2">No query sets yet.</p>
+            <p className="text-sm text-gray-400 mb-2">{t("noQuerySets")}</p>
             <button
               onClick={openModal}
               className="text-sm text-blue-600 underline"
             >
-              Upload a FASTA file to get started
+              {t("uploadFastaCta")}
             </button>
           </div>
         )}
@@ -158,7 +160,7 @@ export default function QuerySetsPage() {
                   onClick={(e) => { e.stopPropagation(); handleDelete(qs.id, qs.name); }}
                   className="rounded border border-red-200 px-2 py-1 text-xs text-red-600 hover:bg-red-50 transition-colors"
                 >
-                  Delete
+                  {t("delete")}
                 </button>
               </div>
             </div>
@@ -166,7 +168,7 @@ export default function QuerySetsPage() {
             {expandedId === qs.id && qs.entries && (
               <div className="border-t bg-gray-50 px-6 py-3">
                 <p className="text-xs font-semibold uppercase tracking-wide text-gray-500 mb-2">
-                  Entries ({qs.entries.length})
+                  {t("expandedEntries.title")} {t("expandedEntries.count", { count: qs.entries.length })}
                 </p>
                 <div className="max-h-48 overflow-y-auto space-y-1">
                   {qs.entries.map((entry) => (
@@ -187,44 +189,44 @@ export default function QuerySetsPage() {
         <div className="fixed inset-0 z-50 flex items-center justify-center bg-black/40 p-4">
           <div className="w-full max-w-md rounded-xl border bg-white shadow-xl flex flex-col max-h-[90vh]">
             <div className="flex items-center justify-between border-b px-5 py-4">
-              <h2 className="text-base font-semibold">Upload FASTA</h2>
+              <h2 className="text-base font-semibold">{t("uploadModal.title")}</h2>
               <button
                 onClick={() => setShowModal(false)}
                 className="text-gray-400 hover:text-gray-600 text-xl leading-none"
               >
-                ×
+                {t("uploadModal.close")}
               </button>
             </div>
 
             <form onSubmit={handleUpload} className="flex-1 overflow-y-auto p-5 space-y-4">
               <div>
                 <label className={labelClass}>
-                  Name <span className="text-red-500">*</span>
+                  {t("uploadModal.nameLabel")} <span className="text-red-500">{t("uploadModal.nameRequired")}</span>
                 </label>
                 <input
                   type="text"
                   value={uploadName}
                   onChange={(e) => setUploadName(e.target.value)}
-                  placeholder="e.g. human_novel_proteins"
+                  placeholder={t("uploadModal.namePlaceholder")}
                   required
                   className={inputClass}
                 />
               </div>
 
               <div>
-                <label className={labelClass}>Description (optional)</label>
+                <label className={labelClass}>{t("uploadModal.descriptionLabel")}</label>
                 <input
                   type="text"
                   value={uploadDescription}
                   onChange={(e) => setUploadDescription(e.target.value)}
-                  placeholder="Short description"
+                  placeholder={t("uploadModal.descriptionPlaceholder")}
                   className={inputClass}
                 />
               </div>
 
               <div>
                 <label className={labelClass}>
-                  FASTA file <span className="text-red-500">*</span>
+                  {t("uploadModal.fastaFileLabel")} <span className="text-red-500">{t("uploadModal.fastaFileRequired")}</span>
                 </label>
 
                 {/* Drag & drop zone */}
@@ -243,17 +245,17 @@ export default function QuerySetsPage() {
                 >
                   {uploadFile ? (
                     <>
-                      <span className="text-lg">✓</span>
+                      <span className="text-lg">{t("uploadModal.fileSelected")}</span>
                       <span className="font-medium">{uploadFile.name}</span>
                       <span className="text-xs opacity-70">
-                        {(uploadFile.size / 1024).toFixed(1)} KB — click to change
+                        {(uploadFile.size / 1024).toFixed(1)} KB — {t("uploadModal.clickToChange")}
                       </span>
                     </>
                   ) : (
                     <>
                       <span className="text-2xl">↑</span>
-                      <span>Drop FASTA here or <span className="underline">browse</span></span>
-                      <span className="text-xs opacity-60">.fasta · .fa · .faa · .txt</span>
+                      <span>{t("uploadModal.dragDrop")}</span>
+                      <span className="text-xs opacity-60">{t("uploadModal.supportedFormats")}</span>
                     </>
                   )}
                 </div>
@@ -282,14 +284,14 @@ export default function QuerySetsPage() {
                   onClick={() => setShowModal(false)}
                   className="rounded-md border px-4 py-2 text-sm hover:bg-gray-50"
                 >
-                  Cancel
+                  {t("uploadModal.cancel")}
                 </button>
                 <button
                   type="submit"
                   disabled={uploading}
                   className="rounded-md bg-blue-600 px-4 py-2 text-sm text-white hover:bg-blue-700 disabled:opacity-50"
                 >
-                  {uploading ? "Uploading…" : "Upload"}
+                  {uploading ? t("uploadModal.uploading") : t("uploadModal.upload")}
                 </button>
               </div>
             </form>
diff --git a/apps/web/app/[locale]/scoring/page.tsx b/apps/web/app/[locale]/scoring/page.tsx
new file mode 100644
index 0000000..a0ce6a6
--- /dev/null
+++ b/apps/web/app/[locale]/scoring/page.tsx
@@ -0,0 +1,693 @@
+"use client";
+
+/**
+ * Scoring Configs management page.
+ *
+ * Allows users to:
+ *   - View all stored ScoringConfigs with their signal and evidence weights.
+ *   - Load the four built-in preset configs in one click.
+ *   - Create new configs by tuning signal sliders, formula, and optionally
+ *     overriding per-evidence-code quality weights.
+ *   - Delete configs that are no longer needed.
+ *
+ * The scoring system has two independent layers of configuration:
+ *
+ *   1. **Signal weights** — how much each composite signal contributes to
+ *      the weighted average (embedding similarity, NW/SW identity, evidence
+ *      quality signal, taxonomic proximity).
+ *
+ *   2. **Evidence-code weights** — per-GO-evidence-code quality multipliers
+ *      that define what each code tier is worth.  The system ships with
+ *      sensible defaults (EXP/IDA → 1.0, ISS/IBA → 0.7, IEA → 0.3 …).
+ *      Leaving the override null means "use the system defaults".
+ */
+
+import { useEffect, useState } from "react";
+import { useTranslations } from "next-intl";
+import { useToast } from "@/components/Toast";
+import {
+  listScoringConfigs,
+  createScoringConfig,
+  deleteScoringConfig,
+  createPresetScoringConfigs,
+  ScoringConfig,
+} from "@/lib/api";
+
+type Signal = { key: string; label: string; hint: string };
+type EvidenceGroup = { label: string; description: string; codes: { code: string; label: string }[] };
+
+const DEFAULT_SIGNAL_WEIGHTS: Record<string, number> = {
+  embedding_similarity: 1.0,
+  identity_nw: 0.0,
+  identity_sw: 0.0,
+  evidence_weight: 0.0,
+  taxonomic_proximity: 0.0,
+};
+
+/** System defaults — matches DEFAULT_EVIDENCE_WEIGHTS in scoring_config.py. */
+const SYSTEM_EVIDENCE_DEFAULTS: Record<string, number> = {
+  EXP: 1.0, IDA: 1.0, IPI: 1.0, IMP: 1.0, IGI: 1.0, IEP: 1.0,
+  HTP: 1.0, HDA: 1.0, HMP: 1.0, HGI: 1.0, HEP: 1.0, IC: 1.0, TAS: 1.0,
+  ISS: 0.7, ISO: 0.7, ISA: 0.7, ISM: 0.7, IGC: 0.7,
+  IBA: 0.7, IBD: 0.7, IKR: 0.7, IRD: 0.7, RCA: 0.7,
+  NAS: 0.5,
+  IEA: 0.3,
+  ND: 0.1,
+};
+
+// ── Helpers ───────────────────────────────────────────────────────────────────
+
+function allCodes(groups: EvidenceGroup[]): string[] {
+  return groups.flatMap((g) => g.codes.map((c) => c.code));
+}
+
+// ── WeightBar ─────────────────────────────────────────────────────────────────
+
+function WeightBar({
+  label,
+  value,
+  hint,
+}: {
+  label: string;
+  value: number;
+  hint: string;
+}) {
+  return (
+    <div className="flex items-center gap-2" title={hint}>
+      <span className="text-xs text-gray-500 w-40 shrink-0">{label}</span>
+      <div className="flex-1 h-1.5 rounded-full bg-gray-100 overflow-hidden">
+        <div
+          className="h-1.5 rounded-full bg-blue-400 transition-all"
+          style={{ width: `${value * 100}%` }}
+        />
+      </div>
+      <span
+        className={`font-mono text-xs w-8 text-right ${
+          value > 0 ? "text-gray-700" : "text-gray-300"
+        }`}
+      >
+        {value.toFixed(2)}
+      </span>
+    </div>
+  );
+}
+
+// ── ConfigCard ────────────────────────────────────────────────────────────────
+
+function ConfigCard({
+  config,
+  signals,
+  evidenceGroups,
+  onDelete,
+}: {
+  config: ScoringConfig;
+  signals: Signal[];
+  evidenceGroups: EvidenceGroup[];
+  onDelete: (id: string) => void;
+}) {
+  const t = useTranslations("scoring");
+  const [deleting, setDeleting] = useState(false);
+  const [showEvidenceWeights, setShowEvidenceWeights] = useState(false);
+
+  async function handleDelete() {
+    if (!confirm(t("configCard.deleteConfirm", { name: config.name }))) return;
+    setDeleting(true);
+    try {
+      await deleteScoringConfig(config.id);
+      onDelete(config.id);
+    } catch {
+      setDeleting(false);
+    }
+  }
+
+  const hasCustomEvidence = config.evidence_weights != null;
+
+  return (
+    <div className="rounded-lg border bg-white p-4 shadow-sm">
+      {/* Header */}
+      <div className="flex items-start justify-between gap-3 mb-3">
+        <div>
+          <span className="font-semibold text-gray-900">{config.name}</span>
+          <span className="ml-2 rounded bg-blue-50 border border-blue-100 px-1.5 py-0.5 text-xs font-mono text-blue-700">
+            {config.formula}
+          </span>
+          {hasCustomEvidence && (
+            <span className="ml-1.5 rounded bg-amber-50 border border-amber-100 px-1.5 py-0.5 text-xs text-amber-700">
+              {t("configCard.customEvidenceWeights")}
+            </span>
+          )}
+          {config.description && (
+            <p className="mt-0.5 text-xs text-gray-400">{config.description}</p>
+          )}
+        </div>
+        <button
+          onClick={handleDelete}
+          disabled={deleting}
+          className="text-xs text-red-400 hover:text-red-600 disabled:opacity-40 shrink-0"
+        >
+          {deleting ? "…" : t("configCard.delete")}
+        </button>
+      </div>
+
+      {/* Signal weights */}
+      <div className="space-y-1.5">
+        {signals.map(({ key, label, hint }) => (
+          <WeightBar key={key} label={label} value={config.weights[key] ?? 0} hint={hint} />
+        ))}
+      </div>
+
+      {/* Evidence weights */}
+      <div className="mt-3 border-t pt-2.5">
+        <button
+          onClick={() => setShowEvidenceWeights((v) => !v)}
+          className="text-xs text-gray-400 hover:text-gray-600"
+        >
+          {showEvidenceWeights ? t("configCard.collapse") : t("configCard.expand")} {t("configCard.evidenceCodeWeights")}{" "}
+          {hasCustomEvidence ? t("configCard.custom") : t("configCard.systemDefaults")}
+        </button>
+
+        {showEvidenceWeights && (
+          <div className="mt-3 space-y-4">
+            {evidenceGroups.map((group) => (
+              <div key={group.label}>
+                <p className="text-xs font-semibold text-gray-500 mb-1.5">
+                  {group.label}
+                </p>
+                <div className="space-y-1">
+                  {group.codes.map(({ code, label }) => {
+                    const val =
+                      config.evidence_weights?.[code] ??
+                      SYSTEM_EVIDENCE_DEFAULTS[code] ??
+                      0.5;
+                    const isOverridden = config.evidence_weights?.[code] != null;
+                    return (
+                      <div key={code} className="flex items-center gap-2">
+                        <span
+                          className={`font-mono text-xs w-10 shrink-0 ${
+                            isOverridden ? "text-amber-700 font-semibold" : "text-gray-500"
+                          }`}
+                        >
+                          {code}
+                        </span>
+                        <span className="text-xs text-gray-400 w-52 shrink-0 truncate" title={label}>
+                          {label}
+                        </span>
+                        <div className="flex-1 h-1.5 rounded-full bg-gray-100 overflow-hidden">
+                          <div
+                            className={`h-1.5 rounded-full transition-all ${
+                              isOverridden ? "bg-amber-400" : "bg-gray-300"
+                            }`}
+                            style={{ width: `${val * 100}%` }}
+                          />
+                        </div>
+                        <span className="font-mono text-xs w-8 text-right text-gray-600">
+                          {val.toFixed(2)}
+                        </span>
+                      </div>
+                    );
+                  })}
+                </div>
+              </div>
+            ))}
+          </div>
+        )}
+      </div>
+
+      <p className="mt-2 text-xs text-gray-300">
+        Created {new Date(config.created_at).toLocaleDateString()}
+      </p>
+    </div>
+  );
+}
+
+// ── NewConfigForm ─────────────────────────────────────────────────────────────
+
+function NewConfigForm({
+  signals,
+  evidenceGroups,
+  onCreated,
+}: {
+  signals: Signal[];
+  evidenceGroups: EvidenceGroup[];
+  onCreated: (c: ScoringConfig) => void;
+}) {
+  const t = useTranslations("scoring");
+  const [open, setOpen] = useState(false);
+  const [name, setName] = useState("");
+  const [formula, setFormula] = useState("linear");
+  const [weights, setWeights] = useState<Record<string, number>>({
+    ...DEFAULT_SIGNAL_WEIGHTS,
+  });
+  const [description, setDescription] = useState("");
+
+  const [useCustomEvidence, setUseCustomEvidence] = useState(false);
+  const [evidenceWeights, setEvidenceWeights] = useState<Record<string, number>>({
+    ...SYSTEM_EVIDENCE_DEFAULTS,
+  });
+
+  const [saving, setSaving] = useState(false);
+  const toast = useToast();
+
+  function reset() {
+    setName("");
+    setFormula("linear");
+    setWeights({ ...DEFAULT_SIGNAL_WEIGHTS });
+    setDescription("");
+    setUseCustomEvidence(false);
+    setEvidenceWeights({ ...SYSTEM_EVIDENCE_DEFAULTS });
+  }
+
+  async function handleSubmit(e: React.FormEvent) {
+    e.preventDefault();
+    if (!name.trim()) return;
+    setSaving(true);
+    try {
+      const c = await createScoringConfig({
+        name: name.trim(),
+        formula,
+        weights,
+        evidence_weights: useCustomEvidence ? evidenceWeights : null,
+        description: description || undefined,
+      });
+      onCreated(c);
+      reset();
+      setOpen(false);
+      toast("Scoring config created", "success");
+    } catch (err: any) {
+      toast(err.message ?? "Failed to create config", "error");
+    } finally {
+      setSaving(false);
+    }
+  }
+
+  function setGroupWeight(codes: string[], value: number) {
+    setEvidenceWeights((prev) => {
+      const next = { ...prev };
+      for (const code of codes) next[code] = value;
+      return next;
+    });
+  }
+
+  if (!open) {
+    return (
+      <button
+        onClick={() => setOpen(true)}
+        className="rounded-md border border-dashed border-gray-300 bg-white px-4 py-3 text-sm text-gray-500 hover:border-blue-400 hover:text-blue-600 w-full text-center"
+      >
+        {t("newConfigForm.newConfig")}
+      </button>
+    );
+  }
+
+  return (
+    <form
+      onSubmit={handleSubmit}
+      className="rounded-lg border bg-white p-5 shadow-sm space-y-5"
+    >
+      {/* ── Header ── */}
+      <div className="flex items-center justify-between">
+        <span className="text-sm font-semibold text-gray-700">{t("newConfigForm.formTitle")}</span>
+        <button
+          type="button"
+          onClick={() => { setOpen(false); reset(); }}
+          className="text-gray-400 hover:text-gray-600 text-lg leading-none"
+        >
+          {t("newConfigForm.close")}
+        </button>
+      </div>
+
+      {/* ── Name + formula ── */}
+      <div className="grid grid-cols-2 gap-3">
+        <div>
+          <label className="block text-xs font-medium text-gray-600 mb-1">{t("newConfigForm.nameLabel")}</label>
+          <input
+            type="text"
+            value={name}
+            onChange={(e) => setName(e.target.value)}
+            placeholder={t("newConfigForm.namePlaceholder")}
+            className="w-full rounded border px-2 py-1.5 text-sm focus:outline-none focus:ring-2 focus:ring-blue-500"
+            required
+          />
+        </div>
+        <div>
+          <label className="block text-xs font-medium text-gray-600 mb-1">{t("newConfigForm.formulaLabel")}</label>
+          <select
+            value={formula}
+            onChange={(e) => setFormula(e.target.value)}
+            className="w-full rounded border px-2 py-1.5 text-sm focus:outline-none focus:ring-2 focus:ring-blue-500"
+          >
+            <option value="linear">{t("newConfigForm.linear")}</option>
+            <option value="evidence_weighted">{t("newConfigForm.evidenceWeighted")}</option>
+          </select>
+        </div>
+      </div>
+
+      {formula === "evidence_weighted" && (
+        <p className="text-xs text-blue-700 bg-blue-50 rounded px-2.5 py-2">
+          <strong>evidence_weighted</strong>: the weighted average is multiplied
+          by the resolved evidence quality at the end — even when the
+          &ldquo;Evidence weight&rdquo; signal slider is set to 0. This
+          down-ranks IEA-sourced predictions regardless of embedding strength.
+        </p>
+      )}
+
+      <div>
+        <label className="block text-xs font-medium text-gray-600 mb-1">
+          {t("newConfigForm.descriptionLabel")} <span className="font-normal text-gray-400">{t("newConfigForm.descriptionHelper")}</span>
+        </label>
+        <input
+          type="text"
+          value={description}
+          onChange={(e) => setDescription(e.target.value)}
+          placeholder={t("newConfigForm.descriptionPlaceholder")}
+          className="w-full rounded border px-2 py-1.5 text-sm focus:outline-none focus:ring-2 focus:ring-blue-500"
+        />
+      </div>
+
+      {/* ── Signal weights ── */}
+      <div>
+        <label className="block text-xs font-medium text-gray-600 mb-2">
+          {t("newConfigForm.signalWeights")}
+        </label>
+        <div className="space-y-3">
+          {signals.map(({ key, label, hint }) => {
+            const val = weights[key] ?? 0;
+            return (
+              <div key={key} className="flex items-center gap-3" title={hint}>
+                <span className="text-xs text-gray-600 w-44 shrink-0">{label}</span>
+                <input
+                  type="range"
+                  min="0"
+                  max="1"
+                  step="0.05"
+                  value={val}
+                  onChange={(e) =>
+                    setWeights((prev) => ({ ...prev, [key]: parseFloat(e.target.value) }))
+                  }
+                  className="flex-1 accent-blue-500"
+                />
+                <span className="font-mono text-xs text-gray-700 w-10 text-right">
+                  {val.toFixed(2)}
+                </span>
+              </div>
+            );
+          })}
+        </div>
+      </div>
+
+      {/* ── Evidence-code weights ── */}
+      <div className="border rounded-md p-3 space-y-3">
+        <div className="flex items-center gap-2">
+          <input
+            id="custom-evidence"
+            type="checkbox"
+            checked={useCustomEvidence}
+            onChange={(e) => setUseCustomEvidence(e.target.checked)}
+            className="accent-blue-500"
+          />
+          <label htmlFor="custom-evidence" className="text-xs font-medium text-gray-700 cursor-pointer">
+            {t("newConfigForm.overrideCheckbox")}
+          </label>
+        </div>
+
+        {!useCustomEvidence && (
+          <p className="text-xs text-gray-400">
+            {t("newConfigForm.systemDefaultsNote")}
+          </p>
+        )}
+
+        {useCustomEvidence && (
+          <div className="space-y-5">
+            {evidenceGroups.map((group) => (
+              <div key={group.label}>
+                <div className="flex items-center justify-between mb-1.5">
+                  <div>
+                    <p className="text-xs font-semibold text-gray-600">{group.label}</p>
+                    <p className="text-xs text-gray-400">{group.description}</p>
+                  </div>
+                  {/* Group-level shortcuts */}
+                  <div className="flex gap-1 shrink-0">
+                    {[0, 0.5, 1].map((v) => (
+                      <button
+                        key={v}
+                        type="button"
+                        onClick={() =>
+                          setGroupWeight(group.codes.map((c) => c.code), v)
+                        }
+                        className="rounded border px-1.5 py-0.5 text-xs text-gray-500 hover:bg-gray-50"
+                      >
+                        {v === 0 ? t("newConfigForm.off") : v === 1 ? t("newConfigForm.max") : t("newConfigForm.groupShortcut")}
+                      </button>
+                    ))}
+                  </div>
+                </div>
+                <div className="space-y-2">
+                  {group.codes.map(({ code, label }) => {
+                    const val = evidenceWeights[code] ?? SYSTEM_EVIDENCE_DEFAULTS[code] ?? 0.5;
+                    const isDefault =
+                      Math.abs(val - (SYSTEM_EVIDENCE_DEFAULTS[code] ?? 0.5)) < 0.001;
+                    return (
+                      <div key={code} className="flex items-center gap-2">
+                        <span
+                          className={`font-mono text-xs w-10 shrink-0 ${
+                            isDefault ? "text-gray-500" : "text-amber-700 font-semibold"
+                          }`}
+                          title={label}
+                        >
+                          {code}
+                        </span>
+                        <span className="text-xs text-gray-400 w-48 shrink-0 truncate" title={label}>
+                          {label}
+                        </span>
+                        <input
+                          type="range"
+                          min="0"
+                          max="1"
+                          step="0.05"
+                          value={val}
+                          onChange={(e) =>
+                            setEvidenceWeights((prev) => ({
+                              ...prev,
+                              [code]: parseFloat(e.target.value),
+                            }))
+                          }
+                          className="flex-1 accent-blue-500"
+                        />
+                        <span
+                          className={`font-mono text-xs w-8 text-right ${
+                            isDefault ? "text-gray-500" : "text-amber-700 font-semibold"
+                          }`}
+                        >
+                          {val.toFixed(2)}
+                        </span>
+                      </div>
+                    );
+                  })}
+                </div>
+              </div>
+            ))}
+
+            <button
+              type="button"
+              onClick={() => setEvidenceWeights({ ...SYSTEM_EVIDENCE_DEFAULTS })}
+              className="text-xs text-gray-400 hover:text-gray-600 underline"
+            >
+              {t("newConfigForm.resetEvidenceWeights")}
+            </button>
+          </div>
+        )}
+      </div>
+
+      {/* ── Actions ── */}
+      <div className="flex gap-2 pt-1">
+        <button
+          type="submit"
+          disabled={saving || !name.trim()}
+          className="rounded-md bg-blue-600 px-4 py-2 text-sm font-medium text-white hover:bg-blue-700 disabled:opacity-40"
+        >
+          {saving ? t("newConfigForm.saving") : t("newConfigForm.saveConfig")}
+        </button>
+        <button
+          type="button"
+          onClick={() => { setOpen(false); reset(); }}
+          className="rounded-md border px-4 py-2 text-sm text-gray-600 hover:bg-gray-50"
+        >
+          {t("newConfigForm.cancel")}
+        </button>
+      </div>
+    </form>
+  );
+}
+
+// ── Page ──────────────────────────────────────────────────────────────────────
+
+export default function ScoringPage() {
+  const t = useTranslations("scoring");
+  const [configs, setConfigs] = useState<ScoringConfig[]>([]);
+  const [loading, setLoading] = useState(true);
+  const [loadingPresets, setLoadingPresets] = useState(false);
+  const toast = useToast();
+
+  // Build translated signal and evidence-group arrays inside the component
+  const signals: Signal[] = [
+    { key: "embedding_similarity", label: t("signals.embeddingSimilarity"), hint: t("signals.embeddingSimilarityHint") },
+    { key: "identity_nw", label: t("signals.identityNw"), hint: t("signals.identityNwHint") },
+    { key: "identity_sw", label: t("signals.identitySw"), hint: t("signals.identitySwHint") },
+    { key: "evidence_weight", label: t("signals.evidenceWeight"), hint: t("signals.evidenceWeightHint") },
+    { key: "taxonomic_proximity", label: t("signals.taxonomicProximity"), hint: t("signals.taxonomicProximityHint") },
+  ];
+
+  const evidenceGroups: EvidenceGroup[] = [
+    {
+      label: t("newConfigForm.experimental"),
+      description: t("newConfigForm.experimentalDescription"),
+      codes: [
+        { code: "EXP", label: "Inferred from Experiment" },
+        { code: "IDA", label: "Direct Assay" },
+        { code: "IPI", label: "Physical Interaction" },
+        { code: "IMP", label: "Mutant Phenotype" },
+        { code: "IGI", label: "Genetic Interaction" },
+        { code: "IEP", label: "Expression Pattern" },
+        { code: "HTP", label: "High-Throughput (umbrella)" },
+        { code: "HDA", label: "HT Direct Assay" },
+        { code: "HMP", label: "HT Mutant Phenotype" },
+        { code: "HGI", label: "HT Genetic Interaction" },
+        { code: "HEP", label: "HT Expression Pattern" },
+        { code: "IC",  label: "Inferred by Curator" },
+        { code: "TAS", label: "Traceable Author Statement" },
+      ],
+    },
+    {
+      label: t("newConfigForm.computational"),
+      description: t("newConfigForm.computationalDescription"),
+      codes: [
+        { code: "ISS", label: "Sequence or Structural Similarity" },
+        { code: "ISO", label: "Sequence Orthology" },
+        { code: "ISA", label: "Sequence Alignment" },
+        { code: "ISM", label: "Sequence Model" },
+        { code: "IGC", label: "Genomic Context" },
+        { code: "IBA", label: "Biological aspect of Ancestor" },
+        { code: "IBD", label: "Biological aspect of Descendant" },
+        { code: "IKR", label: "Key Residues" },
+        { code: "IRD", label: "Rapid Divergence" },
+        { code: "RCA", label: "Reviewed Computational Analysis" },
+      ],
+    },
+    {
+      label: t("newConfigForm.electronic"),
+      description: t("newConfigForm.electronicDescription"),
+      codes: [
+        { code: "NAS", label: "Non-traceable Author Statement" },
+        { code: "IEA", label: "Inferred from Electronic Annotation" },
+      ],
+    },
+    {
+      label: t("newConfigForm.noData"),
+      description: t("newConfigForm.noDataDescription"),
+      codes: [{ code: "ND", label: "No biological Data" }],
+    },
+  ];
+
+  useEffect(() => {
+    listScoringConfigs()
+      .then(setConfigs)
+      .catch(() => toast("Failed to load scoring configs", "error"))
+      .finally(() => setLoading(false));
+  }, []);
+
+  async function handleLoadPresets() {
+    setLoadingPresets(true);
+    try {
+      const result = await createPresetScoringConfigs();
+      const updated = await listScoringConfigs();
+      setConfigs(updated);
+      toast(
+        result.created.length > 0
+          ? `Presets created: ${result.created.join(", ")}`
+          : "All presets already exist",
+        result.created.length > 0 ? "success" : "info",
+      );
+    } catch (err: any) {
+      toast(err.message ?? "Failed to load presets", "error");
+    } finally {
+      setLoadingPresets(false);
+    }
+  }
+
+  return (
+    <>
+      {/* ── Page header ── */}
+      <div className="mb-6">
+        <div className="flex items-center justify-between gap-4 mb-1">
+          <h1 className="text-xl font-semibold text-gray-900">{t("title")}</h1>
+          <button
+            onClick={handleLoadPresets}
+            disabled={loadingPresets}
+            className="rounded-md border bg-white px-3 py-1.5 text-sm text-gray-700 shadow-sm hover:bg-gray-50 disabled:opacity-40 shrink-0"
+          >
+            {loadingPresets ? t("presetsLoading") : t("loadPresets")}
+          </button>
+        </div>
+        <p className="text-sm text-gray-500">
+          {t("description")}
+        </p>
+      </div>
+
+      {/* ── Reference card ── */}
+      <div className="mb-6 rounded-lg border bg-white p-4 shadow-sm">
+        <p className="text-xs font-semibold uppercase tracking-wide text-gray-400 mb-3">
+          {t("availableSignals")}
+        </p>
+        <div className="grid grid-cols-1 gap-2 sm:grid-cols-2 mb-4">
+          {signals.map(({ key, label, hint }) => (
+            <div key={key} className="flex items-start gap-2">
+              <span className="rounded bg-gray-100 px-1.5 py-0.5 font-mono text-xs text-gray-700 shrink-0">
+                {label}
+              </span>
+              <span className="text-xs text-gray-500">{hint}</span>
+            </div>
+          ))}
+        </div>
+        <div className="border-t pt-3 grid grid-cols-1 gap-1.5 sm:grid-cols-2 text-xs text-gray-500">
+          <div>
+            <strong className="text-gray-700">linear:</strong>{" "}
+            Σ(w_i · s_i) / Σ(w_i) over all active (w_i &gt; 0 and signal available) signals.
+          </div>
+          <div>
+            <strong className="text-gray-700">evidence_weighted:</strong>{" "}
+            Same as linear, then multiplied by the resolved evidence weight — down-ranks IEA
+            even when other signals are strong.
+          </div>
+        </div>
+      </div>
+
+      {loading && <p className="text-sm text-gray-400">Loading…</p>}
+
+      {!loading && (
+        <div className="space-y-3">
+          <NewConfigForm
+            signals={signals}
+            evidenceGroups={evidenceGroups}
+            onCreated={(c) => setConfigs((prev) => [...prev, c])}
+          />
+
+          {configs.length === 0 && (
+            <p className="text-sm text-gray-400 text-center py-8">
+              {t("noConfigs")}
+            </p>
+          )}
+
+          {configs.map((c) => (
+            <ConfigCard
+              key={c.id}
+              config={c}
+              signals={signals}
+              evidenceGroups={evidenceGroups}
+              onDelete={(id) => setConfigs((prev) => prev.filter((x) => x.id !== id))}
+            />
+          ))}
+        </div>
+      )}
+    </>
+  );
+}
diff --git a/apps/web/app/[locale]/support/page.tsx b/apps/web/app/[locale]/support/page.tsx
new file mode 100644
index 0000000..2a57462
--- /dev/null
+++ b/apps/web/app/[locale]/support/page.tsx
@@ -0,0 +1,74 @@
+"use client";
+
+import { useEffect, useState } from "react";
+import { baseUrl } from "@/lib/api";
+import { useTranslations } from "next-intl";
+
+type Comment = { id: string; comment: string; created_at: string };
+type SupportData = { count: number; comments: Comment[] };
+
+function timeAgo(iso: string): string {
+  const diff = Math.floor((Date.now() - new Date(iso).getTime()) / 1000);
+  if (diff < 60) return "just now";
+  if (diff < 3600) return `${Math.floor(diff / 60)}m ago`;
+  if (diff < 86400) return `${Math.floor(diff / 3600)}h ago`;
+  return `${Math.floor(diff / 86400)}d ago`;
+}
+
+export default function SupportPage() {
+  const t = useTranslations("support");
+  const [data, setData] = useState<SupportData | null>(null);
+  const [loading, setLoading] = useState(true);
+
+  useEffect(() => {
+    fetch(`${baseUrl()}/support?all_comments=true`, { cache: "no-store" })
+      .then((r) => r.json())
+      .then(setData)
+      .finally(() => setLoading(false));
+  }, []);
+
+  if (loading) return <div className="p-8 text-sm text-gray-400">Loading…</div>;
+  if (!data) return <div className="p-8 text-sm text-red-500">Could not load support data.</div>;
+
+  const withComments = data.comments.length;
+  const anonymous = data.count - withComments;
+
+  return (
+    <div className="max-w-2xl space-y-10">
+
+      {/* Hero */}
+      <div className="rounded-2xl border border-blue-100 bg-blue-50 px-8 py-10 text-center space-y-3">
+        <div className="text-6xl">👍</div>
+        <div className="text-5xl font-bold text-blue-700">{data.count.toLocaleString()}</div>
+        <div className="text-base text-blue-500 font-medium">{t("hero.supportCount", { count: data.count })}</div>
+        <div className="text-xs text-blue-400 pt-1">
+          {t("hero.withComments", { count: withComments })} · {t("hero.anonymous", { count: anonymous })}
+        </div>
+      </div>
+
+      {/* Comments */}
+      {data.comments.length > 0 && (
+        <section className="space-y-3">
+          <h2 className="text-sm font-semibold uppercase tracking-wide text-gray-400">
+            {t("commentsSection.heading")}
+          </h2>
+          <div className="space-y-3">
+            {data.comments.map((c) => (
+              <div
+                key={c.id}
+                className="rounded-xl border border-gray-100 bg-white px-5 py-4 shadow-sm"
+              >
+                <p className="text-sm text-gray-700 leading-relaxed">{c.comment}</p>
+                <p className="mt-2 text-xs text-gray-400">{timeAgo(c.created_at)}</p>
+              </div>
+            ))}
+          </div>
+        </section>
+      )}
+
+      {data.comments.length === 0 && (
+        <p className="text-center text-sm text-gray-400">{t("commentsSection.noComments")}</p>
+      )}
+    </div>
+  );
+}
diff --git a/apps/web/app/evaluation/page.tsx b/apps/web/app/evaluation/page.tsx
deleted file mode 100644
index fbc416a..0000000
--- a/apps/web/app/evaluation/page.tsx
+++ /dev/null
@@ -1,455 +0,0 @@
-"use client";
-
-import { useEffect, useState } from "react";
-import { listAnnotationSets, listPredictionSets, baseUrl } from "@/lib/api";
-import type { AnnotationSet, PredictionSet } from "@/lib/api";
-
-const labelClass = "block text-sm font-medium text-gray-700 mb-1";
-const selectClass =
-  "w-full rounded-md border border-gray-300 px-3 py-2 text-sm focus:outline-none focus:ring-2 focus:ring-blue-500";
-const btnPrimary =
-  "rounded-md bg-blue-600 px-4 py-2 text-sm font-medium text-white hover:bg-blue-700 disabled:opacity-50 transition-colors";
-const btnSecondary =
-  "rounded-md border border-gray-300 bg-white px-4 py-2 text-sm font-medium text-gray-700 hover:bg-gray-50 disabled:opacity-50 transition-colors";
-
-type NsMetrics = {
-  fmax: number;
-  precision: number;
-  recall: number;
-  tau: number;
-  coverage: number;
-  n_proteins?: number;
-};
-
-type SettingResults = Record<string, NsMetrics>; // BPO | MFO | CCO
-
-type EvaluationResult = {
-  id: string;
-  evaluation_set_id: string;
-  prediction_set_id: string;
-  job_id: string | null;
-  created_at: string;
-  results: Record<string, SettingResults>; // NK | LK | PK
-};
-
-type EvaluationSet = {
-  id: string;
-  old_annotation_set_id: string;
-  new_annotation_set_id: string;
-  job_id: string | null;
-  created_at: string;
-  stats: {
-    delta_proteins?: number;
-    nk_proteins?: number;
-    lk_proteins?: number;
-    nk_annotations?: number;
-    lk_annotations?: number;
-    known_terms_count?: number;
-  };
-};
-
-async function apiFetch<T>(path: string, init?: RequestInit): Promise<T> {
-  const res = await fetch(`${baseUrl()}${path}`, { cache: "no-store", ...init });
-  if (!res.ok) throw new Error(await res.text());
-  return res.json();
-}
-
-const listEvaluationSets = () => apiFetch<EvaluationSet[]>("/annotations/evaluation-sets");
-const listResults = (evalId: string) =>
-  apiFetch<EvaluationResult[]>(`/annotations/evaluation-sets/${evalId}/results`);
-
-function setLabel(s: AnnotationSet) {
-  const date = new Date(s.created_at).toLocaleDateString();
-  const count = s.annotation_count != null ? ` · ${s.annotation_count.toLocaleString()} ann.` : "";
-  return `[${s.source.toUpperCase()}] ${s.source_version ?? "—"} · ${date}${count}`;
-}
-
-function predLabel(p: PredictionSet) {
-  const date = new Date(p.created_at).toLocaleDateString();
-  const count = p.prediction_count != null ? ` · ${p.prediction_count.toLocaleString()} preds.` : "";
-  return `${p.id.slice(0, 8)}… · ${date}${count}`;
-}
-
-function evalLabel(e: EvaluationSet, annotationSets: AnnotationSet[]) {
-  const date = new Date(e.created_at).toLocaleDateString();
-  const oldSet = annotationSets.find((a) => a.id === e.old_annotation_set_id);
-  const newSet = annotationSets.find((a) => a.id === e.new_annotation_set_id);
-  const delta = e.stats.delta_proteins ?? "?";
-  return `${oldSet?.source_version ?? "old"} → ${newSet?.source_version ?? "new"} · ${delta} delta proteins · ${date}`;
-}
-
-function DownloadLink({ href, label, filename }: { href: string; label: string; filename: string }) {
-  return (
-    <a
-      href={href}
-      download={filename}
-      className="inline-flex items-center gap-1 rounded-md border border-gray-300 bg-white px-3 py-1.5 text-sm hover:bg-gray-50 transition-colors"
-    >
-      ↓ {label}
-    </a>
-  );
-}
-
-function StatBadge({ label, value }: { label: string; value: number | undefined }) {
-  return (
-    <div className="rounded-lg border border-gray-200 bg-gray-50 px-4 py-3 text-center">
-      <div className="text-lg font-semibold text-gray-900">
-        {value != null ? value.toLocaleString() : "—"}
-      </div>
-      <div className="text-xs text-gray-500 mt-0.5">{label}</div>
-    </div>
-  );
-}
-
-const NS_LABELS: Record<string, string> = {
-  BPO: "Biological Process",
-  MFO: "Molecular Function",
-  CCO: "Cellular Component",
-};
-
-const SETTING_COLORS: Record<string, string> = {
-  NK: "bg-purple-50 border-purple-200",
-  LK: "bg-blue-50 border-blue-200",
-  PK: "bg-green-50 border-green-200",
-};
-
-function ResultsTable({ results }: { results: Record<string, SettingResults> }) {
-  const settings = ["NK", "LK", "PK"].filter((s) => results[s] && Object.keys(results[s]).length > 0);
-  if (settings.length === 0) return <p className="text-sm text-gray-400">No results computed.</p>;
-
-  return (
-    <div className="space-y-4">
-      {settings.map((setting) => (
-        <div key={setting} className={`rounded-lg border p-4 ${SETTING_COLORS[setting] ?? ""}`}>
-          <div className="text-sm font-semibold text-gray-700 mb-3">{setting}</div>
-          <div className="grid grid-cols-3 gap-3">
-            {["BPO", "MFO", "CCO"].map((ns) => {
-              const m = results[setting]?.[ns];
-              if (!m) return null;
-              return (
-                <div key={ns} className="rounded-md bg-white border border-gray-200 p-3">
-                  <div className="text-xs font-medium text-gray-500 mb-2">{NS_LABELS[ns]}</div>
-                  <div className="space-y-1 text-sm">
-                    <div className="flex justify-between">
-                      <span className="text-gray-500">Fmax</span>
-                      <span className="font-semibold text-gray-900">{m.fmax.toFixed(3)}</span>
-                    </div>
-                    <div className="flex justify-between">
-                      <span className="text-gray-500">Precision</span>
-                      <span className="text-gray-700">{m.precision.toFixed(3)}</span>
-                    </div>
-                    <div className="flex justify-between">
-                      <span className="text-gray-500">Recall</span>
-                      <span className="text-gray-700">{m.recall.toFixed(3)}</span>
-                    </div>
-                    <div className="flex justify-between">
-                      <span className="text-gray-500">Coverage</span>
-                      <span className="text-gray-700">{(m.coverage * 100).toFixed(1)}%</span>
-                    </div>
-                    <div className="flex justify-between">
-                      <span className="text-gray-500">τ</span>
-                      <span className="text-gray-700">{m.tau.toFixed(2)}</span>
-                    </div>
-                  </div>
-                </div>
-              );
-            })}
-          </div>
-        </div>
-      ))}
-    </div>
-  );
-}
-
-function EvaluationSetCard({
-  e,
-  annotationSets,
-  predictionSets,
-  isSelected,
-  onSelect,
-}: {
-  e: EvaluationSet;
-  annotationSets: AnnotationSet[];
-  predictionSets: PredictionSet[];
-  isSelected: boolean;
-  onSelect: () => void;
-}) {
-  const [results, setResults] = useState<EvaluationResult[]>([]);
-  const [loadingResults, setLoadingResults] = useState(false);
-  const [predSetId, setPredSetId] = useState("");
-  const [maxDistance, setMaxDistance] = useState("");
-  const [running, setRunning] = useState(false);
-  const [runError, setRunError] = useState("");
-
-  useEffect(() => {
-    if (!isSelected) return;
-    setLoadingResults(true);
-    listResults(e.id).then(setResults).finally(() => setLoadingResults(false));
-  }, [isSelected, e.id]);
-
-  async function handleRun() {
-    if (!predSetId) return;
-    setRunning(true);
-    setRunError("");
-    try {
-      const body: Record<string, any> = { prediction_set_id: predSetId };
-      if (maxDistance) body.max_distance = parseFloat(maxDistance);
-      await apiFetch(`/annotations/evaluation-sets/${e.id}/run`, {
-        method: "POST",
-        headers: { "Content-Type": "application/json" },
-        body: JSON.stringify(body),
-      });
-      // Refresh results after a short delay
-      setTimeout(() => {
-        listResults(e.id).then(setResults);
-      }, 2000);
-    } catch (err: any) {
-      setRunError(err.message ?? "Unknown error");
-    } finally {
-      setRunning(false);
-    }
-  }
-
-  return (
-    <div
-      className={`rounded-lg border transition-colors ${
-        isSelected ? "border-blue-400" : "border-gray-200"
-      }`}
-    >
-      {/* Header */}
-      <div
-        className="cursor-pointer p-4 hover:bg-gray-50 rounded-t-lg"
-        onClick={onSelect}
-      >
-        <div className="text-sm font-medium text-gray-800">{evalLabel(e, annotationSets)}</div>
-        <div className="mt-3 grid grid-cols-3 gap-2">
-          <StatBadge label="Delta proteins" value={e.stats.delta_proteins} />
-          <StatBadge label="NK proteins" value={e.stats.nk_proteins} />
-          <StatBadge label="LK proteins" value={e.stats.lk_proteins} />
-        </div>
-      </div>
-
-      {isSelected && (
-        <div className="border-t border-gray-200 p-4 space-y-5 bg-gray-50 rounded-b-lg">
-
-          {/* Downloads */}
-          <div>
-            <p className="text-xs font-medium text-gray-500 mb-2">Ground truth files</p>
-            <div className="flex flex-wrap gap-2">
-              <DownloadLink
-                href={`${baseUrl()}/annotations/evaluation-sets/${e.id}/ground-truth-NK.tsv`}
-                label="NK"
-                filename="ground_truth_NK.tsv"
-              />
-              <DownloadLink
-                href={`${baseUrl()}/annotations/evaluation-sets/${e.id}/ground-truth-LK.tsv`}
-                label="LK"
-                filename="ground_truth_LK.tsv"
-              />
-              <DownloadLink
-                href={`${baseUrl()}/annotations/evaluation-sets/${e.id}/ground-truth-PK.tsv`}
-                label="PK"
-                filename="ground_truth_PK.tsv"
-              />
-              <DownloadLink
-                href={`${baseUrl()}/annotations/evaluation-sets/${e.id}/known-terms.tsv`}
-                label="Known terms"
-                filename="known_terms.tsv"
-              />
-            </div>
-          </div>
-
-          {/* Run evaluation */}
-          <div className="space-y-3">
-            <p className="text-xs font-medium text-gray-500">Run CAFA evaluator</p>
-            <div className="grid grid-cols-2 gap-3">
-              <div>
-                <label className={labelClass}>Prediction set</label>
-                <select
-                  value={predSetId}
-                  onChange={(ev) => setPredSetId(ev.target.value)}
-                  className={selectClass}
-                >
-                  <option value="">— select —</option>
-                  {predictionSets.map((p) => (
-                    <option key={p.id} value={p.id}>{predLabel(p)}</option>
-                  ))}
-                </select>
-              </div>
-              <div>
-                <label className={labelClass}>Max distance (optional)</label>
-                <input
-                  type="number" min="0" max="2" step="0.05" placeholder="no limit"
-                  value={maxDistance}
-                  onChange={(ev) => setMaxDistance(ev.target.value)}
-                  className="w-full rounded-md border border-gray-300 px-3 py-2 text-sm focus:outline-none focus:ring-2 focus:ring-blue-500"
-                />
-              </div>
-            </div>
-            {runError && (
-              <p className="rounded border border-red-200 bg-red-50 px-3 py-2 text-sm text-red-700">{runError}</p>
-            )}
-            <button
-              onClick={handleRun}
-              disabled={!predSetId || running}
-              className={btnPrimary}
-            >
-              {running ? "Queuing…" : "Run Evaluation (NK + LK + PK)"}
-            </button>
-          </div>
-
-          {/* Results */}
-          <div>
-            <p className="text-xs font-medium text-gray-500 mb-3">Results</p>
-            {loadingResults ? (
-              <p className="text-sm text-gray-400">Loading…</p>
-            ) : results.length === 0 ? (
-              <p className="text-sm text-gray-400">No evaluations run yet.</p>
-            ) : (
-              <div className="space-y-6">
-                {results.map((r) => (
-                  <div key={r.id}>
-                    <div className="flex items-center justify-between mb-2">
-                      <div className="text-xs text-gray-400">
-                        Pred: {r.prediction_set_id.slice(0, 8)}… · {new Date(r.created_at).toLocaleString()}
-                      </div>
-                      <a
-                        href={`${baseUrl()}/annotations/evaluation-sets/${e.id}/results/${r.id}/artifacts.zip`}
-                        download={`cafaeval_${r.id.slice(0, 8)}.zip`}
-                        className="inline-flex items-center gap-1 rounded-md border border-gray-300 bg-white px-3 py-1 text-xs hover:bg-gray-50 transition-colors"
-                      >
-                        ↓ Artifacts (.zip)
-                      </a>
-                    </div>
-                    <ResultsTable results={r.results} />
-                  </div>
-                ))}
-              </div>
-            )}
-          </div>
-        </div>
-      )}
-    </div>
-  );
-}
-
-export default function EvaluationPage() {
-  const [annotationSets, setAnnotationSets] = useState<AnnotationSet[]>([]);
-  const [predictionSets, setPredictionSets] = useState<PredictionSet[]>([]);
-  const [evaluationSets, setEvaluationSets] = useState<EvaluationSet[]>([]);
-  const [loading, setLoading] = useState(true);
-
-  const [oldSetId, setOldSetId] = useState("");
-  const [newSetId, setNewSetId] = useState("");
-  const [generating, setGenerating] = useState(false);
-  const [genError, setGenError] = useState("");
-  const [selectedEvalId, setSelectedEvalId] = useState("");
-
-  const reload = () =>
-    Promise.all([listAnnotationSets(), listPredictionSets(), listEvaluationSets()])
-      .then(([ann, pred, ev]) => {
-        setAnnotationSets(ann);
-        setPredictionSets(pred);
-        setEvaluationSets(ev);
-      })
-      .finally(() => setLoading(false));
-
-  useEffect(() => { reload(); }, []);
-
-  const goaSets = annotationSets.filter((s) => s.source === "goa");
-  const canGenerate = oldSetId && newSetId && oldSetId !== newSetId;
-
-  async function handleGenerate() {
-    setGenerating(true);
-    setGenError("");
-    try {
-      await apiFetch("/annotations/evaluation-sets/generate", {
-        method: "POST",
-        headers: { "Content-Type": "application/json" },
-        body: JSON.stringify({ old_annotation_set_id: oldSetId, new_annotation_set_id: newSetId }),
-      });
-      setOldSetId("");
-      setNewSetId("");
-      await reload();
-    } catch (e: any) {
-      setGenError(e.message ?? "Unknown error");
-    } finally {
-      setGenerating(false);
-    }
-  }
-
-  if (loading) return <div className="p-8 text-sm text-gray-500">Loading…</div>;
-
-  return (
-    <div className="p-8 max-w-3xl space-y-10">
-      <h1 className="text-xl font-semibold text-gray-900">CAFA Evaluation</h1>
-
-      {/* ── Generate Evaluation Set ───────────────────────────────── */}
-      <section className="rounded-lg border border-gray-200 p-6 space-y-5">
-        <div>
-          <h2 className="text-base font-semibold text-gray-800">New Evaluation Set</h2>
-          <p className="mt-1 text-sm text-gray-500">
-            Computes the delta between two GOA releases. Applies experimental evidence
-            filtering and NOT-qualifier propagation through the GO DAG.
-          </p>
-        </div>
-        <div className="grid grid-cols-2 gap-4">
-          <div>
-            <label className={labelClass}>Old GOA set (reference)</label>
-            <select value={oldSetId} onChange={(e) => setOldSetId(e.target.value)} className={selectClass}>
-              <option value="">— select —</option>
-              {goaSets.map((s) => (
-                <option key={s.id} value={s.id}>{setLabel(s)}</option>
-              ))}
-            </select>
-          </div>
-          <div>
-            <label className={labelClass}>New GOA set (ground truth)</label>
-            <select value={newSetId} onChange={(e) => setNewSetId(e.target.value)} className={selectClass}>
-              <option value="">— select —</option>
-              {goaSets.map((s) => (
-                <option key={s.id} value={s.id}>{setLabel(s)}</option>
-              ))}
-            </select>
-          </div>
-        </div>
-        {oldSetId && newSetId && oldSetId === newSetId && (
-          <p className="text-xs text-red-500">Old and new sets must be different.</p>
-        )}
-        {genError && (
-          <p className="rounded border border-red-200 bg-red-50 px-3 py-2 text-sm text-red-700">{genError}</p>
-        )}
-        <button onClick={handleGenerate} disabled={!canGenerate || generating} className={btnPrimary}>
-          {generating ? "Queuing…" : "Generate Evaluation Set"}
-        </button>
-      </section>
-
-      {/* ── Evaluation Sets ───────────────────────────────────────── */}
-      {evaluationSets.length > 0 && (
-        <section className="space-y-4">
-          <h2 className="text-base font-semibold text-gray-800">Evaluation Sets</h2>
-          {evaluationSets.map((e) => (
-            <EvaluationSetCard
-              key={e.id}
-              e={e}
-              annotationSets={annotationSets}
-              predictionSets={predictionSets}
-              isSelected={selectedEvalId === e.id}
-              onSelect={() => setSelectedEvalId(e.id === selectedEvalId ? "" : e.id)}
-            />
-          ))}
-        </section>
-      )}
-
-      {/* ── Evaluator command reference ───────────────────────────── */}
-      <section className="rounded-lg border border-gray-100 bg-gray-50 p-5">
-        <h2 className="text-sm font-semibold text-gray-700 mb-2">Manual evaluator command</h2>
-        <pre className="text-xs text-gray-600 overflow-x-auto whitespace-pre-wrap leading-relaxed">
-{`python -m cafaeval go-basic.obo predictions/ ground_truth_NK.tsv -out_dir results/NK
-python -m cafaeval go-basic.obo predictions/ ground_truth_LK.tsv -out_dir results/LK
-python -m cafaeval go-basic.obo predictions/ ground_truth_PK.tsv -known known_terms.tsv -out_dir results/PK`}
-        </pre>
-      </section>
-    </div>
-  );
-}
diff --git a/apps/web/app/layout.tsx b/apps/web/app/layout.tsx
deleted file mode 100644
index 5982d69..0000000
--- a/apps/web/app/layout.tsx
+++ /dev/null
@@ -1,47 +0,0 @@
-import type { Metadata } from "next";
-import { Geist, Geist_Mono } from "next/font/google";
-import "./globals.css";
-import { ResetDbButton } from "@/components/ResetDbButton";
-import { NavLinks } from "@/components/NavLinks";
-import { ToastProvider } from "@/components/Toast";
-
-const geistSans = Geist({
-  variable: "--font-geist-sans",
-  subsets: ["latin"],
-});
-
-const geistMono = Geist_Mono({
-  variable: "--font-geist-mono",
-  subsets: ["latin"],
-});
-
-export const metadata: Metadata = {
-  title: "PROTEA",
-  description: "Protein data platform — job queue and pipeline management",
-};
-
-export default function RootLayout({
-  children,
-}: Readonly<{
-  children: React.ReactNode;
-}>) {
-  return (
-    <html lang="en">
-      <body className={`${geistSans.variable} ${geistMono.variable} antialiased min-h-screen bg-gray-50`}>
-        <ToastProvider>
-          <header className="border-b bg-white px-6 py-3 flex items-center gap-3">
-            <span className="text-lg font-bold tracking-tight text-blue-700">PROTEA</span>
-            <span className="text-gray-300">|</span>
-            <NavLinks />
-            <div className="ml-auto">
-              <ResetDbButton />
-            </div>
-          </header>
-          <main className="mx-auto max-w-5xl px-6 py-6">
-            {children}
-          </main>
-        </ToastProvider>
-      </body>
-    </html>
-  );
-}
diff --git a/apps/web/app/page.tsx b/apps/web/app/page.tsx
deleted file mode 100644
index c93ba0d..0000000
--- a/apps/web/app/page.tsx
+++ /dev/null
@@ -1,5 +0,0 @@
-import { redirect } from "next/navigation";
-
-export default function Home() {
-  redirect("/jobs");
-}
\ No newline at end of file
diff --git a/apps/web/components/EventTimeline.tsx b/apps/web/components/EventTimeline.tsx
index f8c7708..27ecdfe 100644
--- a/apps/web/components/EventTimeline.tsx
+++ b/apps/web/components/EventTimeline.tsx
@@ -1,5 +1,8 @@
+"use client";
+
 import { Fragment } from "react";
 import { JobEvent } from "@/lib/api";
+import { useTranslations } from "next-intl";
 
 const LEVEL_STYLES: Record<string, string> = {
   info: "bg-blue-50 text-blue-700 border-blue-200",
@@ -31,8 +34,9 @@ function Fields({ fields }: { fields: Record<string, any> }) {
 }
 
 export function EventTimeline({ events }: { events: JobEvent[] }) {
+  const t = useTranslations("components.eventTimeline");
   if (events.length === 0) {
-    return <p className="text-sm text-gray-400">No events yet.</p>;
+    return <p className="text-sm text-gray-400">{t("noEvents")}</p>;
   }
 
   return (
diff --git a/apps/web/components/LanguageSwitcher.tsx b/apps/web/components/LanguageSwitcher.tsx
new file mode 100644
index 0000000..ae175d1
--- /dev/null
+++ b/apps/web/components/LanguageSwitcher.tsx
@@ -0,0 +1,43 @@
+"use client";
+import { useLocale } from "next-intl";
+import { useRouter, usePathname } from "next/navigation";
+import { routing } from "@/i18n/routing";
+
+const LOCALE_LABELS: Record<string, string> = {
+  en: "EN",
+  es: "ES",
+  de: "DE",
+  pt: "PT",
+  zh: "中文",
+};
+
+export function LanguageSwitcher() {
+  const locale = useLocale();
+  const router = useRouter();
+  const pathname = usePathname();
+
+  function switchLocale(newLocale: string) {
+    // Replace the locale segment in the pathname
+    const segments = pathname.split("/");
+    segments[1] = newLocale;
+    router.push(segments.join("/"));
+  }
+
+  return (
+    <div className="flex items-center gap-1">
+      {routing.locales.map((l) => (
+        <button
+          key={l}
+          onClick={() => switchLocale(l)}
+          className={`px-1.5 py-0.5 text-xs rounded transition-colors ${
+            l === locale
+              ? "bg-blue-100 text-blue-700 font-semibold"
+              : "text-gray-500 hover:text-gray-700 hover:bg-gray-100"
+          }`}
+        >
+          {LOCALE_LABELS[l]}
+        </button>
+      ))}
+    </div>
+  );
+}
diff --git a/apps/web/components/NavLinks.tsx b/apps/web/components/NavLinks.tsx
index 26b9041..792e31c 100644
--- a/apps/web/components/NavLinks.tsx
+++ b/apps/web/components/NavLinks.tsx
@@ -2,52 +2,104 @@
 
 import Link from "next/link";
 import { usePathname } from "next/navigation";
+import { useState, useEffect } from "react";
 import { DocLinks } from "./DocLinks";
-
-const NAV_GROUPS = [
-  [
-    { href: "/proteins", label: "Proteins" },
-    { href: "/annotations", label: "Annotations" },
-    { href: "/query-sets", label: "Query Sets" },
-  ],
-  [
-    { href: "/embeddings", label: "Embeddings" },
-    { href: "/functional-annotation", label: "Functional Annotation" },
-    { href: "/evaluation", label: "Evaluation" },
-  ],
-  [
-    { href: "/jobs", label: "Jobs" },
-    { href: "/maintenance", label: "Maintenance" },
-  ],
-];
+import { useTranslations } from "next-intl";
 
 export function NavLinks() {
+  const t = useTranslations("nav");
   const pathname = usePathname();
+  const [open, setOpen] = useState(false);
+
+  const NAV_GROUPS = [
+    [
+      { href: "/proteins", label: t("proteins") },
+      { href: "/annotations", label: t("annotations") },
+      { href: "/query-sets", label: t("querySets") },
+    ],
+    [
+      { href: "/embeddings", label: t("embeddings") },
+      { href: "/functional-annotation", label: t("functionalAnnotation") },
+      { href: "/scoring", label: t("scoring") },
+      { href: "/evaluation", label: t("evaluation") },
+    ],
+    [
+      { href: "/jobs", label: t("jobs") },
+      { href: "/maintenance", label: t("maintenance") },
+    ],
+  ];
+
+  const ALL_LINKS = NAV_GROUPS.flat();
+
+  // Close menu on route change
+  useEffect(() => { setOpen(false); }, [pathname]);
+
   return (
-    <nav className="flex items-center gap-1 text-sm">
-      {NAV_GROUPS.map((group, gi) => (
-        <span key={gi} className="flex items-center gap-1">
-          {gi > 0 && <span className="mx-2 text-gray-200">|</span>}
-          {group.map(({ href, label }) => {
-            const active = pathname === href || pathname.startsWith(href + "/");
-            return (
-              <Link
-                key={href}
-                href={href}
-                className={`px-2 py-1 rounded transition-colors ${
-                  active
-                    ? "font-semibold text-blue-600"
-                    : "text-gray-500 hover:text-gray-900"
-                }`}
-              >
-                {label}
-              </Link>
-            );
-          })}
-        </span>
-      ))}
-      <span className="mx-2 text-gray-200">|</span>
-      <DocLinks />
-    </nav>
+    <>
+      {/* Desktop nav */}
+      <nav className="hidden lg:flex items-center gap-1 text-sm">
+        {NAV_GROUPS.map((group, gi) => (
+          <span key={gi} className="flex items-center gap-1">
+            {gi > 0 && <span className="mx-2 text-gray-200">|</span>}
+            {group.map(({ href, label }) => {
+              const active = pathname === href || pathname.startsWith(href + "/");
+              return (
+                <Link
+                  key={href}
+                  href={href}
+                  className={`px-2 py-1 rounded transition-colors ${
+                    active
+                      ? "font-semibold text-blue-600"
+                      : "text-gray-500 hover:text-gray-900"
+                  }`}
+                >
+                  {label}
+                </Link>
+              );
+            })}
+          </span>
+        ))}
+        <span className="mx-2 text-gray-200">|</span>
+        <DocLinks />
+      </nav>
+
+      {/* Mobile hamburger */}
+      <button
+        className="lg:hidden flex flex-col justify-center items-center w-8 h-8 gap-1.5 rounded text-gray-600 hover:bg-gray-100 transition-colors"
+        onClick={() => setOpen((v) => !v)}
+        aria-label="Toggle menu"
+      >
+        <span className={`block h-0.5 w-5 bg-current transition-all duration-200 ${open ? "rotate-45 translate-y-2" : ""}`} />
+        <span className={`block h-0.5 w-5 bg-current transition-all duration-200 ${open ? "opacity-0" : ""}`} />
+        <span className={`block h-0.5 w-5 bg-current transition-all duration-200 ${open ? "-rotate-45 -translate-y-2" : ""}`} />
+      </button>
+
+      {/* Mobile dropdown */}
+      {open && (
+        <div className="lg:hidden absolute left-0 right-0 top-full z-50 border-b bg-white shadow-lg">
+          <nav className="px-4 py-3 flex flex-col gap-1">
+            {ALL_LINKS.map(({ href, label }) => {
+              const active = pathname === href || pathname.startsWith(href + "/");
+              return (
+                <Link
+                  key={href}
+                  href={href}
+                  className={`px-3 py-2.5 rounded-md text-sm transition-colors ${
+                    active
+                      ? "font-semibold text-blue-600 bg-blue-50"
+                      : "text-gray-600 hover:bg-gray-50 hover:text-gray-900"
+                  }`}
+                >
+                  {label}
+                </Link>
+              );
+            })}
+            <div className="mt-1 pt-2 border-t flex gap-4 px-3 text-sm text-gray-500">
+              <DocLinks />
+            </div>
+          </nav>
+        </div>
+      )}
+    </>
   );
 }
diff --git a/apps/web/components/ResetDbButton.tsx b/apps/web/components/ResetDbButton.tsx
index 9a66443..532381f 100644
--- a/apps/web/components/ResetDbButton.tsx
+++ b/apps/web/components/ResetDbButton.tsx
@@ -1,8 +1,10 @@
 "use client";
 
 import { useState } from "react";
+import { useTranslations } from "next-intl";
 
 export function ResetDbButton() {
+  const t = useTranslations("components.resetDbButton");
   const [showConfirm, setShowConfirm] = useState(false);
   const [loading, setLoading] = useState(false);
   const [result, setResult] = useState<"ok" | "error" | null>(null);
@@ -30,37 +32,35 @@ export function ResetDbButton() {
         onClick={() => { setResult(null); setShowConfirm(true); }}
         className="rounded-md border border-red-200 bg-red-50 px-3 py-1.5 text-xs font-medium text-red-600 hover:bg-red-100 transition-colors"
       >
-        Reset DB
+        {t("button")}
       </button>
 
       {result === "ok" && (
-        <span className="ml-2 text-xs text-green-600 font-medium">✓ Reset OK</span>
+        <span className="ml-2 text-xs text-green-600 font-medium">{t("resetOk")}</span>
       )}
       {result === "error" && (
-        <span className="ml-2 text-xs text-red-600 font-medium">✗ Error</span>
+        <span className="ml-2 text-xs text-red-600 font-medium">{t("error")}</span>
       )}
 
       {showConfirm && (
         <div className="fixed inset-0 z-50 flex items-center justify-center bg-black/40 p-4">
           <div className="w-full max-w-sm rounded-xl border bg-white shadow-xl p-6">
-            <h2 className="text-base font-semibold text-gray-900">Reset database?</h2>
-            <p className="mt-2 text-sm text-gray-500">
-              Se borrarán <span className="font-medium text-red-600">todos los datos</span>: proteínas, anotaciones, embeddings, predicciones y jobs. Esta acción es irreversible.
-            </p>
+            <h2 className="text-base font-semibold text-gray-900">{t("confirmTitle")}</h2>
+            <p className="mt-2 text-sm text-gray-500">{t("confirmMessage")}</p>
             <div className="mt-5 flex justify-end gap-2">
               <button
                 onClick={() => setShowConfirm(false)}
                 disabled={loading}
                 className="rounded-md border px-4 py-2 text-sm hover:bg-gray-50 disabled:opacity-50"
               >
-                Cancelar
+                {t("cancel")}
               </button>
               <button
                 onClick={handleReset}
                 disabled={loading}
                 className="rounded-md bg-red-600 px-4 py-2 text-sm text-white hover:bg-red-700 disabled:opacity-50"
               >
-                {loading ? "Reseteando…" : "Sí, resetear"}
+                {loading ? t("confirming") : t("confirm")}
               </button>
             </div>
           </div>
diff --git a/apps/web/components/StatusBadge.tsx b/apps/web/components/StatusBadge.tsx
index 885fcfd..da45929 100644
--- a/apps/web/components/StatusBadge.tsx
+++ b/apps/web/components/StatusBadge.tsx
@@ -1,3 +1,7 @@
+"use client";
+
+import { useTranslations } from "next-intl";
+
 type Status = "queued" | "running" | "succeeded" | "failed" | "cancelled" | string;
 
 const STYLES: Record<string, string> = {
@@ -8,15 +12,21 @@ const STYLES: Record<string, string> = {
   cancelled: "bg-gray-100 text-gray-600 border-gray-200",
 };
 
+const KNOWN_STATUSES = ["queued", "running", "succeeded", "failed", "cancelled"] as const;
+type KnownStatus = typeof KNOWN_STATUSES[number];
+
 export function StatusBadge({ status }: { status: Status }) {
+  const t = useTranslations("components.statusBadge");
   const key = status.toLowerCase();
   const cls = STYLES[key] ?? "bg-gray-100 text-gray-600 border-gray-200";
+  const isKnown = KNOWN_STATUSES.includes(key as KnownStatus);
+  const label = isKnown ? t(key as KnownStatus) : status.toUpperCase();
   return (
     <span className={`inline-flex items-center gap-1.5 rounded-full border px-2.5 py-0.5 text-xs font-medium ${cls}`}>
       {key === "running" && (
         <span className="inline-block h-1.5 w-1.5 animate-pulse rounded-full bg-blue-500" />
       )}
-      {status.toUpperCase()}
+      {label}
     </span>
   );
 }
diff --git a/apps/web/components/SupportButton.tsx b/apps/web/components/SupportButton.tsx
new file mode 100644
index 0000000..7f6a5cb
--- /dev/null
+++ b/apps/web/components/SupportButton.tsx
@@ -0,0 +1,134 @@
+"use client";
+
+import { useEffect, useRef, useState } from "react";
+import Link from "next/link";
+import { baseUrl } from "@/lib/api";
+import { useTranslations } from "next-intl";
+
+type SupportData = {
+  count: number;
+  comments: { id: string; comment: string; created_at: string }[];
+};
+
+export function SupportButton() {
+  const t = useTranslations("components.supportButton");
+  const [data, setData] = useState<SupportData | null>(null);
+  const [open, setOpen] = useState(false);
+  const [comment, setComment] = useState("");
+  const [submitting, setSubmitting] = useState(false);
+  const [submitted, setSubmitted] = useState(false);
+  const ref = useRef<HTMLDivElement>(null);
+
+  useEffect(() => {
+    fetch(`${baseUrl()}/support`, { cache: "no-store" })
+      .then((r) => r.json())
+      .then(setData)
+      .catch(() => {});
+  }, []);
+
+  // Close on outside click
+  useEffect(() => {
+    if (!open) return;
+    function handler(e: MouseEvent) {
+      if (ref.current && !ref.current.contains(e.target as Node)) setOpen(false);
+    }
+    document.addEventListener("mousedown", handler);
+    return () => document.removeEventListener("mousedown", handler);
+  }, [open]);
+
+  async function handleSubmit() {
+    setSubmitting(true);
+    try {
+      const res = await fetch(`${baseUrl()}/support`, {
+        method: "POST",
+        headers: { "Content-Type": "application/json" },
+        body: JSON.stringify({ comment: comment.trim() || null }),
+      });
+      const json = await res.json();
+      setData((prev) => prev ? { ...prev, count: json.count } : { count: json.count, comments: [] });
+      setSubmitted(true);
+      setComment("");
+      setTimeout(() => setOpen(false), 1500);
+    } finally {
+      setSubmitting(false);
+    }
+  }
+
+  const count = data?.count ?? null;
+
+  return (
+    <div ref={ref} className="relative">
+      {/* Trigger button with tooltip */}
+      <div className="group relative inline-block">
+        <button
+          onClick={() => { setOpen((v) => !v); setSubmitted(false); }}
+          className="flex items-center gap-1.5 rounded-full border border-gray-200 bg-white px-2.5 sm:px-3 py-1.5 text-sm text-gray-600 hover:border-blue-300 hover:text-blue-600 transition-colors shadow-sm"
+        >
+          <span className="text-base leading-none">👍</span>
+          <span className="font-medium hidden sm:inline">{t("support")}</span>
+          {count !== null && (
+            <span className="rounded-full bg-blue-50 px-1.5 py-0.5 text-xs font-semibold text-blue-600">
+              {count.toLocaleString()}
+            </span>
+          )}
+        </button>
+        <span className="pointer-events-none absolute bottom-full right-0 mb-2 z-20 hidden group-hover:block w-64 rounded-md border border-gray-200 bg-white px-3 py-2 text-xs text-gray-500 shadow-lg leading-relaxed">
+          {t("tooltip")}
+        </span>
+      </div>
+
+      {/* Popover */}
+      {open && (
+        <div className="absolute right-0 top-full mt-2 z-30 w-80 max-w-[calc(100vw-2rem)] rounded-xl border border-gray-200 bg-white shadow-xl">
+          <div className="p-4 space-y-3">
+            {submitted ? (
+              <p className="text-center text-sm font-medium text-green-600 py-2">
+                {t("thanks")}
+              </p>
+            ) : (
+              <>
+                <p className="text-sm font-semibold text-gray-800">{t("projectSupport")}</p>
+                <textarea
+                  value={comment}
+                  onChange={(e) => setComment(e.target.value)}
+                  placeholder={t("commentPlaceholder")}
+                  maxLength={500}
+                  rows={3}
+                  className="w-full rounded-md border border-gray-300 px-3 py-2 text-sm resize-none focus:outline-none focus:ring-2 focus:ring-blue-500"
+                />
+                <p className="text-xs text-gray-400">
+                  {t("publicNote")}
+                </p>
+                <button
+                  onClick={handleSubmit}
+                  disabled={submitting}
+                  className="w-full rounded-md bg-blue-600 px-4 py-2 text-sm font-medium text-white hover:bg-blue-700 disabled:opacity-50 transition-colors"
+                >
+                  {submitting ? t("sending") : t("sendThumbsUp")}
+                </button>
+              </>
+            )}
+          </div>
+
+          {/* Recent comments */}
+          {data && data.comments?.length > 0 && (
+            <div className="border-t border-gray-100 px-4 py-3 space-y-2 max-h-48 overflow-y-auto">
+              <div className="flex items-center justify-between">
+                <p className="text-xs font-medium text-gray-400 uppercase tracking-wide">{t("recentComments")}</p>
+                <Link href="/support" className="text-xs text-blue-500 hover:underline" onClick={() => setOpen(false)}>
+                  {t("viewAll")}
+                </Link>
+              </div>
+              {data.comments.map((c) => (
+                <div key={c.id} className="text-xs text-gray-600 leading-relaxed">
+                  <span className="text-gray-400 mr-1">{new Date(c.created_at).toLocaleDateString()}</span>
+                  {c.comment}
+                </div>
+              ))}
+            </div>
+          )}
+        </div>
+      )}
+    </div>
+  );
+}
diff --git a/apps/web/components/UsagePolicyModal.tsx b/apps/web/components/UsagePolicyModal.tsx
new file mode 100644
index 0000000..b39e15a
--- /dev/null
+++ b/apps/web/components/UsagePolicyModal.tsx
@@ -0,0 +1,67 @@
+"use client";
+
+import { useEffect, useState } from "react";
+import { useTranslations } from "next-intl";
+
+const STORAGE_KEY = "protea_policy_accepted_v1";
+
+export function UsagePolicyModal() {
+  const t = useTranslations("components.usagePolicyModal");
+  const [visible, setVisible] = useState(false);
+
+  useEffect(() => {
+    if (!localStorage.getItem(STORAGE_KEY)) {
+      setVisible(true);
+    }
+  }, []);
+
+  function accept() {
+    localStorage.setItem(STORAGE_KEY, "1");
+    setVisible(false);
+  }
+
+  if (!visible) return null;
+
+  return (
+    <div className="fixed inset-0 z-50 flex items-center justify-center bg-black/60 backdrop-blur-sm">
+      <div className="relative mx-4 w-full max-w-lg rounded-xl bg-white shadow-2xl">
+        {/* Header */}
+        <div className="border-b px-6 py-4">
+          <h2 className="text-lg font-semibold text-gray-900">{t("title")}</h2>
+          <p className="mt-0.5 text-sm text-gray-500">{t("subtitle")}</p>
+        </div>
+
+        {/* Body */}
+        <div className="px-6 py-5 text-sm text-gray-700 space-y-3">
+          <p>{t("intro")}</p>
+          <ul className="space-y-2 list-none">
+            {(["rule1", "rule2", "rule3", "rule4", "rule5", "rule6"] as const).map((key) => (
+              <li key={key} className="flex gap-2">
+                <span className="mt-0.5 flex-shrink-0 text-blue-600">•</span>
+                <span>{key === "rule4" ? t.rich(key, {
+                  a: (chunks) => (
+                    <a href="https://github.com/frapercan/PROTEA" target="_blank" rel="noopener noreferrer" className="text-blue-600 underline hover:text-blue-800">
+                      {chunks}
+                    </a>
+                  ),
+                }) : t(key)}</span>
+              </li>
+            ))}
+          </ul>
+          <p className="text-xs text-gray-400 pt-1">{t("dataPublicNote")}</p>
+        </div>
+
+        {/* Footer */}
+        <div className="border-t px-6 py-4 flex justify-end">
+          <button
+            onClick={accept}
+            className="rounded-lg bg-blue-600 px-5 py-2 text-sm font-medium text-white hover:bg-blue-700 transition-colors"
+          >
+            {t("accept")}
+          </button>
+        </div>
+      </div>
+    </div>
+  );
+}
+
diff --git a/apps/web/e2e/mobile-check.spec.ts b/apps/web/e2e/mobile-check.spec.ts
new file mode 100644
index 0000000..42b5bfa
--- /dev/null
+++ b/apps/web/e2e/mobile-check.spec.ts
@@ -0,0 +1,101 @@
+import { test, expect } from "@playwright/test";
+
+const PAGES = [
+  { path: "/jobs", name: "jobs" },
+  { path: "/proteins", name: "proteins" },
+  { path: "/embeddings", name: "embeddings" },
+  { path: "/annotations", name: "annotations" },
+  { path: "/query-sets", name: "query-sets" },
+  { path: "/functional-annotation", name: "functional-annotation" },
+];
+
+const NAV_LABELS = [
+  "Proteins", "Annotations", "Query Sets",
+  "Embeddings", "Functional Annotation", "Scoring", "Evaluation",
+  "Jobs", "Maintenance",
+];
+
+// ── Navigation ─────────────────────────────────────────────────────────────
+
+test("header shows hamburger and hides desktop nav", async ({ page }) => {
+  await page.goto("/jobs");
+  await expect(page.locator('button[aria-label="Toggle menu"]')).toBeVisible();
+  // Desktop nav (lg:flex) is hidden on mobile — it has display:none from 'hidden' class
+  await expect(page.locator("nav.hidden").first()).toBeHidden();
+});
+
+test("hamburger opens dropdown with all nav links", async ({ page }) => {
+  await page.goto("/jobs");
+  await page.locator('button[aria-label="Toggle menu"]').click();
+  // After opening, desktop nav links remain display:none, dropdown links become visible.
+  // getByRole('link') only returns non-hidden elements.
+  for (const label of NAV_LABELS) {
+    await expect(page.getByRole("link", { name: label }).first()).toBeVisible();
+  }
+});
+
+test("hamburger closes dropdown on second click", async ({ page }) => {
+  await page.goto("/jobs");
+  const btn = page.locator('button[aria-label="Toggle menu"]');
+  // Dropdown div has class lg:hidden — target it specifically
+  const dropdown = page.locator("div.lg\\:hidden.absolute");
+  await btn.click();
+  await expect(dropdown).toBeVisible();
+  await btn.click();
+  await expect(dropdown).toHaveCount(0);
+});
+
+test("menu closes automatically after navigating", async ({ page }) => {
+  await page.goto("/jobs");
+  await page.locator('button[aria-label="Toggle menu"]').click();
+  const dropdown = page.locator("div.lg\\:hidden.absolute");
+  await expect(dropdown).toBeVisible();
+  // Click "Proteins" link in the dropdown (only this one is visible; desktop nav is display:none)
+  await page.getByRole("link", { name: "Proteins" }).first().click();
+  await page.waitForURL(/\/proteins/);
+  // Dropdown should be removed from DOM after navigation
+  await expect(dropdown).toHaveCount(0);
+});
+
+test("Support button hides text label on mobile", async ({ page }) => {
+  await page.goto("/jobs");
+  await expect(page.locator("header span.hidden.sm\\:inline")).toBeHidden();
+});
+
+// ── Jobs page ──────────────────────────────────────────────────────────────
+
+test("jobs page shows card layout and hides desktop table", async ({ page }) => {
+  await page.goto("/jobs");
+  await expect(page.locator("div.hidden.lg\\:block").first()).toBeHidden();
+  await expect(page.locator("div.lg\\:hidden").first()).toBeVisible();
+});
+
+// ── No overflow ────────────────────────────────────────────────────────────
+
+for (const { path, name } of PAGES) {
+  test(`no horizontal overflow on ${name}`, async ({ page }) => {
+    await page.goto(path);
+    await page.waitForLoadState("networkidle");
+    const bodyWidth = await page.evaluate(() => document.body.scrollWidth);
+    const viewportWidth = page.viewportSize()!.width;
+    expect(bodyWidth, `${name}: scrollWidth=${bodyWidth} viewport=${viewportWidth}`).toBeLessThanOrEqual(viewportWidth + 2);
+  });
+}
+
+// ── Screenshots ────────────────────────────────────────────────────────────
+
+for (const { path, name } of PAGES) {
+  test(`screenshot — ${name}`, async ({ page }) => {
+    await page.goto(path);
+    await page.waitForLoadState("networkidle");
+    await page.screenshot({ path: `e2e/screenshots/mobile-${name}.png`, fullPage: true });
+  });
+}
+
+test("screenshot — jobs menu open", async ({ page }) => {
+  await page.goto("/jobs");
+  await page.waitForLoadState("networkidle");
+  await page.locator('button[aria-label="Toggle menu"]').click();
+  await page.waitForTimeout(300);
+  await page.screenshot({ path: "e2e/screenshots/mobile-menu-open.png" });
+});
diff --git a/apps/web/e2e/screenshots/mobile-annotations.png b/apps/web/e2e/screenshots/mobile-annotations.png
new file mode 100644
index 0000000..587a19a
Binary files /dev/null and b/apps/web/e2e/screenshots/mobile-annotations.png differ
diff --git a/apps/web/e2e/screenshots/mobile-embeddings.png b/apps/web/e2e/screenshots/mobile-embeddings.png
new file mode 100644
index 0000000..5c54dd3
Binary files /dev/null and b/apps/web/e2e/screenshots/mobile-embeddings.png differ
diff --git a/apps/web/e2e/screenshots/mobile-functional-annotation.png b/apps/web/e2e/screenshots/mobile-functional-annotation.png
new file mode 100644
index 0000000..10d57f4
Binary files /dev/null and b/apps/web/e2e/screenshots/mobile-functional-annotation.png differ
diff --git a/apps/web/e2e/screenshots/mobile-jobs.png b/apps/web/e2e/screenshots/mobile-jobs.png
new file mode 100644
index 0000000..5f14b57
Binary files /dev/null and b/apps/web/e2e/screenshots/mobile-jobs.png differ
diff --git a/apps/web/e2e/screenshots/mobile-menu-open.png b/apps/web/e2e/screenshots/mobile-menu-open.png
new file mode 100644
index 0000000..19f6da6
Binary files /dev/null and b/apps/web/e2e/screenshots/mobile-menu-open.png differ
diff --git a/apps/web/e2e/screenshots/mobile-proteins.png b/apps/web/e2e/screenshots/mobile-proteins.png
new file mode 100644
index 0000000..c2fe1ae
Binary files /dev/null and b/apps/web/e2e/screenshots/mobile-proteins.png differ
diff --git a/apps/web/e2e/screenshots/mobile-query-sets.png b/apps/web/e2e/screenshots/mobile-query-sets.png
new file mode 100644
index 0000000..4ee4300
Binary files /dev/null and b/apps/web/e2e/screenshots/mobile-query-sets.png differ
diff --git a/apps/web/e2e/screenshots/tablet-annotations.png b/apps/web/e2e/screenshots/tablet-annotations.png
new file mode 100644
index 0000000..8b2e952
Binary files /dev/null and b/apps/web/e2e/screenshots/tablet-annotations.png differ
diff --git a/apps/web/e2e/screenshots/tablet-embeddings.png b/apps/web/e2e/screenshots/tablet-embeddings.png
new file mode 100644
index 0000000..5f6a207
Binary files /dev/null and b/apps/web/e2e/screenshots/tablet-embeddings.png differ
diff --git a/apps/web/e2e/screenshots/tablet-functional-annotation.png b/apps/web/e2e/screenshots/tablet-functional-annotation.png
new file mode 100644
index 0000000..c65ab78
Binary files /dev/null and b/apps/web/e2e/screenshots/tablet-functional-annotation.png differ
diff --git a/apps/web/e2e/screenshots/tablet-jobs.png b/apps/web/e2e/screenshots/tablet-jobs.png
new file mode 100644
index 0000000..b773f72
Binary files /dev/null and b/apps/web/e2e/screenshots/tablet-jobs.png differ
diff --git a/apps/web/e2e/screenshots/tablet-proteins.png b/apps/web/e2e/screenshots/tablet-proteins.png
new file mode 100644
index 0000000..40c6710
Binary files /dev/null and b/apps/web/e2e/screenshots/tablet-proteins.png differ
diff --git a/apps/web/e2e/screenshots/tablet-query-sets.png b/apps/web/e2e/screenshots/tablet-query-sets.png
new file mode 100644
index 0000000..c421f8b
Binary files /dev/null and b/apps/web/e2e/screenshots/tablet-query-sets.png differ
diff --git a/apps/web/e2e/tablet-check.spec.ts b/apps/web/e2e/tablet-check.spec.ts
new file mode 100644
index 0000000..cecdcb7
--- /dev/null
+++ b/apps/web/e2e/tablet-check.spec.ts
@@ -0,0 +1,43 @@
+import { test, expect } from "@playwright/test";
+
+// Tablet = 768px. Desktop nav shows at lg (1024px+), so tablet shows hamburger.
+
+const PAGES = [
+  { path: "/jobs", name: "jobs" },
+  { path: "/proteins", name: "proteins" },
+  { path: "/embeddings", name: "embeddings" },
+  { path: "/annotations", name: "annotations" },
+  { path: "/query-sets", name: "query-sets" },
+  { path: "/functional-annotation", name: "functional-annotation" },
+];
+
+test("shows hamburger on tablet (desktop nav at lg, not md)", async ({ page }) => {
+  await page.goto("/jobs");
+  await expect(page.locator('button[aria-label="Toggle menu"]')).toBeVisible();
+  await expect(page.locator("nav.hidden").first()).toBeHidden();
+});
+
+test("jobs page shows card layout on tablet", async ({ page }) => {
+  await page.goto("/jobs");
+  // Card layout uses lg:hidden (visible below 1024px), desktop table uses lg:block (hidden below 1024px)
+  await expect(page.locator("div.hidden.lg\\:block").first()).toBeHidden();
+  await expect(page.locator("div.lg\\:hidden").first()).toBeVisible();
+});
+
+for (const { path, name } of PAGES) {
+  test(`no horizontal overflow on ${name}`, async ({ page }) => {
+    await page.goto(path);
+    await page.waitForLoadState("networkidle");
+    const bodyWidth = await page.evaluate(() => document.body.scrollWidth);
+    const viewportWidth = page.viewportSize()!.width;
+    expect(bodyWidth, `${name}: scrollWidth=${bodyWidth} viewport=${viewportWidth}`).toBeLessThanOrEqual(viewportWidth + 2);
+  });
+}
+
+for (const { path, name } of PAGES) {
+  test(`screenshot — ${name}`, async ({ page }) => {
+    await page.goto(path);
+    await page.waitForLoadState("networkidle");
+    await page.screenshot({ path: `e2e/screenshots/tablet-${name}.png`, fullPage: true });
+  });
+}
diff --git a/apps/web/i18n/request.ts b/apps/web/i18n/request.ts
new file mode 100644
index 0000000..1d3c825
--- /dev/null
+++ b/apps/web/i18n/request.ts
@@ -0,0 +1,13 @@
+import { getRequestConfig } from "next-intl/server";
+import { routing } from "./routing";
+
+export default getRequestConfig(async ({ requestLocale }) => {
+  let locale = await requestLocale;
+  if (!locale || !routing.locales.includes(locale as any)) {
+    locale = routing.defaultLocale;
+  }
+  return {
+    locale,
+    messages: (await import(`../messages/${locale}.json`)).default,
+  };
+});
diff --git a/apps/web/i18n/routing.ts b/apps/web/i18n/routing.ts
new file mode 100644
index 0000000..1c51a60
--- /dev/null
+++ b/apps/web/i18n/routing.ts
@@ -0,0 +1,6 @@
+import { defineRouting } from "next-intl/routing";
+
+export const routing = defineRouting({
+  locales: ["en", "es", "de", "pt", "zh"],
+  defaultLocale: "en",
+});
diff --git a/apps/web/lib/api.ts b/apps/web/lib/api.ts
index 2c093d3..a3a638d 100644
--- a/apps/web/lib/api.ts
+++ b/apps/web/lib/api.ts
@@ -29,8 +29,19 @@ export function baseUrl(): string {
 }
 
 async function http<T>(path: string, init?: RequestInit): Promise<T> {
-  const res = await fetch(`${baseUrl()}${path}`, { cache: "no-store", ...init });
-  if (!res.ok) throw new Error(await res.text());
+  let res: Response;
+  try {
+    res = await fetch(`${baseUrl()}${path}`, { cache: "no-store", ...init });
+  } catch (e: any) {
+    throw new Error(e?.message ?? "Network error");
+  }
+  if (!res.ok) {
+    const body = await res.text();
+    const msg = body.trimStart().startsWith("<")
+      ? `HTTP ${res.status} ${res.statusText}`
+      : body;
+    throw new Error(msg);
+  }
   return await res.json();
 }
 
@@ -97,8 +108,11 @@ export type EmbeddingConfig = {
 export type PredictionSet = {
   id: string;
   embedding_config_id: string;
+  embedding_config_name?: string;
   annotation_set_id: string;
+  annotation_set_label?: string;
   ontology_snapshot_id: string;
+  ontology_snapshot_version?: string;
   limit_per_entry: number;
   distance_threshold?: number | null;
   created_at: string;
@@ -120,6 +134,7 @@ export type OntologySnapshot = {
   id: string;
   obo_url: string;
   obo_version: string;
+  ia_url?: string | null;
   loaded_at: string;
   go_term_count?: number;
 };
@@ -232,6 +247,8 @@ export function launchPredictGoTerms(body: {
   // Feature engineering
   compute_alignments?: boolean;
   compute_taxonomy?: boolean;
+  // Per-aspect KNN indices
+  aspect_separated_knn?: boolean;
 }) {
   return http<{ id: string; status: string }>(`/embeddings/predict`, {
     method: "POST",
@@ -348,6 +365,13 @@ export function listOntologySnapshots() {
   return http<OntologySnapshot[]>(`/annotations/snapshots`);
 }
 
+export function setSnapshotIaUrl(snapshotId: string, iaUrl: string | null) {
+  return http<{ id: string; obo_version: string; ia_url: string | null }>(
+    `/annotations/snapshots/${snapshotId}/ia-url`,
+    { method: "PATCH", headers: { "Content-Type": "application/json" }, body: JSON.stringify({ ia_url: iaUrl }) }
+  );
+}
+
 export type QuerySet = {
   id: string;
   name: string;
@@ -397,6 +421,78 @@ export function runVacuumEmbeddings() {
   return http<{ deleted_embeddings: number }>(`/maintenance/vacuum-embeddings`, { method: "POST" });
 }
 
+// ---------------------------------------------------------------------------
+// Scoring
+// ---------------------------------------------------------------------------
+
+export type ScoringConfig = {
+  id: string;
+  name: string;
+  formula: string;
+  /** Signal weights (embedding_similarity, identity_nw, …) — all in [0, 1]. */
+  weights: Record<string, number>;
+  /**
+   * Optional per-GO-evidence-code quality overrides in [0, 1].
+   * When null the engine falls back to the system defaults
+   * (EXP/IDA → 1.0, ISS/IBA → 0.7, IEA → 0.3, ND → 0.1, …).
+   * Partial dicts are valid: absent codes still resolve via the system table.
+   */
+  evidence_weights: Record<string, number> | null;
+  description?: string | null;
+  created_at: string;
+};
+
+export function listScoringConfigs() {
+  return http<ScoringConfig[]>(`/scoring/configs`);
+}
+
+export function createScoringConfig(body: {
+  name: string;
+  formula: string;
+  weights: Record<string, number>;
+  /**
+   * Optional per-GO-evidence-code quality overrides.
+   * Omit or pass null to use system defaults.
+   * Keys must be valid GO evidence codes (EXP, IDA, IEA, …).
+   * Values must be floats in [0, 1].
+   */
+  evidence_weights?: Record<string, number> | null;
+  description?: string;
+}) {
+  return http<ScoringConfig>(`/scoring/configs`, {
+    method: "POST",
+    headers: { "Content-Type": "application/json" },
+    body: JSON.stringify(body),
+  });
+}
+
+export async function deleteScoringConfig(id: string) {
+  const res = await fetch(`${baseUrl()}/scoring/configs/${id}`, {
+    cache: "no-store",
+    method: "DELETE",
+  });
+  if (!res.ok) throw new Error(await res.text());
+}
+
+export function createPresetScoringConfigs() {
+  return http<{ created: string[] }>(`/scoring/configs/presets`, {
+    method: "POST",
+    headers: { "Content-Type": "application/json" },
+    body: "{}",
+  });
+}
+
+export function getScoredTsvUrl(
+  setId: string,
+  scoringConfigId: string,
+  params?: { minScore?: number },
+): string {
+  const q = new URLSearchParams();
+  q.set("scoring_config_id", scoringConfigId);
+  if (params?.minScore !== undefined) q.set("min_score", String(params.minScore));
+  return `${baseUrl()}/scoring/prediction-sets/${setId}/score.tsv?${q.toString()}`;
+}
+
 export async function createQuerySet(file: File, name: string, description?: string): Promise<QuerySet> {
   const form = new FormData();
   form.append("file", file);
diff --git a/apps/web/messages/de.json b/apps/web/messages/de.json
new file mode 100644
index 0000000..909550d
--- /dev/null
+++ b/apps/web/messages/de.json
@@ -0,0 +1,16 @@
+{
+  "layout": { "title": "PROTEA", "description": "Proteindatenplattform — Job-Warteschlange und Pipeline-Verwaltung" },
+  "nav": { "proteins": "Proteine", "annotations": "Annotationen", "querySets": "Query-Sets", "embeddings": "Embeddings", "functionalAnnotation": "Funktionale Annotation", "scoring": "Bewertung", "evaluation": "Evaluierung", "jobs": "Jobs", "maintenance": "Wartung" },
+  "jobs": { "title": "Jobs", "allStatuses": "Alle Status", "queued": "In der Warteschlange", "running": "Laufend", "succeeded": "Erfolgreich", "failed": "Fehlgeschlagen", "cancelled": "Abgebrochen", "autoRefresh": "Auto-Aktualisierung", "refresh": "Aktualisieren", "noJobsFound": "Keine Jobs gefunden.", "status": "Status", "operation": "Operation", "jobId": "Job-ID", "created": "Erstellt", "activeJobs": "{count} aktiv", "jobDetail": { "title": "Job-Details", "backToJobs": "← Jobs", "live": "Live", "cancel": "Abbrechen", "delete": "Löschen", "deleteConfirm": "Diesen Job löschen?", "queue": "Warteschlange:", "created": "Erstellt:", "started": "Gestartet:", "finished": "Beendet:", "progress": "{current} / {total} {unit} ({percent}%)", "payloadLabel": "Nutzlast", "childJobsTitle": "Untergeordnete Jobs", "childJobsCount": "({count})", "eventsTitle": "Ereignisse", "eventsCount": "({count})" } },
+  "proteins": { "title": "Proteine", "tabs": { "browse": "Durchsuchen", "stats": "Statistiken", "insert": "Proteine einfügen", "metadata": "Metadaten abrufen" }, "browseTab": { "searchPlaceholder": "Akzession, Gen, Organismus…", "search": "Suchen", "clear": "Zurücksetzen", "allProteins": "Alle Proteine", "swissProt": "Nur Swiss-Prot", "trembl": "Nur TrEMBL", "canonicalOnly": "Nur kanonische", "totalProteins": "{count} Proteine", "tableHeaders": { "accession": "Akzession", "entryName": "Eintragsname", "gene": "Gen", "organism": "Organismus", "length": "Länge", "source": "Quelle" }, "noProteinsCta": "Keine Proteine gefunden. Verwenden Sie den Tab 'Proteine einfügen', um aus UniProt zu importieren.", "pagination": { "page": "Seite {current} von {total}", "previous": "Zurück", "next": "Weiter" } }, "statsTab": { "refresh": "Aktualisieren", "loading": "Lädt…", "overview": "Übersicht", "coverage": "Abdeckung", "totalProteins": "Proteine gesamt", "canonical": "Kanonische", "isoforms": "{count} Isoformen", "reviewed": "Swiss-Prot", "reviewedSub": "geprüft", "unreviewed": "TrEMBL", "unreviewedSub": "ungeprüft", "withMetadata": "Mit Metadaten", "metadataSub": "{percent}% der kanonischen", "withEmbeddings": "Mit Embeddings", "embeddingsSub": "{percent}% der Gesamtzahl", "withGoAnnotations": "Mit GO-Annotationen", "goAnnotationsSub": "{percent}% der Gesamtzahl" }, "insertTab": { "title": "Proteine aus UniProt einfügen", "description": "Lädt FASTA-Sequenzen herunter und fügt Protein- und Sequenz-Einträge ein.", "searchCriteriaLabel": "Suchkriterien", "searchCriteriaHelper": "UniProt-Abfrage — reviewed:true = nur Swiss-Prot", "pageSizeLabel": "Seitengröße", "totalLimitLabel": "Gesamtlimit", "totalLimitOptional": "(optional)", "includeIsoforms": "Isoformen einschließen", "jobQueuedPrefix": "Job in Warteschlange: ", "launchJob": "Job starten", "launching": "Wird gestartet…" }, "metadataTab": { "title": "UniProt-Metadaten abrufen", "description": "Lädt TSV-Annotationen herunter und fügt ProteinUniProtMetadata-Einträge ein.", "searchCriteriaLabel": "Suchkriterien", "searchCriteriaHelper": "UniProt-Abfrage — reviewed:true = nur Swiss-Prot", "pageSizeLabel": "Seitengröße", "totalLimitLabel": "Gesamtlimit", "totalLimitOptional": "(optional)", "launchJob": "Job starten", "launching": "Wird gestartet…" }, "sourceSwissProt": "Swiss-Prot", "sourceTrembl": "TrEMBL" },
+  "proteinDetail": { "backToProteins": "← Proteine", "tabs": { "overview": "Übersicht", "annotations": "GO-Annotationen" }, "overviewTab": { "identity": "Identität", "gene": "Gen", "organism": "Organismus", "taxonId": "Taxon-ID", "length": "Länge", "aa": "AS", "sequenceId": "Sequenz-ID", "canonical": "Kanonisch", "coverage": "Abdeckung", "embeddings": "Embeddings", "goAnnotations": "GO-Annotationen", "metadata": "Metadaten", "yes": "ja", "none": "keine", "isoforms": "Isoformen", "function": "Funktion", "biochemistry": "Biochemie", "ecNumber": "EC-Nummer", "catalyticActivity": "Katalytische Aktivität", "cofactor": "Kofaktor", "activityRegulation": "Aktivitätsregulation", "pathway": "Signalweg", "absorption": "Absorption", "kinetics": "Kinetik", "phDependence": "pH-Abhängigkeit", "redoxPotential": "Redoxpotenzial", "temperatureDependence": "Temperaturabhängigkeit", "rheaId": "Rhea-ID", "keywords": "Schlüsselwörter", "noFunctionalMetadata": "Keine funktionalen Metadaten verfügbar. Verwenden Sie den Tab 'Metadaten abrufen', um aus UniProt zu importieren.", "showGoGraph": "GO-Graph anzeigen", "hideGoGraph": "GO-Graph ausblenden", "loadingGraph": "Graph wird geladen…", "noGoAnnotations": "Keine GO-Annotationen für dieses Protein gefunden.", "molecularFunction": "Molekulare Funktion", "biologicalProcess": "Biologischer Prozess", "cellularComponent": "Zelluläre Komponente", "annotations": "Annotationen", "goTableHeaders": { "goId": "GO-ID", "name": "Name", "evidence": "Evidenz", "qualifier": "Qualifikator", "source": "Quelle" } } },
+  "annotations": { "title": "Annotationen", "tabs": { "sets": "Annotationssets", "snapshots": "Ontologie-Snapshots", "loadSnapshot": "Snapshot laden", "loadGoa": "GOA laden", "loadQuickgo": "QuickGO laden" }, "setsTab": { "annotationSets": "{count} Annotationssets", "refresh": "Aktualisieren", "noSetsFound": "Noch keine Annotationssets. Laden Sie GO-Annotationen aus dem Tab 'GOA laden' oder 'QuickGO laden'.", "tableHeaders": { "id": "ID", "source": "Quelle", "version": "Version", "annotations": "Annotationen", "meta": "Meta", "created": "Erstellt" }, "delete": "Löschen", "deleteConfirm": "Dieses Annotationsset und seine {count} GO-Annotationen löschen? Dies kann nicht rückgängig gemacht werden.", "deleteConfirmNoAnnotations": "Dieses Annotationsset löschen?" }, "snapshotsTab": { "snapshots": "{count} Snapshots", "refresh": "Aktualisieren", "noSnapshotsFound": "Noch keine Ontologie-Snapshots. Verwenden Sie den Tab 'Snapshot laden'.", "tableHeaders": { "id": "ID", "version": "Version", "goTerms": "GO-Terme", "iaUrl": "IA-URL", "loaded": "Geladen" }, "notSet": "nicht gesetzt", "save": "Speichern", "cancel": "Abbrechen", "editTooltip": "Tippen zum Bearbeiten der IA-URL" }, "loadSnapshotTab": { "title": "Ontologie-Snapshot laden", "description": "Lädt eine GO OBO-Datei herunter und füllt GOTerm-Einträge.", "oboUrlLabel": "OBO-URL", "launchJob": "Job starten", "launching": "Wird gestartet…" }, "loadGoaTab": { "title": "GOA-Annotationen laden", "description": "Lädt GO-Annotationen aus einer GAF-Datei in großen Mengen.", "snapshotLabel": "Ontologie-Snapshot", "selectSnapshot": "— Snapshot auswählen —", "noSnapshots": "Keine Snapshots — zuerst Snapshot laden ausführen.", "gafUrlLabel": "GAF-URL", "gafUrlPlaceholder": "https://current.geneontology.org/annotations/goa_human.gaf.gz", "sourceVersionLabel": "Quellversion", "sourceVersionPlaceholder": "2025-03", "launchJob": "Job starten", "launching": "Wird gestartet…" }, "loadQuickgoTab": { "title": "QuickGO-Annotationen laden", "description": "Streamt GO-Annotationen aus der QuickGO-Massen-Download-API.", "snapshotLabel": "Ontologie-Snapshot", "selectSnapshot": "— Snapshot auswählen —", "noSnapshots": "Keine Snapshots — zuerst Snapshot laden ausführen.", "sourceVersionLabel": "Quellversion", "sourceVersionPlaceholder": "2025-03", "launchJob": "Job starten", "launching": "Wird gestartet…" } },
+  "embeddings": { "title": "Embeddings", "tabs": { "configs": "Konfigurationen", "compute": "Berechnen" }, "configsTab": { "configs": "{count} Konfigurationen", "newConfig": "+ Neue Konfiguration", "cancel": "Abbrechen", "newConfigForm": { "title": "Neue Embedding-Konfiguration", "layerIndexingWarning": "Schicht-Indizierung — umgekehrte Konvention: 0 = letzte (semantischste) Schicht, 1 = vorletzte, usw.", "modelBackendLabel": "Modell-Backend", "modelBackendEsm": "esm — HuggingFace EsmModel (ESM-2)", "modelBackendEsm3c": "esm3c — ESM SDK ESMC (ESM3c) · FP16 auf GPU", "modelBackendT5": "t5 — HuggingFace T5EncoderModel (ProstT5…)", "modelBackendAuto": "auto — Rückfall auf esm", "modelLabel": "Modell", "customModelPlaceholder": "z.B. facebook/esm2_t33_650M_UR50D", "layerIndicesLabel": "Schichtindizes", "layerIndicesHelper": "(0 = letzte, 1 = vorletzte…)", "layerIndicesPlaceholder": "0  oder  0,1,2", "layerAggLabel": "Schicht-Aggregation", "layerAggMean": "mean — elementweiser Durchschnitt", "layerAggLast": "last — nur die letzte ausgewählte Schicht", "layerAggConcat": "concat — alle verketten (dim × n_layers)", "poolingLabel": "Sequenz-Pooling", "poolingMean": "mean — Mittelwert über Reste", "poolingMax": "max — Maximum über Reste", "poolingMeanMax": "mean_max — concat(mean, max) · dim × 2", "poolingCls": "cls — CLS/BOS-Token an Position 0", "maxLengthLabel": "Maximale Länge (Token)", "descriptionLabel": "Beschreibung (optional)", "normalizeResidues": "Reste normalisieren (L2 pro Rest vor Pooling)", "normalizeFinal": "Finales Embedding normalisieren (L2 nach Pooling)", "enableChunking": "Chunking aktivieren (lange Sequenzen → mehrere Embeddings pro Sequenz)", "chunkSizeLabel": "Chunk-Größe (Reste)", "chunkOverlapLabel": "Chunk-Überlappung (Reste)", "createConfig": "Konfiguration erstellen", "creating": "Wird erstellt…" }, "tableHeaders": { "description": "Beschreibung", "model": "Modell", "backend": "Backend", "layers": "Schichten", "agg": "Agg", "pool": "Pool", "norm": "Norm", "created": "Erstellt" }, "noConfigs": "Noch keine Embedding-Konfigurationen. Erstellen Sie eine", "deleteConfirm": "Diese Embedding-Konfiguration und ihre {count} gespeicherten Embeddings löschen? Dies kann nicht rückgängig gemacht werden.", "deleteConfirmNoEmbeddings": "Diese Embedding-Konfiguration löschen?" }, "computeTab": { "title": "Embeddings berechnen", "loading": "Lädt…", "configLabel": "Embedding-Konfiguration", "noConfigs": "— keine Konfigurationen verfügbar —", "querySetLabel": "Query-Set", "querySetHelper": "(optional — leer lassen für alle berechnen)", "allSequences": "— alle Sequenzen —", "queueBatchSizeLabel": "Warteschlangen-Batchgröße", "queueBatchSizeHelper": "(Seq/Job)", "modelBatchSizeLabel": "Modell-Batchgröße", "modelBatchSizeHelper": "(Seq/Vorwärtsdurchlauf)", "deviceLabel": "Gerät", "deviceCpu": "cpu — CPU (FP32)", "deviceCuda": "cuda — Standard-GPU (FP16 für ESM3c/T5)", "deviceCuda0": "cuda:0 — GPU 0", "deviceCuda1": "cuda:1 — GPU 1", "deviceCustom": "benutzerdefiniert…", "skipExisting": "Vorhandene Embeddings überspringen", "launchComputeJob": "Berechnungs-Job starten", "launching": "Wird gestartet…" } },
+  "functionalAnnotation": { "title": "Funktionale Annotation", "tabs": { "predict": "Annotation ausführen", "results": "Ergebnisse" }, "predictTab": { "title": "GO-Term-Annotation durch Embedding-Ähnlichkeit", "loading": "Lädt…", "configLabel": "Embedding-Konfiguration", "noConfigs": "— keine Konfigurationen verfügbar —", "querySetLabel": "Query-Set", "querySetHelper": "(optional — leer lassen für alle annotieren)", "allSequences": "— alle Sequenzen —", "annotationSetLabel": "Annotationsset", "noAnnotationSets": "— keine Annotationssets verfügbar —", "snapshotLabel": "Ontologie-Snapshot", "noSnapshots": "— keine Snapshots verfügbar —", "limitPerEntryLabel": "Limit pro Eintrag", "batchSizeLabel": "Batchgröße", "distanceThresholdLabel": "Distanzschwelle", "distanceThresholdHelper": "(optional)", "knnStrategy": "KNN-Strategie", "aspectSeparatedKnn": "Aspekt-getrennte KNN-Indizes", "aspectSeparatedKnnHelper": "Separate BPO / MFO / CCO Referenzindizes — verbessert die Trefferquote für jeden Aspekt unabhängig", "featureEngineering": "Feature Engineering", "featureEngineeringHelper": "(opt-in — erhöht Rechenzeit)", "sequenceAlignments": "Sequenzalignments", "sequenceAlignmentsHelper": "NW (global) + SW (lokal) via parasail/BLOSUM62", "taxonomicDistance": "Taxonomische Distanz", "taxonomicDistanceHelper": "LCA, Distanz und Relation via NCBI-Taxonomie", "searchBackend": "Such-Backend", "searchBackendLabel": "Backend", "numpyBackend": "numpy — exakt", "faissBackend": "faiss — indiziert", "metricLabel": "Metrik", "cosineSimilarity": "Kosinus", "euclideanDistance": "L2 (Euklidisch²)", "indexTypeLabel": "Indextyp", "flatIndex": "Flat — exakt", "ivfflatIndex": "IVFFlat — näherungsweise (>100K Referenzen)", "hnswIndex": "HNSW — näherungsweise, graphbasiert", "nlistLabel": "nlist", "nprobeLabel": "nprobe", "mLabel": "M", "efSearchLabel": "efSearch", "launchAnnotationJob": "Annotations-Job starten", "launching": "Wird gestartet…" }, "resultsTab": { "title": "Ergebnisse", "refresh": "Aktualisieren", "noResults": "Noch keine Annotationsergebnisse. Führen Sie einen Annotations-Job im Tab 'Annotation ausführen' durch.", "tableHeaders": { "id": "ID", "config": "Konfiguration", "annotationSet": "Annotationsset", "snapshot": "Snapshot", "goTerms": "GO-Terme", "distanceThreshold": "Dist.-Schwelle", "created": "Erstellt" }, "delete": "Löschen", "deleteConfirm": "Dieses Annotationsset und seine {count} GO-Term-Zuweisungen löschen? Dies kann nicht rückgängig gemacht werden.", "deleteConfirmNoAssignments": "Dieses Annotationsset löschen?" } },
+  "evaluation": { "title": "CAFA-Evaluierung", "generateSection": { "heading": "Neues Evaluierungsset", "description": "Berechnet das Delta zwischen zwei GOA-Releases.", "oldSetLabel": "Altes GOA-Set (Referenz)", "selectSet": "— auswählen —", "newSetLabel": "Neues GOA-Set (Grundwahrheit)", "errorSameSets": "Altes und neues Set müssen unterschiedlich sein.", "generateEvaluationSet": "Evaluierungsset generieren", "generating": "Wird in Warteschlange eingereiht…" }, "evaluationSetsSection": { "heading": "Evaluierungssets" }, "evaluationSetCard": { "deleteConfirm": "Dieses Evaluierungsset und alle seine Ergebnisse löschen?", "delete": "Löschen", "deltaProteins": "Delta-Proteine", "nkProteins": "NK-Proteine", "lkProteins": "LK-Proteine", "pkProteins": "PK-Proteine", "groundTruthFiles": "Grundwahrheitsdateien", "downloadNK": "NK", "downloadLK": "LK", "downloadPK": "PK", "downloadKnownTerms": "Bekannte Terme", "deltaProteinSequences": "Delta-Proteinsequenzen (FASTA)", "allDelta": "Alle Delta (NK+LK+PK)", "nkOnly": "Nur NK", "lkOnly": "Nur LK", "pkOnly": "Nur PK", "runCafaEvaluator": "CAFA-Evaluator ausführen", "predictionSetLabel": "Vorhersageset", "scoringConfigLabel": "Bewertungskonfiguration (optional)", "fallbackFormula": "— Rückfall (1−d/2) —", "maxDistanceLabel": "Maximale Distanz (optional)", "jobQueued": "Job in Warteschlange.", "viewJob": "Job anzeigen →", "runEvaluation": "Evaluierung ausführen (NK + LK + PK)", "resultsHeading": "Ergebnisse", "pollingResults": "● Abrufen", "refreshResults": "↻ Aktualisieren", "refreshing": "Wird aktualisiert…", "noEvaluations": "Noch keine Evaluierungen durchgeführt.", "predictionSet": "Vorhersageset:", "scoring": "Bewertung:", "artifactsDownload": "↓ Artefakte (.zip)", "resultDelete": "Löschen", "deleteResultConfirm": "Dieses Evaluierungsergebnis löschen?" }, "resultMetrics": { "nk": "NK", "lk": "LK", "pk": "PK", "biologicalProcess": "Biologischer Prozess", "molecularFunction": "Molekulare Funktion", "cellularComponent": "Zelluläre Komponente", "fmax": "Fmax", "precision": "Präzision", "recall": "Trefferquote", "coverage": "Abdeckung", "tau": "τ" }, "manualEvaluatorSection": { "heading": "Manueller Evaluator-Befehl" } },
+  "scoring": { "title": "Bewertungskonfigurationen", "description": "Eine ScoringConfig definiert, wie rohe Vorhersagesignale zu einem einzigen [0, 1]-Konfidenzwert kombiniert werden.", "loadPresets": "Voreinstellungen laden", "presetsLoading": "Lädt…", "availableSignals": "Verfügbare Signale", "signals": { "embeddingSimilarity": "Embedding-Ähnlichkeit", "embeddingSimilarityHint": "1 − Kosinus-Distanz / 2 — immer verfügbar.", "identityNw": "Identität NW", "identityNwHint": "Needleman-Wunsch globale Sequenzidentität [0, 1].", "identitySw": "Identität SW", "identitySwHint": "Smith-Waterman lokale Sequenzidentität [0, 1].", "evidenceWeight": "Evidenzgewicht", "evidenceWeightHint": "Qualität des GO-Evidenzcodes der Referenzannotation.", "taxonomicProximity": "Taxonomische Nähe", "taxonomicProximityHint": "1 / (1 + taxonomische_Distanz)" }, "configCard": { "deleteConfirm": "Bewertungskonfiguration \"{name}\" löschen?", "delete": "Löschen", "customEvidenceWeights": "benutzerdefinierte Evidenzgewichte", "evidenceCodeWeights": "Evidenzcode-Gewichte", "expand": "▶", "collapse": "▲", "systemDefaults": "(Systemstandards)", "custom": "(benutzerdefiniert)" }, "newConfigForm": { "newConfig": "+ Neue Bewertungskonfiguration", "formTitle": "Neue Konfiguration", "close": "×", "nameLabel": "Name", "namePlaceholder": "meine_konfiguration", "formulaLabel": "Formel", "linear": "linear", "evidenceWeighted": "evidence_weighted", "descriptionLabel": "Beschreibung", "descriptionHelper": "(optional)", "descriptionPlaceholder": "Wofür diese Konfiguration ist…", "signalWeights": "Signalgewichte", "evidenceCodeWeights": "Evidenzcode-Gewichte", "overrideCheckbox": "Pro-Evidenzcode-Qualitätsgewichte überschreiben", "systemDefaultsNote": "Systemstandards werden verwendet — EXP/IDA → 1,0 · ISS/IBA → 0,7 · IEA → 0,3 · ND → 0,1", "experimental": "Experimentell", "experimentalDescription": "Annotationen, die durch direkte experimentelle Evidenz gestützt werden. Höchste Vertrauensstufe.", "computational": "Rechnerisch / Phylogenetisch", "computationalDescription": "Annotationen abgeleitet aus Sequenzähnlichkeit, Orthologie oder phylogenetischer Inferenz.", "electronic": "Elektronisch", "electronicDescription": "Automatisierte Annotationen (IEA) oder nicht rückverfolgbare Autorenaussagen (NAS). Geringere Konfidenz.", "noData": "Keine Daten", "noDataDescription": "Platzhaltercode, der angibt, dass keine biologischen Daten verfügbar sind.", "off": "Aus", "max": "Max", "groupShortcut": "0,5", "resetEvidenceWeights": "Alle auf Systemstandards zurücksetzen", "saveConfig": "Konfiguration speichern", "saving": "Wird gespeichert…", "cancel": "Abbrechen" }, "noConfigs": "Noch keine Konfigurationen. Voreinstellungen laden oder oben eine erstellen." },
+  "querySets": { "title": "Query-Sets", "uploadFasta": "+ FASTA hochladen", "noQuerySets": "Noch keine Query-Sets.", "uploadFastaCta": "Laden Sie eine FASTA-Datei hoch, um zu beginnen", "tableHeaders": { "name": "Name", "sequences": "Sequenzen", "created": "Erstellt" }, "delete": "Löschen", "deleteConfirm": "Query-Set \"{name}\" löschen? Dies kann nicht rückgängig gemacht werden.", "uploadModal": { "title": "FASTA hochladen", "close": "×", "nameLabel": "Name", "nameRequired": "*", "namePlaceholder": "z.B. humane_neue_proteine", "descriptionLabel": "Beschreibung (optional)", "descriptionPlaceholder": "Kurze Beschreibung", "fastaFileLabel": "FASTA-Datei", "fastaFileRequired": "*", "dragDrop": "FASTA hier ablegen oder durchsuchen", "supportedFormats": ".fasta · .fa · .faa · .txt", "fileSelected": "✓", "clickToChange": "zum Ändern klicken", "cancel": "Abbrechen", "upload": "Hochladen", "uploading": "Wird hochgeladen…" }, "expandedEntries": { "title": "Einträge", "count": "({count})" } },
+  "support": { "title": "Unterstützung", "hero": { "supportCount": "{count} Personen unterstützen dieses Projekt", "withComments": "{count} mit Kommentaren", "anonymous": "{count} anonym" }, "commentsSection": { "heading": "Was die Leute sagen", "noComments": "Noch keine Kommentare. Seien Sie der Erste!" } },
+  "maintenance": { "title": "Wartung", "description": "Verwaiste Daten bereinigen. Alle Operationen können sicher ausgeführt werden, während das System aktiv ist.", "orphanSequences": { "title": "Verwaiste Sequenzen", "description": "Sequenzen ohne Protein und ohne QuerySet-Eintrag, der auf sie verweist.", "orphanLabel": "Verwaiste Sequenzen", "totalSequences": "Sequenzen gesamt", "referencedSequences": "Referenzierte Sequenzen", "clean": "Bereinigen", "toClean": "{count} zu bereinigen", "refresh": "Aktualisieren", "vacuum": "Vacuum" }, "unindexedEmbeddings": { "title": "Nicht indizierte Embeddings", "description": "Embeddings für Sequenzen, die nicht in der Protein-Referenzdatenbank enthalten sind.", "orphanLabel": "Nicht indizierte Embeddings", "totalEmbeddings": "Embeddings gesamt", "indexedEmbeddings": "Indizierte Embeddings", "clean": "Bereinigen", "toClean": "{count} zu bereinigen", "refresh": "Aktualisieren", "vacuum": "Vacuum", "cleaning": "Wird bereinigt…" } },
+  "components": { "resetDbButton": { "button": "DB zurücksetzen", "resetOk": "✓ Zurücksetzen OK", "error": "✗ Fehler", "confirmTitle": "Datenbank zurücksetzen?", "confirmMessage": "Dadurch werden alle Daten dauerhaft gelöscht: Proteine, Annotationen, Embeddings, Vorhersagen und Jobs. Diese Aktion kann nicht rückgängig gemacht werden.", "cancel": "Abbrechen", "confirm": "Ja, zurücksetzen", "confirming": "Wird zurückgesetzt…" }, "supportButton": { "support": "Unterstützen", "tooltip": "Kommentare und Metriken sind öffentlich.", "projectSupport": "Unterstützen Sie das Projekt!", "commentPlaceholder": "Kommentar hinterlassen (optional)…", "publicNote": "Kommentare und Metriken sind öffentlich.", "sendThumbsUp": "👍 Daumen hoch senden", "sending": "Wird gesendet…", "thanks": "Danke für die Unterstützung! 🎉", "recentComments": "Aktuelle Kommentare", "viewAll": "Alle anzeigen →" }, "usagePolicyModal": { "title": "Willkommen bei PROTEA", "subtitle": "Persönliche Rechenressourcen, offen geteilt — bitte lesen Sie dies, bevor Sie fortfahren", "intro": "Diese Plattform läuft auf persönlicher Hardware, die frei für Forschungszwecke geteilt wird.", "rule1": "Dies sind persönliche Ressourcen, die freiwillig geteilt werden. Bitte beachten Sie die von Ihnen erzeugte Last.", "rule2": "Bevor Sie schwere Jobs starten, nehmen Sie bitte zuerst Kontakt auf.", "rule3": "Alle verarbeiteten Daten sind öffentlich und offen.", "rule4": "PROTEA ist frei und Open Source. Quellcode auf GitHub.", "rule5": "Dieser Dienst läuft auf Best-Effort-Basis und kann ohne Vorankündigung abgeschaltet werden.", "rule6": "Wenn etwas nicht funktioniert, melden Sie es bitte.", "dataPublicNote": "Alle hier verarbeiteten Daten sind öffentlich. Danke für die verantwortungsvolle Nutzung.", "accept": "Verstanden, los geht's" }, "statusBadge": { "queued": "QUEUED", "running": "RUNNING", "succeeded": "SUCCEEDED", "failed": "FAILED", "cancelled": "CANCELLED" }, "eventTimeline": { "noEvents": "Noch keine Ereignisse." }, "languageSwitcher": { "label": "Sprache" } }
+}
\ No newline at end of file
diff --git a/apps/web/messages/en.json b/apps/web/messages/en.json
new file mode 100644
index 0000000..0621577
--- /dev/null
+++ b/apps/web/messages/en.json
@@ -0,0 +1,657 @@
+{
+  "layout": {
+    "title": "PROTEA",
+    "description": "Protein data platform — job queue and pipeline management"
+  },
+  "nav": {
+    "proteins": "Proteins",
+    "annotations": "Annotations",
+    "querySets": "Query Sets",
+    "embeddings": "Embeddings",
+    "functionalAnnotation": "Functional Annotation",
+    "scoring": "Scoring",
+    "evaluation": "Evaluation",
+    "jobs": "Jobs",
+    "maintenance": "Maintenance"
+  },
+  "jobs": {
+    "title": "Jobs",
+    "allStatuses": "All statuses",
+    "queued": "Queued",
+    "running": "Running",
+    "succeeded": "Succeeded",
+    "failed": "Failed",
+    "cancelled": "Cancelled",
+    "autoRefresh": "Auto-refresh",
+    "refresh": "Refresh",
+    "noJobsFound": "No jobs found.",
+    "status": "Status",
+    "operation": "Operation",
+    "jobId": "Job ID",
+    "created": "Created",
+    "activeJobs": "{count} active",
+    "jobDetail": {
+      "title": "Job Detail",
+      "backToJobs": "← Jobs",
+      "live": "Live",
+      "cancel": "Cancel",
+      "delete": "Delete",
+      "deleteConfirm": "Delete this job?",
+      "queue": "Queue:",
+      "created": "Created:",
+      "started": "Started:",
+      "finished": "Finished:",
+      "progress": "{current} / {total} {unit} ({percent}%)",
+      "payloadLabel": "Payload",
+      "childJobsTitle": "Child Jobs",
+      "childJobsCount": "({count})",
+      "eventsTitle": "Events",
+      "eventsCount": "({count})"
+    }
+  },
+  "proteins": {
+    "title": "Proteins",
+    "tabs": {
+      "browse": "Browse",
+      "stats": "Stats",
+      "insert": "Insert Proteins",
+      "metadata": "Fetch Metadata"
+    },
+    "browseTab": {
+      "searchPlaceholder": "accession, gene, organism…",
+      "search": "Search",
+      "clear": "Clear",
+      "allProteins": "All proteins",
+      "swissProt": "Swiss-Prot only",
+      "trembl": "TrEMBL only",
+      "canonicalOnly": "Canonical only",
+      "totalProteins": "{count} proteins",
+      "tableHeaders": {
+        "accession": "Accession",
+        "entryName": "Entry Name",
+        "gene": "Gene",
+        "organism": "Organism",
+        "length": "Length",
+        "source": "Source"
+      },
+      "noProteinsCta": "No proteins found. Use the Insert Proteins tab to import from UniProt.",
+      "pagination": {
+        "page": "Page {current} of {total}",
+        "previous": "Previous",
+        "next": "Next"
+      }
+    },
+    "statsTab": {
+      "refresh": "Refresh",
+      "loading": "Loading…",
+      "overview": "Overview",
+      "coverage": "Coverage",
+      "totalProteins": "Total Proteins",
+      "canonical": "Canonical",
+      "isoforms": "{count} isoforms",
+      "reviewed": "Swiss-Prot",
+      "reviewedSub": "reviewed",
+      "unreviewed": "TrEMBL",
+      "unreviewedSub": "unreviewed",
+      "withMetadata": "With Metadata",
+      "metadataSub": "{percent}% of canonical",
+      "withEmbeddings": "With Embeddings",
+      "embeddingsSub": "{percent}% of total",
+      "withGoAnnotations": "With GO Annotations",
+      "goAnnotationsSub": "{percent}% of total"
+    },
+    "insertTab": {
+      "title": "Insert Proteins from UniProt",
+      "description": "Downloads FASTA sequences and upserts Protein + Sequence rows.",
+      "searchCriteriaLabel": "Search criteria",
+      "searchCriteriaHelper": "UniProt query — reviewed:true = Swiss-Prot only",
+      "pageSizeLabel": "Page size",
+      "totalLimitLabel": "Total limit",
+      "totalLimitOptional": "(optional)",
+      "includeIsoforms": "Include isoforms",
+      "jobQueuedPrefix": "Job queued: ",
+      "launchJob": "Launch Job",
+      "launching": "Launching…"
+    },
+    "metadataTab": {
+      "title": "Fetch UniProt Metadata",
+      "description": "Downloads TSV annotations and upserts ProteinUniProtMetadata rows.",
+      "searchCriteriaLabel": "Search criteria",
+      "searchCriteriaHelper": "UniProt query — reviewed:true = Swiss-Prot only",
+      "pageSizeLabel": "Page size",
+      "totalLimitLabel": "Total limit",
+      "totalLimitOptional": "(optional)",
+      "launchJob": "Launch Job",
+      "launching": "Launching…"
+    },
+    "sourceSwissProt": "Swiss-Prot",
+    "sourceTrembl": "TrEMBL"
+  },
+  "proteinDetail": {
+    "backToProteins": "← Proteins",
+    "tabs": {
+      "overview": "Overview",
+      "annotations": "GO Annotations"
+    },
+    "overviewTab": {
+      "identity": "Identity",
+      "gene": "Gene",
+      "organism": "Organism",
+      "taxonId": "Taxon ID",
+      "length": "Length",
+      "aa": "aa",
+      "sequenceId": "Sequence ID",
+      "canonical": "Canonical",
+      "coverage": "Coverage",
+      "embeddings": "Embeddings",
+      "goAnnotations": "GO Annotations",
+      "metadata": "Metadata",
+      "yes": "yes",
+      "none": "none",
+      "isoforms": "Isoforms",
+      "function": "Function",
+      "biochemistry": "Biochemistry",
+      "ecNumber": "EC Number",
+      "catalyticActivity": "Catalytic Activity",
+      "cofactor": "Cofactor",
+      "activityRegulation": "Activity Regulation",
+      "pathway": "Pathway",
+      "absorption": "Absorption",
+      "kinetics": "Kinetics",
+      "phDependence": "pH Dependence",
+      "redoxPotential": "Redox Potential",
+      "temperatureDependence": "Temperature Dependence",
+      "rheaId": "Rhea ID",
+      "keywords": "Keywords",
+      "noFunctionalMetadata": "No functional metadata available. Use the Fetch Metadata tab to import from UniProt.",
+      "showGoGraph": "Show GO Graph",
+      "hideGoGraph": "Hide GO Graph",
+      "loadingGraph": "Loading graph…",
+      "noGoAnnotations": "No GO annotations found for this protein.",
+      "molecularFunction": "Molecular Function",
+      "biologicalProcess": "Biological Process",
+      "cellularComponent": "Cellular Component",
+      "annotations": "annotations",
+      "goTableHeaders": {
+        "goId": "GO ID",
+        "name": "Name",
+        "evidence": "Evidence",
+        "qualifier": "Qualifier",
+        "source": "Source"
+      }
+    }
+  },
+  "annotations": {
+    "title": "Annotations",
+    "tabs": {
+      "sets": "Annotation Sets",
+      "snapshots": "Ontology Snapshots",
+      "loadSnapshot": "Load Snapshot",
+      "loadGoa": "Load GOA",
+      "loadQuickgo": "Load QuickGO"
+    },
+    "setsTab": {
+      "annotationSets": "{count} annotation sets",
+      "refresh": "Refresh",
+      "noSetsFound": "No annotation sets yet. Load GO annotations from the Load GOA or Load QuickGO tabs.",
+      "tableHeaders": {
+        "id": "ID",
+        "source": "Source",
+        "version": "Version",
+        "annotations": "Annotations",
+        "meta": "Meta",
+        "created": "Created"
+      },
+      "delete": "Delete",
+      "deleteConfirm": "Delete this annotation set and its {count} GO annotations? This cannot be undone.",
+      "deleteConfirmNoAnnotations": "Delete this annotation set?"
+    },
+    "snapshotsTab": {
+      "snapshots": "{count} snapshots",
+      "refresh": "Refresh",
+      "noSnapshotsFound": "No ontology snapshots yet. Use the Load Snapshot tab.",
+      "tableHeaders": {
+        "id": "ID",
+        "version": "Version",
+        "goTerms": "GO Terms",
+        "iaUrl": "IA URL",
+        "loaded": "Loaded"
+      },
+      "notSet": "not set",
+      "save": "Save",
+      "cancel": "Cancel",
+      "editTooltip": "Tap to edit IA URL"
+    },
+    "loadSnapshotTab": {
+      "title": "Load Ontology Snapshot",
+      "description": "Downloads a GO OBO file and populates GOTerm rows.",
+      "oboUrlLabel": "OBO URL",
+      "launchJob": "Launch Job",
+      "launching": "Launching…"
+    },
+    "loadGoaTab": {
+      "title": "Load GOA Annotations",
+      "description": "Bulk-loads GO annotations from a GAF file.",
+      "snapshotLabel": "Ontology Snapshot",
+      "selectSnapshot": "— select snapshot —",
+      "noSnapshots": "No snapshots — run Load Snapshot first.",
+      "gafUrlLabel": "GAF URL",
+      "gafUrlPlaceholder": "https://current.geneontology.org/annotations/goa_human.gaf.gz",
+      "sourceVersionLabel": "Source version",
+      "sourceVersionPlaceholder": "2025-03",
+      "launchJob": "Launch Job",
+      "launching": "Launching…"
+    },
+    "loadQuickgoTab": {
+      "title": "Load QuickGO Annotations",
+      "description": "Streams GO annotations from the QuickGO bulk download API.",
+      "snapshotLabel": "Ontology Snapshot",
+      "selectSnapshot": "— select snapshot —",
+      "noSnapshots": "No snapshots — run Load Snapshot first.",
+      "sourceVersionLabel": "Source version",
+      "sourceVersionPlaceholder": "2025-03",
+      "launchJob": "Launch Job",
+      "launching": "Launching…"
+    }
+  },
+  "embeddings": {
+    "title": "Embeddings",
+    "tabs": {
+      "configs": "Configs",
+      "compute": "Compute"
+    },
+    "configsTab": {
+      "configs": "{count} configs",
+      "newConfig": "+ New Config",
+      "cancel": "Cancel",
+      "newConfigForm": {
+        "title": "New Embedding Config",
+        "layerIndexingWarning": "Layer indexing — reverse convention: 0 = last (most semantic) layer, 1 = penultimate, etc.",
+        "modelBackendLabel": "Model Backend",
+        "modelBackendEsm": "esm — HuggingFace EsmModel (ESM-2)",
+        "modelBackendEsm3c": "esm3c — ESM SDK ESMC (ESM3c) · FP16 on GPU",
+        "modelBackendT5": "t5 — HuggingFace T5EncoderModel (ProstT5…)",
+        "modelBackendAuto": "auto — falls back to esm",
+        "modelLabel": "Model",
+        "customModelPlaceholder": "e.g. facebook/esm2_t33_650M_UR50D",
+        "layerIndicesLabel": "Layer Indices",
+        "layerIndicesHelper": "(0 = last, 1 = penultimate…)",
+        "layerIndicesPlaceholder": "0  or  0,1,2",
+        "layerAggLabel": "Layer Aggregation",
+        "layerAggMean": "mean — element-wise average",
+        "layerAggLast": "last — only the last selected layer",
+        "layerAggConcat": "concat — concatenate all (dim × n_layers)",
+        "poolingLabel": "Sequence Pooling",
+        "poolingMean": "mean — mean over residues",
+        "poolingMax": "max — max over residues",
+        "poolingMeanMax": "mean_max — concat(mean, max) · dim × 2",
+        "poolingCls": "cls — CLS/BOS token at position 0",
+        "maxLengthLabel": "Max Length (tokens)",
+        "descriptionLabel": "Description (optional)",
+        "normalizeResidues": "Normalize residues (L2 per-residue before pooling)",
+        "normalizeFinal": "Normalize final embedding (L2 after pooling)",
+        "enableChunking": "Enable chunking (long sequences → multiple embeddings per sequence)",
+        "chunkSizeLabel": "Chunk Size (residues)",
+        "chunkOverlapLabel": "Chunk Overlap (residues)",
+        "createConfig": "Create Config",
+        "creating": "Creating…"
+      },
+      "tableHeaders": {
+        "description": "Description",
+        "model": "Model",
+        "backend": "Backend",
+        "layers": "Layers",
+        "agg": "Agg",
+        "pool": "Pool",
+        "norm": "Norm",
+        "created": "Created"
+      },
+      "noConfigs": "No embedding configs yet. Create one",
+      "deleteConfirm": "Delete this embedding config and its {count} stored embeddings? This cannot be undone.",
+      "deleteConfirmNoEmbeddings": "Delete this embedding config?"
+    },
+    "computeTab": {
+      "title": "Compute Embeddings",
+      "loading": "Loading…",
+      "configLabel": "Embedding Config",
+      "noConfigs": "— no configs available —",
+      "querySetLabel": "Query Set",
+      "querySetHelper": "(optional — leave empty to compute all)",
+      "allSequences": "— all sequences —",
+      "queueBatchSizeLabel": "Queue Batch Size",
+      "queueBatchSizeHelper": "(seqs/job)",
+      "modelBatchSizeLabel": "Model Batch Size",
+      "modelBatchSizeHelper": "(seqs/forward)",
+      "deviceLabel": "Device",
+      "deviceCpu": "cpu — CPU (FP32)",
+      "deviceCuda": "cuda — GPU default (FP16 for ESM3c/T5)",
+      "deviceCuda0": "cuda:0 — GPU 0",
+      "deviceCuda1": "cuda:1 — GPU 1",
+      "deviceCustom": "custom…",
+      "skipExisting": "Skip existing embeddings",
+      "launchComputeJob": "Launch Compute Job",
+      "launching": "Launching…"
+    }
+  },
+  "functionalAnnotation": {
+    "title": "Functional Annotation",
+    "tabs": {
+      "predict": "Run Annotation",
+      "results": "Results"
+    },
+    "predictTab": {
+      "title": "GO Term Annotation by Embedding Similarity",
+      "loading": "Loading…",
+      "configLabel": "Embedding Config",
+      "noConfigs": "— no configs available —",
+      "querySetLabel": "Query Set",
+      "querySetHelper": "(optional — leave empty to annotate all)",
+      "allSequences": "— all sequences —",
+      "annotationSetLabel": "Annotation Set",
+      "noAnnotationSets": "— no annotation sets available —",
+      "snapshotLabel": "Ontology Snapshot",
+      "noSnapshots": "— no snapshots available —",
+      "limitPerEntryLabel": "Limit per Entry",
+      "batchSizeLabel": "Batch Size",
+      "distanceThresholdLabel": "Distance Threshold",
+      "distanceThresholdHelper": "(optional)",
+      "knnStrategy": "KNN Strategy",
+      "aspectSeparatedKnn": "Per-aspect KNN indices",
+      "aspectSeparatedKnnHelper": "Separate BPO / MFO / CCO reference indices — improves recall for each aspect independently",
+      "featureEngineering": "Feature Engineering",
+      "featureEngineeringHelper": "(opt-in — adds compute time)",
+      "sequenceAlignments": "Sequence alignments",
+      "sequenceAlignmentsHelper": "NW (global) + SW (local) via parasail/BLOSUM62",
+      "taxonomicDistance": "Taxonomic distance",
+      "taxonomicDistanceHelper": "LCA, distance and relation via NCBI taxonomy",
+      "searchBackend": "Search Backend",
+      "searchBackendLabel": "Backend",
+      "numpyBackend": "numpy — exact",
+      "faissBackend": "faiss — indexed",
+      "metricLabel": "Metric",
+      "cosineSimilarity": "cosine",
+      "euclideanDistance": "L2 (Euclidean²)",
+      "indexTypeLabel": "Index Type",
+      "flatIndex": "Flat — exact",
+      "ivfflatIndex": "IVFFlat — approximate (>100K refs)",
+      "hnswIndex": "HNSW — approximate, graph-based",
+      "nlistLabel": "nlist",
+      "nprobeLabel": "nprobe",
+      "mLabel": "M",
+      "efSearchLabel": "efSearch",
+      "launchAnnotationJob": "Launch Annotation Job",
+      "launching": "Launching…"
+    },
+    "resultsTab": {
+      "title": "Results",
+      "refresh": "Refresh",
+      "noResults": "No annotation results yet. Run an annotation job from the Run Annotation tab.",
+      "tableHeaders": {
+        "id": "ID",
+        "config": "Config",
+        "annotationSet": "Annotation Set",
+        "snapshot": "Snapshot",
+        "goTerms": "GO Terms",
+        "distanceThreshold": "Dist. Threshold",
+        "created": "Created"
+      },
+      "delete": "Delete",
+      "deleteConfirm": "Delete this annotation set and its {count} GO term assignments? This cannot be undone.",
+      "deleteConfirmNoAssignments": "Delete this annotation set?"
+    }
+  },
+  "evaluation": {
+    "title": "CAFA Evaluation",
+    "generateSection": {
+      "heading": "New Evaluation Set",
+      "description": "Computes the delta between two GOA releases. Applies experimental evidence filtering and NOT-qualifier propagation through the GO DAG.",
+      "oldSetLabel": "Old GOA set (reference)",
+      "selectSet": "— select —",
+      "newSetLabel": "New GOA set (ground truth)",
+      "errorSameSets": "Old and new sets must be different.",
+      "generateEvaluationSet": "Generate Evaluation Set",
+      "generating": "Queuing…"
+    },
+    "evaluationSetsSection": {
+      "heading": "Evaluation Sets"
+    },
+    "evaluationSetCard": {
+      "deleteConfirm": "Delete this evaluation set and all its results?",
+      "delete": "Delete",
+      "deltaProteins": "Delta proteins",
+      "nkProteins": "NK proteins",
+      "lkProteins": "LK proteins",
+      "pkProteins": "PK proteins",
+      "groundTruthFiles": "Ground truth files",
+      "downloadNK": "NK",
+      "downloadLK": "LK",
+      "downloadPK": "PK",
+      "downloadKnownTerms": "Known terms",
+      "deltaProteinSequences": "Delta protein sequences (FASTA)",
+      "allDelta": "All delta (NK+LK+PK)",
+      "nkOnly": "NK only",
+      "lkOnly": "LK only",
+      "pkOnly": "PK only",
+      "runCafaEvaluator": "Run CAFA evaluator",
+      "predictionSetLabel": "Prediction set",
+      "scoringConfigLabel": "Scoring config (optional)",
+      "fallbackFormula": "— fallback (1−d/2) —",
+      "maxDistanceLabel": "Max distance (optional)",
+      "jobQueued": "Job queued.",
+      "viewJob": "View job →",
+      "runEvaluation": "Run Evaluation (NK + LK + PK)",
+      "resultsHeading": "Results",
+      "pollingResults": "● polling",
+      "refreshResults": "↻ Refresh",
+      "refreshing": "Refreshing…",
+      "noEvaluations": "No evaluations run yet.",
+      "predictionSet": "Prediction set:",
+      "scoring": "Scoring:",
+      "artifactsDownload": "↓ Artifacts (.zip)",
+      "resultDelete": "Delete",
+      "deleteResultConfirm": "Delete this evaluation result?"
+    },
+    "resultMetrics": {
+      "nk": "NK",
+      "lk": "LK",
+      "pk": "PK",
+      "biologicalProcess": "Biological Process",
+      "molecularFunction": "Molecular Function",
+      "cellularComponent": "Cellular Component",
+      "fmax": "Fmax",
+      "precision": "Precision",
+      "recall": "Recall",
+      "coverage": "Coverage",
+      "tau": "τ"
+    },
+    "manualEvaluatorSection": {
+      "heading": "Manual evaluator command"
+    }
+  },
+  "scoring": {
+    "title": "Scoring Configs",
+    "description": "A ScoringConfig defines how raw prediction signals are combined into a single [0, 1] confidence score — without re-running the KNN pipeline.",
+    "loadPresets": "Load presets",
+    "presetsLoading": "Loading…",
+    "availableSignals": "Available signals",
+    "signals": {
+      "embeddingSimilarity": "Embedding similarity",
+      "embeddingSimilarityHint": "1 − cosine_distance / 2 — always available.",
+      "identityNw": "Identity NW",
+      "identityNwHint": "Needleman-Wunsch global sequence identity [0, 1].",
+      "identitySw": "Identity SW",
+      "identitySwHint": "Smith-Waterman local sequence identity [0, 1].",
+      "evidenceWeight": "Evidence weight",
+      "evidenceWeightHint": "Quality of the reference annotation's GO evidence code.",
+      "taxonomicProximity": "Taxonomic proximity",
+      "taxonomicProximityHint": "1 / (1 + taxonomic_distance)"
+    },
+    "configCard": {
+      "deleteConfirm": "Delete scoring config \"{name}\"?",
+      "delete": "Delete",
+      "customEvidenceWeights": "custom evidence weights",
+      "evidenceCodeWeights": "Evidence-code weights",
+      "expand": "▶",
+      "collapse": "▲",
+      "systemDefaults": "(system defaults)",
+      "custom": "(custom)"
+    },
+    "newConfigForm": {
+      "newConfig": "+ New scoring config",
+      "formTitle": "New config",
+      "close": "×",
+      "nameLabel": "Name",
+      "namePlaceholder": "my_config",
+      "formulaLabel": "Formula",
+      "linear": "linear",
+      "evidenceWeighted": "evidence_weighted",
+      "descriptionLabel": "Description",
+      "descriptionHelper": "(optional)",
+      "descriptionPlaceholder": "What this config is for…",
+      "signalWeights": "Signal weights",
+      "evidenceCodeWeights": "Evidence-code weights",
+      "overrideCheckbox": "Override per-evidence-code quality weights",
+      "systemDefaultsNote": "Using system defaults — EXP/IDA → 1.0 · ISS/IBA → 0.7 · IEA → 0.3 · ND → 0.1",
+      "experimental": "Experimental",
+      "experimentalDescription": "Annotations backed by direct experimental evidence. Highest confidence tier.",
+      "computational": "Computational / Phylogenetic",
+      "computationalDescription": "Annotations derived from sequence similarity, orthology, or phylogenetic inference.",
+      "electronic": "Electronic",
+      "electronicDescription": "Automated annotations (IEA) or non-traceable author statements (NAS). Lower confidence.",
+      "noData": "No data",
+      "noDataDescription": "Placeholder code indicating no biological data is available.",
+      "off": "Off",
+      "max": "Max",
+      "groupShortcut": "0.5",
+      "resetEvidenceWeights": "Reset all to system defaults",
+      "saveConfig": "Save config",
+      "saving": "Saving…",
+      "cancel": "Cancel"
+    },
+    "noConfigs": "No configs yet. Load the presets or create one above."
+  },
+  "querySets": {
+    "title": "Query Sets",
+    "uploadFasta": "+ Upload FASTA",
+    "noQuerySets": "No query sets yet.",
+    "uploadFastaCta": "Upload a FASTA file to get started",
+    "tableHeaders": {
+      "name": "Name",
+      "sequences": "Sequences",
+      "created": "Created"
+    },
+    "delete": "Delete",
+    "deleteConfirm": "Delete query set \"{name}\"? This cannot be undone.",
+    "uploadModal": {
+      "title": "Upload FASTA",
+      "close": "×",
+      "nameLabel": "Name",
+      "nameRequired": "*",
+      "namePlaceholder": "e.g. human_novel_proteins",
+      "descriptionLabel": "Description (optional)",
+      "descriptionPlaceholder": "Short description",
+      "fastaFileLabel": "FASTA file",
+      "fastaFileRequired": "*",
+      "dragDrop": "Drop FASTA here or browse",
+      "supportedFormats": ".fasta · .fa · .faa · .txt",
+      "fileSelected": "✓",
+      "clickToChange": "click to change",
+      "cancel": "Cancel",
+      "upload": "Upload",
+      "uploading": "Uploading…"
+    },
+    "expandedEntries": {
+      "title": "Entries",
+      "count": "({count})"
+    }
+  },
+  "support": {
+    "title": "Support",
+    "hero": {
+      "supportCount": "{count} people support this project",
+      "withComments": "{count} with comments",
+      "anonymous": "{count} anonymous"
+    },
+    "commentsSection": {
+      "heading": "What people are saying",
+      "noComments": "No comments yet. Be the first!"
+    }
+  },
+  "maintenance": {
+    "title": "Maintenance",
+    "description": "Clean up orphaned data that accumulates over time. All operations are safe to run while the system is active.",
+    "orphanSequences": {
+      "title": "Orphan Sequences",
+      "description": "Sequences with no Protein and no QuerySet entry pointing to them.",
+      "orphanLabel": "Orphan sequences",
+      "totalSequences": "Total sequences",
+      "referencedSequences": "Referenced sequences",
+      "clean": "Clean",
+      "toClean": "{count} to clean",
+      "refresh": "Refresh",
+      "vacuum": "Vacuum"
+    },
+    "unindexedEmbeddings": {
+      "title": "Unindexed Embeddings",
+      "description": "Embeddings for sequences not in the protein reference database.",
+      "orphanLabel": "Unindexed embeddings",
+      "totalEmbeddings": "Total embeddings",
+      "indexedEmbeddings": "Indexed embeddings",
+      "clean": "Clean",
+      "toClean": "{count} to clean",
+      "refresh": "Refresh",
+      "vacuum": "Vacuum",
+      "cleaning": "Cleaning…"
+    }
+  },
+  "components": {
+    "resetDbButton": {
+      "button": "Reset DB",
+      "resetOk": "✓ Reset OK",
+      "error": "✗ Error",
+      "confirmTitle": "Reset database?",
+      "confirmMessage": "This will permanently delete all data: proteins, annotations, embeddings, predictions and jobs. This action cannot be undone.",
+      "cancel": "Cancel",
+      "confirm": "Yes, reset",
+      "confirming": "Resetting…"
+    },
+    "supportButton": {
+      "support": "Support",
+      "tooltip": "Comments and metrics are public and visible to all visitors.",
+      "projectSupport": "Support the project!",
+      "commentPlaceholder": "Leave a comment (optional)…",
+      "publicNote": "Comments and metrics are public.",
+      "sendThumbsUp": "👍 Send thumbs up",
+      "sending": "Sending…",
+      "thanks": "Thanks for the support! 🎉",
+      "recentComments": "Recent comments",
+      "viewAll": "View all →"
+    },
+    "usagePolicyModal": {
+      "title": "Welcome to PROTEA",
+      "subtitle": "Personal compute resources, openly shared — please read before continuing",
+      "intro": "This platform runs on personal hardware shared freely for research purposes. No registration or authorization is required. Please keep the following in mind:",
+      "rule1": "These are personal resources shared voluntarily. Please be mindful of the load you generate.",
+      "rule2": "Before launching heavy or long-running jobs, reach out first — a quick message explaining your use case is much appreciated.",
+      "rule3": "All processed data is public and open. Feel free to use and share results.",
+      "rule4": "PROTEA is free and open source. Any individual or research institution can deploy their own instance — source code on GitHub.",
+      "rule5": "This service runs on a best-effort basis. The system may be taken down for maintenance or personal use at any time without prior notice.",
+      "rule6": "If something breaks or behaves unexpectedly, please report it. Bug reports and feedback are genuinely appreciated.",
+      "dataPublicNote": "All data processed here is public. Thank you for using this responsibly.",
+      "accept": "Got it, let's go"
+    },
+    "statusBadge": {
+      "queued": "QUEUED",
+      "running": "RUNNING",
+      "succeeded": "SUCCEEDED",
+      "failed": "FAILED",
+      "cancelled": "CANCELLED"
+    },
+    "eventTimeline": {
+      "noEvents": "No events yet."
+    },
+    "languageSwitcher": {
+      "label": "Language"
+    }
+  }
+}
diff --git a/apps/web/messages/es.json b/apps/web/messages/es.json
new file mode 100644
index 0000000..d34cc45
--- /dev/null
+++ b/apps/web/messages/es.json
@@ -0,0 +1,657 @@
+{
+  "layout": {
+    "title": "PROTEA",
+    "description": "Plataforma de datos proteicos — cola de trabajos y gestión de pipelines"
+  },
+  "nav": {
+    "proteins": "Proteínas",
+    "annotations": "Anotaciones",
+    "querySets": "Conjuntos de Consulta",
+    "embeddings": "Embeddings",
+    "functionalAnnotation": "Anotación Funcional",
+    "scoring": "Scoring",
+    "evaluation": "Evaluación",
+    "jobs": "Trabajos",
+    "maintenance": "Mantenimiento"
+  },
+  "jobs": {
+    "title": "Trabajos",
+    "allStatuses": "Todos los estados",
+    "queued": "En cola",
+    "running": "En ejecución",
+    "succeeded": "Completado",
+    "failed": "Fallido",
+    "cancelled": "Cancelado",
+    "autoRefresh": "Actualización automática",
+    "refresh": "Actualizar",
+    "noJobsFound": "No se encontraron trabajos.",
+    "status": "Estado",
+    "operation": "Operación",
+    "jobId": "ID del trabajo",
+    "created": "Creado",
+    "activeJobs": "{count} activos",
+    "jobDetail": {
+      "title": "Detalle del trabajo",
+      "backToJobs": "← Trabajos",
+      "live": "En vivo",
+      "cancel": "Cancelar",
+      "delete": "Eliminar",
+      "deleteConfirm": "¿Eliminar este trabajo?",
+      "queue": "Cola:",
+      "created": "Creado:",
+      "started": "Iniciado:",
+      "finished": "Finalizado:",
+      "progress": "{current} / {total} {unit} ({percent}%)",
+      "payloadLabel": "Payload",
+      "childJobsTitle": "Trabajos hijos",
+      "childJobsCount": "({count})",
+      "eventsTitle": "Eventos",
+      "eventsCount": "({count})"
+    }
+  },
+  "proteins": {
+    "title": "Proteínas",
+    "tabs": {
+      "browse": "Explorar",
+      "stats": "Estadísticas",
+      "insert": "Insertar proteínas",
+      "metadata": "Obtener metadatos"
+    },
+    "browseTab": {
+      "searchPlaceholder": "accesión, gen, organismo…",
+      "search": "Buscar",
+      "clear": "Limpiar",
+      "allProteins": "Todas las proteínas",
+      "swissProt": "Solo Swiss-Prot",
+      "trembl": "Solo TrEMBL",
+      "canonicalOnly": "Solo canónicas",
+      "totalProteins": "{count} proteínas",
+      "tableHeaders": {
+        "accession": "Accesión",
+        "entryName": "Nombre de entrada",
+        "gene": "Gen",
+        "organism": "Organismo",
+        "length": "Longitud",
+        "source": "Fuente"
+      },
+      "noProteinsCta": "No se encontraron proteínas. Use la pestaña Insertar proteínas para importar desde UniProt.",
+      "pagination": {
+        "page": "Página {current} de {total}",
+        "previous": "Anterior",
+        "next": "Siguiente"
+      }
+    },
+    "statsTab": {
+      "refresh": "Actualizar",
+      "loading": "Cargando…",
+      "overview": "Resumen",
+      "coverage": "Cobertura",
+      "totalProteins": "Total de proteínas",
+      "canonical": "Canónicas",
+      "isoforms": "{count} isoformas",
+      "reviewed": "Swiss-Prot",
+      "reviewedSub": "revisadas",
+      "unreviewed": "TrEMBL",
+      "unreviewedSub": "no revisadas",
+      "withMetadata": "Con metadatos",
+      "metadataSub": "{percent}% de canónicas",
+      "withEmbeddings": "Con embeddings",
+      "embeddingsSub": "{percent}% del total",
+      "withGoAnnotations": "Con anotaciones GO",
+      "goAnnotationsSub": "{percent}% del total"
+    },
+    "insertTab": {
+      "title": "Insertar proteínas desde UniProt",
+      "description": "Descarga secuencias FASTA e inserta filas Protein + Sequence.",
+      "searchCriteriaLabel": "Criterios de búsqueda",
+      "searchCriteriaHelper": "Consulta UniProt — reviewed:true = solo Swiss-Prot",
+      "pageSizeLabel": "Tamaño de página",
+      "totalLimitLabel": "Límite total",
+      "totalLimitOptional": "(opcional)",
+      "includeIsoforms": "Incluir isoformas",
+      "jobQueuedPrefix": "Trabajo en cola: ",
+      "launchJob": "Lanzar trabajo",
+      "launching": "Lanzando…"
+    },
+    "metadataTab": {
+      "title": "Obtener metadatos de UniProt",
+      "description": "Descarga anotaciones TSV e inserta filas ProteinUniProtMetadata.",
+      "searchCriteriaLabel": "Criterios de búsqueda",
+      "searchCriteriaHelper": "Consulta UniProt — reviewed:true = solo Swiss-Prot",
+      "pageSizeLabel": "Tamaño de página",
+      "totalLimitLabel": "Límite total",
+      "totalLimitOptional": "(opcional)",
+      "launchJob": "Lanzar trabajo",
+      "launching": "Lanzando…"
+    },
+    "sourceSwissProt": "Swiss-Prot",
+    "sourceTrembl": "TrEMBL"
+  },
+  "proteinDetail": {
+    "backToProteins": "← Proteínas",
+    "tabs": {
+      "overview": "Resumen",
+      "annotations": "Anotaciones GO"
+    },
+    "overviewTab": {
+      "identity": "Identidad",
+      "gene": "Gen",
+      "organism": "Organismo",
+      "taxonId": "ID de taxón",
+      "length": "Longitud",
+      "aa": "aa",
+      "sequenceId": "ID de secuencia",
+      "canonical": "Canónica",
+      "coverage": "Cobertura",
+      "embeddings": "Embeddings",
+      "goAnnotations": "Anotaciones GO",
+      "metadata": "Metadatos",
+      "yes": "sí",
+      "none": "ninguno",
+      "isoforms": "Isoformas",
+      "function": "Función",
+      "biochemistry": "Bioquímica",
+      "ecNumber": "Número EC",
+      "catalyticActivity": "Actividad catalítica",
+      "cofactor": "Cofactor",
+      "activityRegulation": "Regulación de actividad",
+      "pathway": "Ruta metabólica",
+      "absorption": "Absorción",
+      "kinetics": "Cinética",
+      "phDependence": "Dependencia de pH",
+      "redoxPotential": "Potencial redox",
+      "temperatureDependence": "Dependencia de temperatura",
+      "rheaId": "ID Rhea",
+      "keywords": "Palabras clave",
+      "noFunctionalMetadata": "No hay metadatos funcionales disponibles. Use la pestaña Obtener metadatos para importar desde UniProt.",
+      "showGoGraph": "Mostrar grafo GO",
+      "hideGoGraph": "Ocultar grafo GO",
+      "loadingGraph": "Cargando grafo…",
+      "noGoAnnotations": "No se encontraron anotaciones GO para esta proteína.",
+      "molecularFunction": "Función molecular",
+      "biologicalProcess": "Proceso biológico",
+      "cellularComponent": "Componente celular",
+      "annotations": "anotaciones",
+      "goTableHeaders": {
+        "goId": "GO ID",
+        "name": "Nombre",
+        "evidence": "Evidencia",
+        "qualifier": "Calificador",
+        "source": "Fuente"
+      }
+    }
+  },
+  "annotations": {
+    "title": "Anotaciones",
+    "tabs": {
+      "sets": "Conjuntos de anotaciones",
+      "snapshots": "Instantáneas de ontología",
+      "loadSnapshot": "Cargar instantánea",
+      "loadGoa": "Cargar GOA",
+      "loadQuickgo": "Cargar QuickGO"
+    },
+    "setsTab": {
+      "annotationSets": "{count} conjuntos de anotaciones",
+      "refresh": "Actualizar",
+      "noSetsFound": "No hay conjuntos de anotaciones aún. Cargue anotaciones GO desde las pestañas Cargar GOA o Cargar QuickGO.",
+      "tableHeaders": {
+        "id": "ID",
+        "source": "Fuente",
+        "version": "Versión",
+        "annotations": "Anotaciones",
+        "meta": "Meta",
+        "created": "Creado"
+      },
+      "delete": "Eliminar",
+      "deleteConfirm": "¿Eliminar este conjunto de anotaciones y sus {count} anotaciones GO? Esta acción no se puede deshacer.",
+      "deleteConfirmNoAnnotations": "¿Eliminar este conjunto de anotaciones?"
+    },
+    "snapshotsTab": {
+      "snapshots": "{count} instantáneas",
+      "refresh": "Actualizar",
+      "noSnapshotsFound": "No hay instantáneas de ontología aún. Use la pestaña Cargar instantánea.",
+      "tableHeaders": {
+        "id": "ID",
+        "version": "Versión",
+        "goTerms": "Términos GO",
+        "iaUrl": "URL IA",
+        "loaded": "Cargado"
+      },
+      "notSet": "no configurado",
+      "save": "Guardar",
+      "cancel": "Cancelar",
+      "editTooltip": "Toca para editar la URL IA"
+    },
+    "loadSnapshotTab": {
+      "title": "Cargar instantánea de ontología",
+      "description": "Descarga un archivo GO OBO y rellena las filas GOTerm.",
+      "oboUrlLabel": "URL OBO",
+      "launchJob": "Lanzar trabajo",
+      "launching": "Lanzando…"
+    },
+    "loadGoaTab": {
+      "title": "Cargar anotaciones GOA",
+      "description": "Carga masiva de anotaciones GO desde un archivo GAF.",
+      "snapshotLabel": "Instantánea de ontología",
+      "selectSnapshot": "— seleccionar instantánea —",
+      "noSnapshots": "Sin instantáneas — ejecute primero Cargar instantánea.",
+      "gafUrlLabel": "URL GAF",
+      "gafUrlPlaceholder": "https://current.geneontology.org/annotations/goa_human.gaf.gz",
+      "sourceVersionLabel": "Versión de fuente",
+      "sourceVersionPlaceholder": "2025-03",
+      "launchJob": "Lanzar trabajo",
+      "launching": "Lanzando…"
+    },
+    "loadQuickgoTab": {
+      "title": "Cargar anotaciones QuickGO",
+      "description": "Transmite anotaciones GO desde la API de descarga masiva de QuickGO.",
+      "snapshotLabel": "Instantánea de ontología",
+      "selectSnapshot": "— seleccionar instantánea —",
+      "noSnapshots": "Sin instantáneas — ejecute primero Cargar instantánea.",
+      "sourceVersionLabel": "Versión de fuente",
+      "sourceVersionPlaceholder": "2025-03",
+      "launchJob": "Lanzar trabajo",
+      "launching": "Lanzando…"
+    }
+  },
+  "embeddings": {
+    "title": "Embeddings",
+    "tabs": {
+      "configs": "Configuraciones",
+      "compute": "Calcular"
+    },
+    "configsTab": {
+      "configs": "{count} configuraciones",
+      "newConfig": "+ Nueva configuración",
+      "cancel": "Cancelar",
+      "newConfigForm": {
+        "title": "Nueva configuración de embedding",
+        "layerIndexingWarning": "Indexación de capas — convención inversa: 0 = última capa (más semántica), 1 = penúltima, etc.",
+        "modelBackendLabel": "Backend del modelo",
+        "modelBackendEsm": "esm — HuggingFace EsmModel (ESM-2)",
+        "modelBackendEsm3c": "esm3c — ESM SDK ESMC (ESM3c) · FP16 en GPU",
+        "modelBackendT5": "t5 — HuggingFace T5EncoderModel (ProstT5…)",
+        "modelBackendAuto": "auto — recurre a esm",
+        "modelLabel": "Modelo",
+        "customModelPlaceholder": "p.ej. facebook/esm2_t33_650M_UR50D",
+        "layerIndicesLabel": "Índices de capa",
+        "layerIndicesHelper": "(0 = última, 1 = penúltima…)",
+        "layerIndicesPlaceholder": "0  o  0,1,2",
+        "layerAggLabel": "Agregación de capas",
+        "layerAggMean": "mean — promedio elemento a elemento",
+        "layerAggLast": "last — solo la última capa seleccionada",
+        "layerAggConcat": "concat — concatenar todas (dim × n_capas)",
+        "poolingLabel": "Pooling de secuencia",
+        "poolingMean": "mean — media sobre residuos",
+        "poolingMax": "max — máximo sobre residuos",
+        "poolingMeanMax": "mean_max — concat(mean, max) · dim × 2",
+        "poolingCls": "cls — token CLS/BOS en posición 0",
+        "maxLengthLabel": "Longitud máxima (tokens)",
+        "descriptionLabel": "Descripción (opcional)",
+        "normalizeResidues": "Normalizar residuos (L2 por residuo antes del pooling)",
+        "normalizeFinal": "Normalizar embedding final (L2 tras el pooling)",
+        "enableChunking": "Activar chunking (secuencias largas → múltiples embeddings por secuencia)",
+        "chunkSizeLabel": "Tamaño de chunk (residuos)",
+        "chunkOverlapLabel": "Solapamiento de chunk (residuos)",
+        "createConfig": "Crear configuración",
+        "creating": "Creando…"
+      },
+      "tableHeaders": {
+        "description": "Descripción",
+        "model": "Modelo",
+        "backend": "Backend",
+        "layers": "Capas",
+        "agg": "Agg",
+        "pool": "Pool",
+        "norm": "Norm",
+        "created": "Creado"
+      },
+      "noConfigs": "Sin configuraciones de embedding aún. Cree una",
+      "deleteConfirm": "¿Eliminar esta configuración de embedding y sus {count} embeddings almacenados? Esta acción no se puede deshacer.",
+      "deleteConfirmNoEmbeddings": "¿Eliminar esta configuración de embedding?"
+    },
+    "computeTab": {
+      "title": "Calcular embeddings",
+      "loading": "Cargando…",
+      "configLabel": "Configuración de embedding",
+      "noConfigs": "— sin configuraciones disponibles —",
+      "querySetLabel": "Conjunto de consulta",
+      "querySetHelper": "(opcional — dejar vacío para calcular todos)",
+      "allSequences": "— todas las secuencias —",
+      "queueBatchSizeLabel": "Tamaño de lote en cola",
+      "queueBatchSizeHelper": "(seqs/trabajo)",
+      "modelBatchSizeLabel": "Tamaño de lote del modelo",
+      "modelBatchSizeHelper": "(seqs/inferencia)",
+      "deviceLabel": "Dispositivo",
+      "deviceCpu": "cpu — CPU (FP32)",
+      "deviceCuda": "cuda — GPU por defecto (FP16 para ESM3c/T5)",
+      "deviceCuda0": "cuda:0 — GPU 0",
+      "deviceCuda1": "cuda:1 — GPU 1",
+      "deviceCustom": "personalizado…",
+      "skipExisting": "Omitir embeddings existentes",
+      "launchComputeJob": "Lanzar trabajo de cálculo",
+      "launching": "Lanzando…"
+    }
+  },
+  "functionalAnnotation": {
+    "title": "Anotación funcional",
+    "tabs": {
+      "predict": "Ejecutar anotación",
+      "results": "Resultados"
+    },
+    "predictTab": {
+      "title": "Anotación de términos GO por similitud de embeddings",
+      "loading": "Cargando…",
+      "configLabel": "Configuración de embedding",
+      "noConfigs": "— sin configuraciones disponibles —",
+      "querySetLabel": "Conjunto de consulta",
+      "querySetHelper": "(opcional — dejar vacío para anotar todo)",
+      "allSequences": "— todas las secuencias —",
+      "annotationSetLabel": "Conjunto de anotaciones",
+      "noAnnotationSets": "— sin conjuntos de anotaciones disponibles —",
+      "snapshotLabel": "Instantánea de ontología",
+      "noSnapshots": "— sin instantáneas disponibles —",
+      "limitPerEntryLabel": "Límite por entrada",
+      "batchSizeLabel": "Tamaño de lote",
+      "distanceThresholdLabel": "Umbral de distancia",
+      "distanceThresholdHelper": "(opcional)",
+      "knnStrategy": "Estrategia KNN",
+      "aspectSeparatedKnn": "Índices KNN por aspecto",
+      "aspectSeparatedKnnHelper": "Índices de referencia separados BPO / MFO / CCO — mejora el recall para cada aspecto de forma independiente",
+      "featureEngineering": "Ingeniería de características",
+      "featureEngineeringHelper": "(opt-in — añade tiempo de cómputo)",
+      "sequenceAlignments": "Alineamientos de secuencia",
+      "sequenceAlignmentsHelper": "NW (global) + SW (local) vía parasail/BLOSUM62",
+      "taxonomicDistance": "Distancia taxonómica",
+      "taxonomicDistanceHelper": "LCA, distancia y relación vía taxonomía NCBI",
+      "searchBackend": "Backend de búsqueda",
+      "searchBackendLabel": "Backend",
+      "numpyBackend": "numpy — exacto",
+      "faissBackend": "faiss — indexado",
+      "metricLabel": "Métrica",
+      "cosineSimilarity": "coseno",
+      "euclideanDistance": "L2 (Euclidiana²)",
+      "indexTypeLabel": "Tipo de índice",
+      "flatIndex": "Flat — exacto",
+      "ivfflatIndex": "IVFFlat — aproximado (>100K refs)",
+      "hnswIndex": "HNSW — aproximado, basado en grafo",
+      "nlistLabel": "nlist",
+      "nprobeLabel": "nprobe",
+      "mLabel": "M",
+      "efSearchLabel": "efSearch",
+      "launchAnnotationJob": "Lanzar trabajo de anotación",
+      "launching": "Lanzando…"
+    },
+    "resultsTab": {
+      "title": "Resultados",
+      "refresh": "Actualizar",
+      "noResults": "Sin resultados de anotación aún. Ejecute un trabajo de anotación desde la pestaña Ejecutar anotación.",
+      "tableHeaders": {
+        "id": "ID",
+        "config": "Configuración",
+        "annotationSet": "Conjunto de anotaciones",
+        "snapshot": "Instantánea",
+        "goTerms": "Términos GO",
+        "distanceThreshold": "Umbral de distancia",
+        "created": "Creado"
+      },
+      "delete": "Eliminar",
+      "deleteConfirm": "¿Eliminar este conjunto de anotaciones y sus {count} asignaciones de términos GO? Esta acción no se puede deshacer.",
+      "deleteConfirmNoAssignments": "¿Eliminar este conjunto de anotaciones?"
+    }
+  },
+  "evaluation": {
+    "title": "Evaluación CAFA",
+    "generateSection": {
+      "heading": "Nuevo conjunto de evaluación",
+      "description": "Calcula el delta entre dos versiones GOA. Aplica filtrado de evidencia experimental y propagación del calificador NOT a través del DAG GO.",
+      "oldSetLabel": "Conjunto GOA antiguo (referencia)",
+      "selectSet": "— seleccionar —",
+      "newSetLabel": "Nuevo conjunto GOA (ground truth)",
+      "errorSameSets": "Los conjuntos antiguo y nuevo deben ser diferentes.",
+      "generateEvaluationSet": "Generar conjunto de evaluación",
+      "generating": "Encolando…"
+    },
+    "evaluationSetsSection": {
+      "heading": "Conjuntos de evaluación"
+    },
+    "evaluationSetCard": {
+      "deleteConfirm": "¿Eliminar este conjunto de evaluación y todos sus resultados?",
+      "delete": "Eliminar",
+      "deltaProteins": "Proteínas delta",
+      "nkProteins": "Proteínas NK",
+      "lkProteins": "Proteínas LK",
+      "pkProteins": "Proteínas PK",
+      "groundTruthFiles": "Archivos de ground truth",
+      "downloadNK": "NK",
+      "downloadLK": "LK",
+      "downloadPK": "PK",
+      "downloadKnownTerms": "Términos conocidos",
+      "deltaProteinSequences": "Secuencias de proteínas delta (FASTA)",
+      "allDelta": "Todo delta (NK+LK+PK)",
+      "nkOnly": "Solo NK",
+      "lkOnly": "Solo LK",
+      "pkOnly": "Solo PK",
+      "runCafaEvaluator": "Ejecutar evaluador CAFA",
+      "predictionSetLabel": "Conjunto de predicciones",
+      "scoringConfigLabel": "Configuración de scoring (opcional)",
+      "fallbackFormula": "— fallback (1−d/2) —",
+      "maxDistanceLabel": "Distancia máxima (opcional)",
+      "jobQueued": "Trabajo en cola.",
+      "viewJob": "Ver trabajo →",
+      "runEvaluation": "Ejecutar evaluación (NK + LK + PK)",
+      "resultsHeading": "Resultados",
+      "pollingResults": "● consultando",
+      "refreshResults": "↻ Actualizar",
+      "refreshing": "Actualizando…",
+      "noEvaluations": "Sin evaluaciones ejecutadas aún.",
+      "predictionSet": "Conjunto de predicciones:",
+      "scoring": "Scoring:",
+      "artifactsDownload": "↓ Artefactos (.zip)",
+      "resultDelete": "Eliminar",
+      "deleteResultConfirm": "¿Eliminar este resultado de evaluación?"
+    },
+    "resultMetrics": {
+      "nk": "NK",
+      "lk": "LK",
+      "pk": "PK",
+      "biologicalProcess": "Proceso biológico",
+      "molecularFunction": "Función molecular",
+      "cellularComponent": "Componente celular",
+      "fmax": "Fmax",
+      "precision": "Precisión",
+      "recall": "Recall",
+      "coverage": "Cobertura",
+      "tau": "τ"
+    },
+    "manualEvaluatorSection": {
+      "heading": "Comando manual del evaluador"
+    }
+  },
+  "scoring": {
+    "title": "Configuraciones de scoring",
+    "description": "Un ScoringConfig define cómo se combinan las señales de predicción en una puntuación de confianza [0, 1] sin volver a ejecutar el pipeline KNN.",
+    "loadPresets": "Cargar presets",
+    "presetsLoading": "Cargando…",
+    "availableSignals": "Señales disponibles",
+    "signals": {
+      "embeddingSimilarity": "Similitud de embedding",
+      "embeddingSimilarityHint": "1 − cosine_distance / 2 — siempre disponible.",
+      "identityNw": "Identidad NW",
+      "identityNwHint": "Identidad de secuencia global Needleman-Wunsch [0, 1].",
+      "identitySw": "Identidad SW",
+      "identitySwHint": "Identidad de secuencia local Smith-Waterman [0, 1].",
+      "evidenceWeight": "Peso de evidencia",
+      "evidenceWeightHint": "Calidad del código de evidencia GO de la anotación de referencia.",
+      "taxonomicProximity": "Proximidad taxonómica",
+      "taxonomicProximityHint": "1 / (1 + distancia_taxonómica)"
+    },
+    "configCard": {
+      "deleteConfirm": "¿Eliminar la configuración de scoring \"{name}\"?",
+      "delete": "Eliminar",
+      "customEvidenceWeights": "pesos de evidencia personalizados",
+      "evidenceCodeWeights": "Pesos de código de evidencia",
+      "expand": "▶",
+      "collapse": "▲",
+      "systemDefaults": "(valores por defecto del sistema)",
+      "custom": "(personalizado)"
+    },
+    "newConfigForm": {
+      "newConfig": "+ Nueva configuración de scoring",
+      "formTitle": "Nueva configuración",
+      "close": "×",
+      "nameLabel": "Nombre",
+      "namePlaceholder": "mi_config",
+      "formulaLabel": "Fórmula",
+      "linear": "linear",
+      "evidenceWeighted": "evidence_weighted",
+      "descriptionLabel": "Descripción",
+      "descriptionHelper": "(opcional)",
+      "descriptionPlaceholder": "Para qué es esta configuración…",
+      "signalWeights": "Pesos de señal",
+      "evidenceCodeWeights": "Pesos de código de evidencia",
+      "overrideCheckbox": "Anular pesos de calidad por código de evidencia",
+      "systemDefaultsNote": "Usando valores por defecto del sistema — EXP/IDA → 1.0 · ISS/IBA → 0.7 · IEA → 0.3 · ND → 0.1",
+      "experimental": "Experimental",
+      "experimentalDescription": "Anotaciones respaldadas por evidencia experimental directa. Nivel de mayor confianza.",
+      "computational": "Computacional / Filogenético",
+      "computationalDescription": "Anotaciones derivadas de similitud de secuencia, ortología o inferencia filogenética.",
+      "electronic": "Electrónico",
+      "electronicDescription": "Anotaciones automáticas (IEA) o declaraciones de autor no trazables (NAS). Menor confianza.",
+      "noData": "Sin datos",
+      "noDataDescription": "Código de marcador que indica que no hay datos biológicos disponibles.",
+      "off": "Desactivado",
+      "max": "Máximo",
+      "groupShortcut": "0.5",
+      "resetEvidenceWeights": "Restablecer todos a valores por defecto del sistema",
+      "saveConfig": "Guardar configuración",
+      "saving": "Guardando…",
+      "cancel": "Cancelar"
+    },
+    "noConfigs": "Sin configuraciones aún. Cargue los presets o cree una arriba."
+  },
+  "querySets": {
+    "title": "Conjuntos de consulta",
+    "uploadFasta": "+ Cargar FASTA",
+    "noQuerySets": "Sin conjuntos de consulta aún.",
+    "uploadFastaCta": "Cargue un archivo FASTA para comenzar",
+    "tableHeaders": {
+      "name": "Nombre",
+      "sequences": "Secuencias",
+      "created": "Creado"
+    },
+    "delete": "Eliminar",
+    "deleteConfirm": "¿Eliminar el conjunto de consulta \"{name}\"? Esta acción no se puede deshacer.",
+    "uploadModal": {
+      "title": "Cargar FASTA",
+      "close": "×",
+      "nameLabel": "Nombre",
+      "nameRequired": "*",
+      "namePlaceholder": "p.ej. proteinas_humanas_nuevas",
+      "descriptionLabel": "Descripción (opcional)",
+      "descriptionPlaceholder": "Descripción breve",
+      "fastaFileLabel": "Archivo FASTA",
+      "fastaFileRequired": "*",
+      "dragDrop": "Suelte el FASTA aquí o explore",
+      "supportedFormats": ".fasta · .fa · .faa · .txt",
+      "fileSelected": "✓",
+      "clickToChange": "clic para cambiar",
+      "cancel": "Cancelar",
+      "upload": "Cargar",
+      "uploading": "Cargando…"
+    },
+    "expandedEntries": {
+      "title": "Entradas",
+      "count": "({count})"
+    }
+  },
+  "support": {
+    "title": "Soporte",
+    "hero": {
+      "supportCount": "{count} personas apoyan este proyecto",
+      "withComments": "{count} con comentarios",
+      "anonymous": "{count} anónimos"
+    },
+    "commentsSection": {
+      "heading": "Lo que dice la gente",
+      "noComments": "Sin comentarios aún. ¡Sé el primero!"
+    }
+  },
+  "maintenance": {
+    "title": "Mantenimiento",
+    "description": "Limpie datos huérfanos que se acumulan con el tiempo. Todas las operaciones son seguras mientras el sistema está activo.",
+    "orphanSequences": {
+      "title": "Secuencias huérfanas",
+      "description": "Secuencias sin Protein ni entrada QuerySet que las referencie.",
+      "orphanLabel": "Secuencias huérfanas",
+      "totalSequences": "Total de secuencias",
+      "referencedSequences": "Secuencias referenciadas",
+      "clean": "Limpiar",
+      "toClean": "{count} para limpiar",
+      "refresh": "Actualizar",
+      "vacuum": "Vacuum"
+    },
+    "unindexedEmbeddings": {
+      "title": "Embeddings no indexados",
+      "description": "Embeddings para secuencias que no están en la base de datos de referencia de proteínas.",
+      "orphanLabel": "Embeddings no indexados",
+      "totalEmbeddings": "Total de embeddings",
+      "indexedEmbeddings": "Embeddings indexados",
+      "clean": "Limpiar",
+      "toClean": "{count} para limpiar",
+      "refresh": "Actualizar",
+      "vacuum": "Vacuum",
+      "cleaning": "Limpiando…"
+    }
+  },
+  "components": {
+    "resetDbButton": {
+      "button": "Resetear DB",
+      "resetOk": "✓ Reset OK",
+      "error": "✗ Error",
+      "confirmTitle": "¿Resetear la base de datos?",
+      "confirmMessage": "Esto eliminará permanentemente todos los datos: proteínas, anotaciones, embeddings, predicciones y trabajos. Esta acción no se puede deshacer.",
+      "cancel": "Cancelar",
+      "confirm": "Sí, resetear",
+      "confirming": "Reseteando…"
+    },
+    "supportButton": {
+      "support": "Apoyar",
+      "tooltip": "Los comentarios y métricas son públicos y visibles para todos los visitantes.",
+      "projectSupport": "¡Apoya el proyecto!",
+      "commentPlaceholder": "Deja un comentario (opcional)…",
+      "publicNote": "Los comentarios y métricas son públicos.",
+      "sendThumbsUp": "👍 Enviar thumbs up",
+      "sending": "Enviando…",
+      "thanks": "¡Gracias por el apoyo! 🎉",
+      "recentComments": "Comentarios recientes",
+      "viewAll": "Ver todos →"
+    },
+    "usagePolicyModal": {
+      "title": "Bienvenido a PROTEA",
+      "subtitle": "Recursos informáticos personales, compartidos abiertamente — por favor lee antes de continuar",
+      "intro": "Esta plataforma se ejecuta en hardware personal compartido libremente con fines de investigación. No se requiere registro ni autorización. Ten en cuenta lo siguiente:",
+      "rule1": "Estos son recursos personales compartidos voluntariamente. Por favor, ten en cuenta la carga que generas.",
+      "rule2": "Antes de lanzar trabajos pesados o de larga duración, contáctanos primero — un mensaje rápido explicando tu caso de uso es muy apreciado.",
+      "rule3": "Todos los datos procesados son públicos y abiertos. Siéntete libre de usar y compartir los resultados.",
+      "rule4": "PROTEA es libre y de código abierto. Cualquier individuo o institución de investigación puede desplegar su propia instancia — código fuente en GitHub.",
+      "rule5": "Este servicio funciona en modo best-effort. El sistema puede ser dado de baja por mantenimiento o uso personal en cualquier momento sin previo aviso.",
+      "rule6": "Si algo falla o se comporta de forma inesperada, por favor repórtalo. Los informes de errores y el feedback son genuinamente apreciados.",
+      "dataPublicNote": "Todos los datos procesados aquí son públicos. Gracias por usar esto de forma responsable.",
+      "accept": "Entendido, ¡vamos!"
+    },
+    "statusBadge": {
+      "queued": "EN COLA",
+      "running": "EJECUTANDO",
+      "succeeded": "COMPLETADO",
+      "failed": "FALLIDO",
+      "cancelled": "CANCELADO"
+    },
+    "eventTimeline": {
+      "noEvents": "Sin eventos aún."
+    },
+    "languageSwitcher": {
+      "label": "Idioma"
+    }
+  }
+}
diff --git a/apps/web/messages/pt.json b/apps/web/messages/pt.json
new file mode 100644
index 0000000..83b238e
--- /dev/null
+++ b/apps/web/messages/pt.json
@@ -0,0 +1,16 @@
+{
+  "layout": { "title": "PROTEA", "description": "Plataforma de dados de proteínas — fila de jobs e gerenciamento de pipelines" },
+  "nav": { "proteins": "Proteínas", "annotations": "Anotações", "querySets": "Query Sets", "embeddings": "Embeddings", "functionalAnnotation": "Anotação Funcional", "scoring": "Pontuação", "evaluation": "Avaliação", "jobs": "Jobs", "maintenance": "Manutenção" },
+  "jobs": { "title": "Jobs", "allStatuses": "Todos os status", "queued": "Na fila", "running": "Em execução", "succeeded": "Concluído", "failed": "Falhou", "cancelled": "Cancelado", "autoRefresh": "Atualização automática", "refresh": "Atualizar", "noJobsFound": "Nenhum job encontrado.", "status": "Status", "operation": "Operação", "jobId": "ID do Job", "created": "Criado", "activeJobs": "{count} ativo(s)", "jobDetail": { "title": "Detalhes do Job", "backToJobs": "← Jobs", "live": "Ao vivo", "cancel": "Cancelar", "delete": "Excluir", "deleteConfirm": "Excluir este job?", "queue": "Fila:", "created": "Criado:", "started": "Iniciado:", "finished": "Concluído:", "progress": "{current} / {total} {unit} ({percent}%)", "payloadLabel": "Payload", "childJobsTitle": "Jobs Filhos", "childJobsCount": "({count})", "eventsTitle": "Eventos", "eventsCount": "({count})" } },
+  "proteins": { "title": "Proteínas", "tabs": { "browse": "Explorar", "stats": "Estatísticas", "insert": "Inserir Proteínas", "metadata": "Buscar Metadados" }, "browseTab": { "searchPlaceholder": "acession, gene, organismo…", "search": "Buscar", "clear": "Limpar", "allProteins": "Todas as proteínas", "swissProt": "Apenas Swiss-Prot", "trembl": "Apenas TrEMBL", "canonicalOnly": "Apenas canônicas", "totalProteins": "{count} proteínas", "tableHeaders": { "accession": "Accession", "entryName": "Nome do Registro", "gene": "Gene", "organism": "Organismo", "length": "Comprimento", "source": "Fonte" }, "noProteinsCta": "Nenhuma proteína encontrada. Use a aba Inserir Proteínas para importar do UniProt.", "pagination": { "page": "Página {current} de {total}", "previous": "Anterior", "next": "Próxima" } }, "statsTab": { "refresh": "Atualizar", "loading": "Carregando…", "overview": "Visão Geral", "coverage": "Cobertura", "totalProteins": "Total de Proteínas", "canonical": "Canônicas", "isoforms": "{count} isoformas", "reviewed": "Swiss-Prot", "reviewedSub": "revisadas", "unreviewed": "TrEMBL", "unreviewedSub": "não revisadas", "withMetadata": "Com Metadados", "metadataSub": "{percent}% das canônicas", "withEmbeddings": "Com Embeddings", "embeddingsSub": "{percent}% do total", "withGoAnnotations": "Com Anotações GO", "goAnnotationsSub": "{percent}% do total" }, "insertTab": { "title": "Inserir Proteínas do UniProt", "description": "Baixa sequências FASTA e insere registros de Proteína + Sequência.", "searchCriteriaLabel": "Critérios de busca", "searchCriteriaHelper": "Consulta UniProt — reviewed:true = apenas Swiss-Prot", "pageSizeLabel": "Tamanho da página", "totalLimitLabel": "Limite total", "totalLimitOptional": "(opcional)", "includeIsoforms": "Incluir isoformas", "jobQueuedPrefix": "Job na fila: ", "launchJob": "Iniciar Job", "launching": "Iniciando…" }, "metadataTab": { "title": "Buscar Metadados do UniProt", "description": "Baixa anotações TSV e insere registros ProteinUniProtMetadata.", "searchCriteriaLabel": "Critérios de busca", "searchCriteriaHelper": "Consulta UniProt — reviewed:true = apenas Swiss-Prot", "pageSizeLabel": "Tamanho da página", "totalLimitLabel": "Limite total", "totalLimitOptional": "(opcional)", "launchJob": "Iniciar Job", "launching": "Iniciando…" }, "sourceSwissProt": "Swiss-Prot", "sourceTrembl": "TrEMBL" },
+  "proteinDetail": { "backToProteins": "← Proteínas", "tabs": { "overview": "Visão Geral", "annotations": "Anotações GO" }, "overviewTab": { "identity": "Identidade", "gene": "Gene", "organism": "Organismo", "taxonId": "ID do Táxon", "length": "Comprimento", "aa": "aa", "sequenceId": "ID da Sequência", "canonical": "Canônica", "coverage": "Cobertura", "embeddings": "Embeddings", "goAnnotations": "Anotações GO", "metadata": "Metadados", "yes": "sim", "none": "nenhum", "isoforms": "Isoformas", "function": "Função", "biochemistry": "Bioquímica", "ecNumber": "Número EC", "catalyticActivity": "Atividade Catalítica", "cofactor": "Cofator", "activityRegulation": "Regulação da Atividade", "pathway": "Via Metabólica", "absorption": "Absorção", "kinetics": "Cinética", "phDependence": "Dependência de pH", "redoxPotential": "Potencial Redox", "temperatureDependence": "Dependência de Temperatura", "rheaId": "ID Rhea", "keywords": "Palavras-chave", "noFunctionalMetadata": "Nenhum metadado funcional disponível. Use a aba Buscar Metadados para importar do UniProt.", "showGoGraph": "Mostrar Grafo GO", "hideGoGraph": "Ocultar Grafo GO", "loadingGraph": "Carregando grafo…", "noGoAnnotations": "Nenhuma anotação GO encontrada para esta proteína.", "molecularFunction": "Função Molecular", "biologicalProcess": "Processo Biológico", "cellularComponent": "Componente Celular", "annotations": "anotações", "goTableHeaders": { "goId": "ID GO", "name": "Nome", "evidence": "Evidência", "qualifier": "Qualificador", "source": "Fonte" } } },
+  "annotations": { "title": "Anotações", "tabs": { "sets": "Conjuntos de Anotações", "snapshots": "Snapshots de Ontologia", "loadSnapshot": "Carregar Snapshot", "loadGoa": "Carregar GOA", "loadQuickgo": "Carregar QuickGO" }, "setsTab": { "annotationSets": "{count} conjuntos de anotações", "refresh": "Atualizar", "noSetsFound": "Nenhum conjunto de anotações ainda. Carregue anotações GO pelas abas Carregar GOA ou Carregar QuickGO.", "tableHeaders": { "id": "ID", "source": "Fonte", "version": "Versão", "annotations": "Anotações", "meta": "Meta", "created": "Criado" }, "delete": "Excluir", "deleteConfirm": "Excluir este conjunto de anotações e suas {count} anotações GO? Esta ação não pode ser desfeita.", "deleteConfirmNoAnnotations": "Excluir este conjunto de anotações?" }, "snapshotsTab": { "snapshots": "{count} snapshots", "refresh": "Atualizar", "noSnapshotsFound": "Nenhum snapshot de ontologia ainda. Use a aba Carregar Snapshot.", "tableHeaders": { "id": "ID", "version": "Versão", "goTerms": "Termos GO", "iaUrl": "URL IA", "loaded": "Carregado" }, "notSet": "não definido", "save": "Salvar", "cancel": "Cancelar", "editTooltip": "Toque para editar a URL IA" }, "loadSnapshotTab": { "title": "Carregar Snapshot de Ontologia", "description": "Baixa um arquivo GO OBO e preenche registros GOTerm.", "oboUrlLabel": "URL OBO", "launchJob": "Iniciar Job", "launching": "Iniciando…" }, "loadGoaTab": { "title": "Carregar Anotações GOA", "description": "Carrega em massa anotações GO de um arquivo GAF.", "snapshotLabel": "Snapshot de Ontologia", "selectSnapshot": "— selecionar snapshot —", "noSnapshots": "Nenhum snapshot — execute Carregar Snapshot primeiro.", "gafUrlLabel": "URL GAF", "gafUrlPlaceholder": "https://current.geneontology.org/annotations/goa_human.gaf.gz", "sourceVersionLabel": "Versão da fonte", "sourceVersionPlaceholder": "2025-03", "launchJob": "Iniciar Job", "launching": "Iniciando…" }, "loadQuickgoTab": { "title": "Carregar Anotações QuickGO", "description": "Transmite anotações GO da API de download em massa do QuickGO.", "snapshotLabel": "Snapshot de Ontologia", "selectSnapshot": "— selecionar snapshot —", "noSnapshots": "Nenhum snapshot — execute Carregar Snapshot primeiro.", "sourceVersionLabel": "Versão da fonte", "sourceVersionPlaceholder": "2025-03", "launchJob": "Iniciar Job", "launching": "Iniciando…" } },
+  "embeddings": { "title": "Embeddings", "tabs": { "configs": "Configurações", "compute": "Computar" }, "configsTab": { "configs": "{count} configurações", "newConfig": "+ Nova Configuração", "cancel": "Cancelar", "newConfigForm": { "title": "Nova Configuração de Embedding", "layerIndexingWarning": "Indexação de camadas — convenção reversa: 0 = última (camada mais semântica), 1 = penúltima, etc.", "modelBackendLabel": "Backend do Modelo", "modelBackendEsm": "esm — HuggingFace EsmModel (ESM-2)", "modelBackendEsm3c": "esm3c — ESM SDK ESMC (ESM3c) · FP16 na GPU", "modelBackendT5": "t5 — HuggingFace T5EncoderModel (ProstT5…)", "modelBackendAuto": "auto — usa esm como fallback", "modelLabel": "Modelo", "customModelPlaceholder": "ex.: facebook/esm2_t33_650M_UR50D", "layerIndicesLabel": "Índices de Camadas", "layerIndicesHelper": "(0 = última, 1 = penúltima…)", "layerIndicesPlaceholder": "0  ou  0,1,2", "layerAggLabel": "Agregação de Camadas", "layerAggMean": "mean — média elemento a elemento", "layerAggLast": "last — apenas a última camada selecionada", "layerAggConcat": "concat — concatenar todas (dim × n_layers)", "poolingLabel": "Pooling de Sequência", "poolingMean": "mean — média sobre resíduos", "poolingMax": "max — máximo sobre resíduos", "poolingMeanMax": "mean_max — concat(mean, max) · dim × 2", "poolingCls": "cls — token CLS/BOS na posição 0", "maxLengthLabel": "Comprimento Máximo (tokens)", "descriptionLabel": "Descrição (opcional)", "normalizeResidues": "Normalizar resíduos (L2 por resíduo antes do pooling)", "normalizeFinal": "Normalizar embedding final (L2 após pooling)", "enableChunking": "Habilitar chunking (sequências longas → múltiplos embeddings por sequência)", "chunkSizeLabel": "Tamanho do Chunk (resíduos)", "chunkOverlapLabel": "Sobreposição do Chunk (resíduos)", "createConfig": "Criar Configuração", "creating": "Criando…" }, "tableHeaders": { "description": "Descrição", "model": "Modelo", "backend": "Backend", "layers": "Camadas", "agg": "Agg", "pool": "Pool", "norm": "Norm", "created": "Criado" }, "noConfigs": "Nenhuma configuração de embedding ainda. Crie uma", "deleteConfirm": "Excluir esta configuração de embedding e seus {count} embeddings armazenados? Esta ação não pode ser desfeita.", "deleteConfirmNoEmbeddings": "Excluir esta configuração de embedding?" }, "computeTab": { "title": "Computar Embeddings", "loading": "Carregando…", "configLabel": "Configuração de Embedding", "noConfigs": "— nenhuma configuração disponível —", "querySetLabel": "Query Set", "querySetHelper": "(opcional — deixe vazio para computar todos)", "allSequences": "— todas as sequências —", "queueBatchSizeLabel": "Tamanho do Lote na Fila", "queueBatchSizeHelper": "(seqs/job)", "modelBatchSizeLabel": "Tamanho do Lote do Modelo", "modelBatchSizeHelper": "(seqs/passagem)", "deviceLabel": "Dispositivo", "deviceCpu": "cpu — CPU (FP32)", "deviceCuda": "cuda — GPU padrão (FP16 para ESM3c/T5)", "deviceCuda0": "cuda:0 — GPU 0", "deviceCuda1": "cuda:1 — GPU 1", "deviceCustom": "personalizado…", "skipExisting": "Ignorar embeddings existentes", "launchComputeJob": "Iniciar Job de Computação", "launching": "Iniciando…" } },
+  "functionalAnnotation": { "title": "Anotação Funcional", "tabs": { "predict": "Executar Anotação", "results": "Resultados" }, "predictTab": { "title": "Anotação de Termos GO por Similaridade de Embedding", "loading": "Carregando…", "configLabel": "Configuração de Embedding", "noConfigs": "— nenhuma configuração disponível —", "querySetLabel": "Query Set", "querySetHelper": "(opcional — deixe vazio para anotar todos)", "allSequences": "— todas as sequências —", "annotationSetLabel": "Conjunto de Anotações", "noAnnotationSets": "— nenhum conjunto de anotações disponível —", "snapshotLabel": "Snapshot de Ontologia", "noSnapshots": "— nenhum snapshot disponível —", "limitPerEntryLabel": "Limite por Entrada", "batchSizeLabel": "Tamanho do Lote", "distanceThresholdLabel": "Limiar de Distância", "distanceThresholdHelper": "(opcional)", "knnStrategy": "Estratégia KNN", "aspectSeparatedKnn": "Índices KNN por aspecto", "aspectSeparatedKnnHelper": "Índices de referência separados BPO / MFO / CCO — melhora o recall para cada aspecto independentemente", "featureEngineering": "Feature Engineering", "featureEngineeringHelper": "(opt-in — aumenta o tempo de computação)", "sequenceAlignments": "Alinhamentos de sequência", "sequenceAlignmentsHelper": "NW (global) + SW (local) via parasail/BLOSUM62", "taxonomicDistance": "Distância taxonômica", "taxonomicDistanceHelper": "LCA, distância e relação via taxonomia NCBI", "searchBackend": "Backend de Busca", "searchBackendLabel": "Backend", "numpyBackend": "numpy — exato", "faissBackend": "faiss — indexado", "metricLabel": "Métrica", "cosineSimilarity": "cosseno", "euclideanDistance": "L2 (Euclidiano²)", "indexTypeLabel": "Tipo de Índice", "flatIndex": "Flat — exato", "ivfflatIndex": "IVFFlat — aproximado (>100K refs)", "hnswIndex": "HNSW — aproximado, baseado em grafo", "nlistLabel": "nlist", "nprobeLabel": "nprobe", "mLabel": "M", "efSearchLabel": "efSearch", "launchAnnotationJob": "Iniciar Job de Anotação", "launching": "Iniciando…" }, "resultsTab": { "title": "Resultados", "refresh": "Atualizar", "noResults": "Nenhum resultado de anotação ainda. Execute um job de anotação pela aba Executar Anotação.", "tableHeaders": { "id": "ID", "config": "Configuração", "annotationSet": "Conjunto de Anotações", "snapshot": "Snapshot", "goTerms": "Termos GO", "distanceThreshold": "Limiar de Dist.", "created": "Criado" }, "delete": "Excluir", "deleteConfirm": "Excluir este conjunto de anotações e suas {count} atribuições de termos GO? Esta ação não pode ser desfeita.", "deleteConfirmNoAssignments": "Excluir este conjunto de anotações?" } },
+  "evaluation": { "title": "Avaliação CAFA", "generateSection": { "heading": "Novo Conjunto de Avaliação", "description": "Computa o delta entre duas releases GOA.", "oldSetLabel": "Conjunto GOA antigo (referência)", "selectSet": "— selecionar —", "newSetLabel": "Conjunto GOA novo (verdade de campo)", "errorSameSets": "Os conjuntos antigo e novo devem ser diferentes.", "generateEvaluationSet": "Gerar Conjunto de Avaliação", "generating": "Enfileirando…" }, "evaluationSetsSection": { "heading": "Conjuntos de Avaliação" }, "evaluationSetCard": { "deleteConfirm": "Excluir este conjunto de avaliação e todos os seus resultados?", "delete": "Excluir", "deltaProteins": "Proteínas delta", "nkProteins": "Proteínas NK", "lkProteins": "Proteínas LK", "pkProteins": "Proteínas PK", "groundTruthFiles": "Arquivos de verdade de campo", "downloadNK": "NK", "downloadLK": "LK", "downloadPK": "PK", "downloadKnownTerms": "Termos conhecidos", "deltaProteinSequences": "Sequências de proteínas delta (FASTA)", "allDelta": "Todo delta (NK+LK+PK)", "nkOnly": "Apenas NK", "lkOnly": "Apenas LK", "pkOnly": "Apenas PK", "runCafaEvaluator": "Executar avaliador CAFA", "predictionSetLabel": "Conjunto de predições", "scoringConfigLabel": "Configuração de pontuação (opcional)", "fallbackFormula": "— fallback (1−d/2) —", "maxDistanceLabel": "Distância máxima (opcional)", "jobQueued": "Job na fila.", "viewJob": "Ver job →", "runEvaluation": "Executar Avaliação (NK + LK + PK)", "resultsHeading": "Resultados", "pollingResults": "● consultando", "refreshResults": "↻ Atualizar", "refreshing": "Atualizando…", "noEvaluations": "Nenhuma avaliação executada ainda.", "predictionSet": "Conjunto de predições:", "scoring": "Pontuação:", "artifactsDownload": "↓ Artefatos (.zip)", "resultDelete": "Excluir", "deleteResultConfirm": "Excluir este resultado de avaliação?" }, "resultMetrics": { "nk": "NK", "lk": "LK", "pk": "PK", "biologicalProcess": "Processo Biológico", "molecularFunction": "Função Molecular", "cellularComponent": "Componente Celular", "fmax": "Fmax", "precision": "Precisão", "recall": "Recall", "coverage": "Cobertura", "tau": "τ" }, "manualEvaluatorSection": { "heading": "Comando do avaliador manual" } },
+  "scoring": { "title": "Configurações de Pontuação", "description": "Uma ScoringConfig define como os sinais brutos de predição são combinados em uma única pontuação de confiança [0, 1].", "loadPresets": "Carregar predefinições", "presetsLoading": "Carregando…", "availableSignals": "Sinais disponíveis", "signals": { "embeddingSimilarity": "Similaridade de embedding", "embeddingSimilarityHint": "1 − distância cosseno / 2 — sempre disponível.", "identityNw": "Identidade NW", "identityNwHint": "Identidade de sequência global Needleman-Wunsch [0, 1].", "identitySw": "Identidade SW", "identitySwHint": "Identidade de sequência local Smith-Waterman [0, 1].", "evidenceWeight": "Peso de evidência", "evidenceWeightHint": "Qualidade do código de evidência GO da anotação de referência.", "taxonomicProximity": "Proximidade taxonômica", "taxonomicProximityHint": "1 / (1 + distância_taxonômica)" }, "configCard": { "deleteConfirm": "Excluir configuração de pontuação \"{name}\"?", "delete": "Excluir", "customEvidenceWeights": "pesos de evidência personalizados", "evidenceCodeWeights": "Pesos de código de evidência", "expand": "▶", "collapse": "▲", "systemDefaults": "(padrões do sistema)", "custom": "(personalizado)" }, "newConfigForm": { "newConfig": "+ Nova configuração de pontuação", "formTitle": "Nova configuração", "close": "×", "nameLabel": "Nome", "namePlaceholder": "minha_configuracao", "formulaLabel": "Fórmula", "linear": "linear", "evidenceWeighted": "evidence_weighted", "descriptionLabel": "Descrição", "descriptionHelper": "(opcional)", "descriptionPlaceholder": "Para que serve esta configuração…", "signalWeights": "Pesos dos sinais", "evidenceCodeWeights": "Pesos de código de evidência", "overrideCheckbox": "Substituir pesos de qualidade por código de evidência", "systemDefaultsNote": "Usando padrões do sistema — EXP/IDA → 1,0 · ISS/IBA → 0,7 · IEA → 0,3 · ND → 0,1", "experimental": "Experimental", "experimentalDescription": "Anotações respaldadas por evidência experimental direta. Maior nível de confiança.", "computational": "Computacional / Filogenético", "computationalDescription": "Anotações derivadas de similaridade de sequência, ortologia ou inferência filogenética.", "electronic": "Eletrônico", "electronicDescription": "Anotações automatizadas (IEA) ou declarações de autores não rastreáveis (NAS). Menor confiança.", "noData": "Sem dados", "noDataDescription": "Código de marcador indicando que nenhum dado biológico está disponível.", "off": "Desligado", "max": "Máx", "groupShortcut": "0,5", "resetEvidenceWeights": "Redefinir todos para padrões do sistema", "saveConfig": "Salvar configuração", "saving": "Salvando…", "cancel": "Cancelar" }, "noConfigs": "Nenhuma configuração ainda. Carregue as predefinições ou crie uma acima." },
+  "querySets": { "title": "Query Sets", "uploadFasta": "+ Enviar FASTA", "noQuerySets": "Nenhum query set ainda.", "uploadFastaCta": "Envie um arquivo FASTA para começar", "tableHeaders": { "name": "Nome", "sequences": "Sequências", "created": "Criado" }, "delete": "Excluir", "deleteConfirm": "Excluir query set \"{name}\"? Esta ação não pode ser desfeita.", "uploadModal": { "title": "Enviar FASTA", "close": "×", "nameLabel": "Nome", "nameRequired": "*", "namePlaceholder": "ex.: proteinas_humanas_novas", "descriptionLabel": "Descrição (opcional)", "descriptionPlaceholder": "Descrição breve", "fastaFileLabel": "Arquivo FASTA", "fastaFileRequired": "*", "dragDrop": "Solte o FASTA aqui ou procure", "supportedFormats": ".fasta · .fa · .faa · .txt", "fileSelected": "✓", "clickToChange": "clique para alterar", "cancel": "Cancelar", "upload": "Enviar", "uploading": "Enviando…" }, "expandedEntries": { "title": "Entradas", "count": "({count})" } },
+  "support": { "title": "Suporte", "hero": { "supportCount": "{count} pessoas apoiam este projeto", "withComments": "{count} com comentários", "anonymous": "{count} anônimos" }, "commentsSection": { "heading": "O que as pessoas estão dizendo", "noComments": "Nenhum comentário ainda. Seja o primeiro!" } },
+  "maintenance": { "title": "Manutenção", "description": "Limpar dados órfãos. Todas as operações são seguras para executar enquanto o sistema está ativo.", "orphanSequences": { "title": "Sequências Órfãs", "description": "Sequências sem Proteína e sem entrada QuerySet apontando para elas.", "orphanLabel": "Sequências órfãs", "totalSequences": "Total de sequências", "referencedSequences": "Sequências referenciadas", "clean": "Limpar", "toClean": "{count} para limpar", "refresh": "Atualizar", "vacuum": "Vacuum" }, "unindexedEmbeddings": { "title": "Embeddings Não Indexados", "description": "Embeddings para sequências que não estão no banco de dados de referência de proteínas.", "orphanLabel": "Embeddings não indexados", "totalEmbeddings": "Total de embeddings", "indexedEmbeddings": "Embeddings indexados", "clean": "Limpar", "toClean": "{count} para limpar", "refresh": "Atualizar", "vacuum": "Vacuum", "cleaning": "Limpando…" } },
+  "components": { "resetDbButton": { "button": "Resetar BD", "resetOk": "✓ Reset OK", "error": "✗ Erro", "confirmTitle": "Resetar banco de dados?", "confirmMessage": "Isso excluirá permanentemente todos os dados: proteínas, anotações, embeddings, predições e jobs. Esta ação não pode ser desfeita.", "cancel": "Cancelar", "confirm": "Sim, resetar", "confirming": "Resetando…" }, "supportButton": { "support": "Apoiar", "tooltip": "Comentários e métricas são públicos.", "projectSupport": "Apoie o projeto!", "commentPlaceholder": "Deixe um comentário (opcional)…", "publicNote": "Comentários e métricas são públicos.", "sendThumbsUp": "👍 Enviar curtida", "sending": "Enviando…", "thanks": "Obrigado pelo apoio! 🎉", "recentComments": "Comentários recentes", "viewAll": "Ver todos →" }, "usagePolicyModal": { "title": "Bem-vindo ao PROTEA", "subtitle": "Recursos computacionais pessoais, compartilhados abertamente — leia antes de continuar", "intro": "Esta plataforma funciona em hardware pessoal compartilhado livremente para fins de pesquisa.", "rule1": "Estes são recursos pessoais compartilhados voluntariamente. Por favor, seja consciente da carga que você gera.", "rule2": "Antes de iniciar jobs pesados, entre em contato primeiro.", "rule3": "Todos os dados processados são públicos e abertos.", "rule4": "PROTEA é gratuito e de código aberto. Código fonte no GitHub.", "rule5": "Este serviço opera em base de melhor esforço e pode ser encerrado sem aviso prévio.", "rule6": "Se algo quebrar, por favor reporte.", "dataPublicNote": "Todos os dados processados aqui são públicos. Obrigado por usar com responsabilidade.", "accept": "Entendido, vamos lá" }, "statusBadge": { "queued": "QUEUED", "running": "RUNNING", "succeeded": "SUCCEEDED", "failed": "FAILED", "cancelled": "CANCELLED" }, "eventTimeline": { "noEvents": "Nenhum evento ainda." }, "languageSwitcher": { "label": "Idioma" } }
+}
\ No newline at end of file
diff --git a/apps/web/messages/zh.json b/apps/web/messages/zh.json
new file mode 100644
index 0000000..d837bc6
--- /dev/null
+++ b/apps/web/messages/zh.json
@@ -0,0 +1,16 @@
+{
+  "layout": { "title": "PROTEA", "description": "蛋白质数据平台 — 任务队列与流水线管理" },
+  "nav": { "proteins": "蛋白质", "annotations": "注释", "querySets": "查询集", "embeddings": "嵌入向量", "functionalAnnotation": "功能注释", "scoring": "评分", "evaluation": "评估", "jobs": "任务", "maintenance": "维护" },
+  "jobs": { "title": "任务", "allStatuses": "所有状态", "queued": "已排队", "running": "运行中", "succeeded": "已成功", "failed": "已失败", "cancelled": "已取消", "autoRefresh": "自动刷新", "refresh": "刷新", "noJobsFound": "未找到任务。", "status": "状态", "operation": "操作", "jobId": "任务 ID", "created": "创建时间", "activeJobs": "{count} 个活跃", "jobDetail": { "title": "任务详情", "backToJobs": "← 任务", "live": "实时", "cancel": "取消", "delete": "删除", "deleteConfirm": "删除此任务？", "queue": "队列：", "created": "创建：", "started": "开始：", "finished": "完成：", "progress": "{current} / {total} {unit} ({percent}%)", "payloadLabel": "载荷", "childJobsTitle": "子任务", "childJobsCount": "({count})", "eventsTitle": "事件", "eventsCount": "({count})" } },
+  "proteins": { "title": "蛋白质", "tabs": { "browse": "浏览", "stats": "统计", "insert": "导入蛋白质", "metadata": "获取元数据" }, "browseTab": { "searchPlaceholder": "登录号、基因、生物体…", "search": "搜索", "clear": "清除", "allProteins": "所有蛋白质", "swissProt": "仅 Swiss-Prot", "trembl": "仅 TrEMBL", "canonicalOnly": "仅规范序列", "totalProteins": "{count} 条蛋白质", "tableHeaders": { "accession": "登录号", "entryName": "条目名称", "gene": "基因", "organism": "生物体", "length": "长度", "source": "来源" }, "noProteinsCta": "未找到蛋白质。请使用\"导入蛋白质\"标签页从 UniProt 导入。", "pagination": { "page": "第 {current} 页，共 {total} 页", "previous": "上一页", "next": "下一页" } }, "statsTab": { "refresh": "刷新", "loading": "加载中…", "overview": "概览", "coverage": "覆盖率", "totalProteins": "蛋白质总数", "canonical": "规范序列", "isoforms": "{count} 个亚型", "reviewed": "Swiss-Prot", "reviewedSub": "已审核", "unreviewed": "TrEMBL", "unreviewedSub": "未审核", "withMetadata": "含元数据", "metadataSub": "占规范序列的 {percent}%", "withEmbeddings": "含嵌入向量", "embeddingsSub": "占总数的 {percent}%", "withGoAnnotations": "含 GO 注释", "goAnnotationsSub": "占总数的 {percent}%" }, "insertTab": { "title": "从 UniProt 导入蛋白质", "description": "下载 FASTA 序列并插入蛋白质及序列记录。", "searchCriteriaLabel": "搜索条件", "searchCriteriaHelper": "UniProt 查询 — reviewed:true = 仅 Swiss-Prot", "pageSizeLabel": "每页条数", "totalLimitLabel": "总限制", "totalLimitOptional": "（可选）", "includeIsoforms": "包含亚型", "jobQueuedPrefix": "任务已排队：", "launchJob": "启动任务", "launching": "启动中…" }, "metadataTab": { "title": "获取 UniProt 元数据", "description": "下载 TSV 注释并插入 ProteinUniProtMetadata 记录。", "searchCriteriaLabel": "搜索条件", "searchCriteriaHelper": "UniProt 查询 — reviewed:true = 仅 Swiss-Prot", "pageSizeLabel": "每页条数", "totalLimitLabel": "总限制", "totalLimitOptional": "（可选）", "launchJob": "启动任务", "launching": "启动中…" }, "sourceSwissProt": "Swiss-Prot", "sourceTrembl": "TrEMBL" },
+  "proteinDetail": { "backToProteins": "← 蛋白质", "tabs": { "overview": "概览", "annotations": "GO 注释" }, "overviewTab": { "identity": "标识", "gene": "基因", "organism": "生物体", "taxonId": "分类单元 ID", "length": "长度", "aa": "aa", "sequenceId": "序列 ID", "canonical": "规范序列", "coverage": "覆盖率", "embeddings": "嵌入向量", "goAnnotations": "GO 注释", "metadata": "元数据", "yes": "是", "none": "无", "isoforms": "亚型", "function": "功能", "biochemistry": "生物化学", "ecNumber": "EC 编号", "catalyticActivity": "催化活性", "cofactor": "辅因子", "activityRegulation": "活性调节", "pathway": "通路", "absorption": "吸收", "kinetics": "动力学", "phDependence": "pH 依赖性", "redoxPotential": "氧化还原电位", "temperatureDependence": "温度依赖性", "rheaId": "Rhea ID", "keywords": "关键词", "noFunctionalMetadata": "无可用功能元数据。请使用\"获取元数据\"标签页从 UniProt 导入。", "showGoGraph": "显示 GO 图", "hideGoGraph": "隐藏 GO 图", "loadingGraph": "加载图中…", "noGoAnnotations": "未找到该蛋白质的 GO 注释。", "molecularFunction": "分子功能", "biologicalProcess": "生物过程", "cellularComponent": "细胞组分", "annotations": "注释", "goTableHeaders": { "goId": "GO ID", "name": "名称", "evidence": "证据", "qualifier": "限定词", "source": "来源" } } },
+  "annotations": { "title": "注释", "tabs": { "sets": "注释集", "snapshots": "本体快照", "loadSnapshot": "加载快照", "loadGoa": "加载 GOA", "loadQuickgo": "加载 QuickGO" }, "setsTab": { "annotationSets": "{count} 个注释集", "refresh": "刷新", "noSetsFound": "暂无注释集。请从\"加载 GOA\"或\"加载 QuickGO\"标签页加载 GO 注释。", "tableHeaders": { "id": "ID", "source": "来源", "version": "版本", "annotations": "注释", "meta": "元信息", "created": "创建时间" }, "delete": "删除", "deleteConfirm": "删除此注释集及其 {count} 条 GO 注释？此操作无法撤销。", "deleteConfirmNoAnnotations": "删除此注释集？" }, "snapshotsTab": { "snapshots": "{count} 个快照", "refresh": "刷新", "noSnapshotsFound": "暂无本体快照。请使用\"加载快照\"标签页。", "tableHeaders": { "id": "ID", "version": "版本", "goTerms": "GO 术语", "iaUrl": "IA URL", "loaded": "加载时间" }, "notSet": "未设置", "save": "保存", "cancel": "取消", "editTooltip": "点击编辑 IA URL" }, "loadSnapshotTab": { "title": "加载本体快照", "description": "下载 GO OBO 文件并填充 GOTerm 记录。", "oboUrlLabel": "OBO URL", "launchJob": "启动任务", "launching": "启动中…" }, "loadGoaTab": { "title": "加载 GOA 注释", "description": "从 GAF 文件批量加载 GO 注释。", "snapshotLabel": "本体快照", "selectSnapshot": "— 选择快照 —", "noSnapshots": "无快照 — 请先运行\"加载快照\"。", "gafUrlLabel": "GAF URL", "gafUrlPlaceholder": "https://current.geneontology.org/annotations/goa_human.gaf.gz", "sourceVersionLabel": "来源版本", "sourceVersionPlaceholder": "2025-03", "launchJob": "启动任务", "launching": "启动中…" }, "loadQuickgoTab": { "title": "加载 QuickGO 注释", "description": "从 QuickGO 批量下载 API 流式传输 GO 注释。", "snapshotLabel": "本体快照", "selectSnapshot": "— 选择快照 —", "noSnapshots": "无快照 — 请先运行\"加载快照\"。", "sourceVersionLabel": "来源版本", "sourceVersionPlaceholder": "2025-03", "launchJob": "启动任务", "launching": "启动中…" } },
+  "embeddings": { "title": "嵌入向量", "tabs": { "configs": "配置", "compute": "计算" }, "configsTab": { "configs": "{count} 个配置", "newConfig": "+ 新建配置", "cancel": "取消", "newConfigForm": { "title": "新建嵌入向量配置", "layerIndexingWarning": "层索引 — 反向约定：0 = 最后（语义最丰富）层，1 = 倒数第二层，以此类推。", "modelBackendLabel": "模型后端", "modelBackendEsm": "esm — HuggingFace EsmModel (ESM-2)", "modelBackendEsm3c": "esm3c — ESM SDK ESMC (ESM3c) · GPU 上 FP16", "modelBackendT5": "t5 — HuggingFace T5EncoderModel (ProstT5…)", "modelBackendAuto": "auto — 回退到 esm", "modelLabel": "模型", "customModelPlaceholder": "例如 facebook/esm2_t33_650M_UR50D", "layerIndicesLabel": "层索引", "layerIndicesHelper": "（0 = 最后，1 = 倒数第二…）", "layerIndicesPlaceholder": "0  或  0,1,2", "layerAggLabel": "层聚合", "layerAggMean": "mean — 逐元素平均", "layerAggLast": "last — 仅最后选定层", "layerAggConcat": "concat — 拼接所有层（dim × n_layers）", "poolingLabel": "序列池化", "poolingMean": "mean — 残基均值", "poolingMax": "max — 残基最大值", "poolingMeanMax": "mean_max — concat(mean, max) · dim × 2", "poolingCls": "cls — 位置 0 的 CLS/BOS token", "maxLengthLabel": "最大长度（token）", "descriptionLabel": "描述（可选）", "normalizeResidues": "归一化残基（池化前按残基 L2 归一化）", "normalizeFinal": "归一化最终嵌入向量（池化后 L2 归一化）", "enableChunking": "启用分块（长序列 → 每条序列生成多个嵌入向量）", "chunkSizeLabel": "块大小（残基）", "chunkOverlapLabel": "块重叠（残基）", "createConfig": "创建配置", "creating": "创建中…" }, "tableHeaders": { "description": "描述", "model": "模型", "backend": "后端", "layers": "层", "agg": "聚合", "pool": "池化", "norm": "归一化", "created": "创建时间" }, "noConfigs": "暂无嵌入向量配置。请创建一个", "deleteConfirm": "删除此嵌入向量配置及其 {count} 个存储的嵌入向量？此操作无法撤销。", "deleteConfirmNoEmbeddings": "删除此嵌入向量配置？" }, "computeTab": { "title": "计算嵌入向量", "loading": "加载中…", "configLabel": "嵌入向量配置", "noConfigs": "— 无可用配置 —", "querySetLabel": "查询集", "querySetHelper": "（可选 — 留空则计算所有序列）", "allSequences": "— 所有序列 —", "queueBatchSizeLabel": "队列批次大小", "queueBatchSizeHelper": "（序列数/任务）", "modelBatchSizeLabel": "模型批次大小", "modelBatchSizeHelper": "（序列数/前向传播）", "deviceLabel": "设备", "deviceCpu": "cpu — CPU (FP32)", "deviceCuda": "cuda — 默认 GPU（ESM3c/T5 使用 FP16）", "deviceCuda0": "cuda:0 — GPU 0", "deviceCuda1": "cuda:1 — GPU 1", "deviceCustom": "自定义…", "skipExisting": "跳过已有嵌入向量", "launchComputeJob": "启动计算任务", "launching": "启动中…" } },
+  "functionalAnnotation": { "title": "功能注释", "tabs": { "predict": "运行注释", "results": "结果" }, "predictTab": { "title": "基于嵌入向量相似性的 GO 术语注释", "loading": "加载中…", "configLabel": "嵌入向量配置", "noConfigs": "— 无可用配置 —", "querySetLabel": "查询集", "querySetHelper": "（可选 — 留空则注释所有序列）", "allSequences": "— 所有序列 —", "annotationSetLabel": "注释集", "noAnnotationSets": "— 无可用注释集 —", "snapshotLabel": "本体快照", "noSnapshots": "— 无可用快照 —", "limitPerEntryLabel": "每条目限制", "batchSizeLabel": "批次大小", "distanceThresholdLabel": "距离阈值", "distanceThresholdHelper": "（可选）", "knnStrategy": "KNN 策略", "aspectSeparatedKnn": "按功能方面分离的 KNN 索引", "aspectSeparatedKnnHelper": "分离的 BPO / MFO / CCO 参考索引 — 独立提高各方面的召回率", "featureEngineering": "特征工程", "featureEngineeringHelper": "（按需启用 — 增加计算时间）", "sequenceAlignments": "序列比对", "sequenceAlignmentsHelper": "NW（全局）+ SW（局部），通过 parasail/BLOSUM62", "taxonomicDistance": "分类距离", "taxonomicDistanceHelper": "通过 NCBI 分类学计算 LCA、距离和关系", "searchBackend": "搜索后端", "searchBackendLabel": "后端", "numpyBackend": "numpy — 精确", "faissBackend": "faiss — 索引化", "metricLabel": "度量", "cosineSimilarity": "余弦", "euclideanDistance": "L2（欧氏²）", "indexTypeLabel": "索引类型", "flatIndex": "Flat — 精确", "ivfflatIndex": "IVFFlat — 近似（>100K 参考）", "hnswIndex": "HNSW — 近似，基于图", "nlistLabel": "nlist", "nprobeLabel": "nprobe", "mLabel": "M", "efSearchLabel": "efSearch", "launchAnnotationJob": "启动注释任务", "launching": "启动中…" }, "resultsTab": { "title": "结果", "refresh": "刷新", "noResults": "暂无注释结果。请在\"运行注释\"标签页运行注释任务。", "tableHeaders": { "id": "ID", "config": "配置", "annotationSet": "注释集", "snapshot": "快照", "goTerms": "GO 术语", "distanceThreshold": "距离阈值", "created": "创建时间" }, "delete": "删除", "deleteConfirm": "删除此注释集及其 {count} 个 GO 术语分配？此操作无法撤销。", "deleteConfirmNoAssignments": "删除此注释集？" } },
+  "evaluation": { "title": "CAFA 评估", "generateSection": { "heading": "新建评估集", "description": "计算两个 GOA 版本之间的差异。", "oldSetLabel": "旧 GOA 集（参考）", "selectSet": "— 选择 —", "newSetLabel": "新 GOA 集（真实标注）", "errorSameSets": "旧集和新集必须不同。", "generateEvaluationSet": "生成评估集", "generating": "排队中…" }, "evaluationSetsSection": { "heading": "评估集" }, "evaluationSetCard": { "deleteConfirm": "删除此评估集及其所有结果？", "delete": "删除", "deltaProteins": "差异蛋白质", "nkProteins": "NK 蛋白质", "lkProteins": "LK 蛋白质", "pkProteins": "PK 蛋白质", "groundTruthFiles": "真实标注文件", "downloadNK": "NK", "downloadLK": "LK", "downloadPK": "PK", "downloadKnownTerms": "已知术语", "deltaProteinSequences": "差异蛋白质序列（FASTA）", "allDelta": "所有差异（NK+LK+PK）", "nkOnly": "仅 NK", "lkOnly": "仅 LK", "pkOnly": "仅 PK", "runCafaEvaluator": "运行 CAFA 评估器", "predictionSetLabel": "预测集", "scoringConfigLabel": "评分配置（可选）", "fallbackFormula": "— 回退（1−d/2）—", "maxDistanceLabel": "最大距离（可选）", "jobQueued": "任务已排队。", "viewJob": "查看任务 →", "runEvaluation": "运行评估（NK + LK + PK）", "resultsHeading": "结果", "pollingResults": "● 轮询中", "refreshResults": "↻ 刷新", "refreshing": "刷新中…", "noEvaluations": "尚未运行任何评估。", "predictionSet": "预测集：", "scoring": "评分：", "artifactsDownload": "↓ 产物（.zip）", "resultDelete": "删除", "deleteResultConfirm": "删除此评估结果？" }, "resultMetrics": { "nk": "NK", "lk": "LK", "pk": "PK", "biologicalProcess": "生物过程", "molecularFunction": "分子功能", "cellularComponent": "细胞组分", "fmax": "Fmax", "precision": "精确率", "recall": "召回率", "coverage": "覆盖率", "tau": "τ" }, "manualEvaluatorSection": { "heading": "手动评估器命令" } },
+  "scoring": { "title": "评分配置", "description": "ScoringConfig 定义如何将原始预测信号合并为单一的 [0, 1] 置信度分数。", "loadPresets": "加载预设", "presetsLoading": "加载中…", "availableSignals": "可用信号", "signals": { "embeddingSimilarity": "嵌入向量相似性", "embeddingSimilarityHint": "1 − 余弦距离 / 2 — 始终可用。", "identityNw": "NW 一致性", "identityNwHint": "Needleman-Wunsch 全局序列一致性 [0, 1]。", "identitySw": "SW 一致性", "identitySwHint": "Smith-Waterman 局部序列一致性 [0, 1]。", "evidenceWeight": "证据权重", "evidenceWeightHint": "参考注释的 GO 证据代码质量。", "taxonomicProximity": "分类邻近度", "taxonomicProximityHint": "1 / (1 + 分类距离)" }, "configCard": { "deleteConfirm": "删除评分配置\"{name}\"？", "delete": "删除", "customEvidenceWeights": "自定义证据权重", "evidenceCodeWeights": "证据代码权重", "expand": "▶", "collapse": "▲", "systemDefaults": "（系统默认值）", "custom": "（自定义）" }, "newConfigForm": { "newConfig": "+ 新建评分配置", "formTitle": "新建配置", "close": "×", "nameLabel": "名称", "namePlaceholder": "我的配置", "formulaLabel": "公式", "linear": "linear", "evidenceWeighted": "evidence_weighted", "descriptionLabel": "描述", "descriptionHelper": "（可选）", "descriptionPlaceholder": "此配置的用途…", "signalWeights": "信号权重", "evidenceCodeWeights": "证据代码权重", "overrideCheckbox": "覆盖各证据代码质量权重", "systemDefaultsNote": "使用系统默认值 — EXP/IDA → 1.0 · ISS/IBA → 0.7 · IEA → 0.3 · ND → 0.1", "experimental": "实验性", "experimentalDescription": "由直接实验证据支持的注释。最高置信度等级。", "computational": "计算/系统发育", "computationalDescription": "源自序列相似性、直系同源或系统发育推断的注释。", "electronic": "电子化", "electronicDescription": "自动化注释（IEA）或不可溯源的作者声明（NAS）。置信度较低。", "noData": "无数据", "noDataDescription": "表示无可用生物数据的占位代码。", "off": "关闭", "max": "最大", "groupShortcut": "0.5", "resetEvidenceWeights": "全部重置为系统默认值", "saveConfig": "保存配置", "saving": "保存中…", "cancel": "取消" }, "noConfigs": "暂无配置。请加载预设或在上方创建一个。" },
+  "querySets": { "title": "查询集", "uploadFasta": "+ 上传 FASTA", "noQuerySets": "暂无查询集。", "uploadFastaCta": "上传 FASTA 文件以开始使用", "tableHeaders": { "name": "名称", "sequences": "序列", "created": "创建时间" }, "delete": "删除", "deleteConfirm": "删除查询集\"{name}\"？此操作无法撤销。", "uploadModal": { "title": "上传 FASTA", "close": "×", "nameLabel": "名称", "nameRequired": "*", "namePlaceholder": "例如 human_novel_proteins", "descriptionLabel": "描述（可选）", "descriptionPlaceholder": "简短描述", "fastaFileLabel": "FASTA 文件", "fastaFileRequired": "*", "dragDrop": "将 FASTA 拖放到此处或浏览文件", "supportedFormats": ".fasta · .fa · .faa · .txt", "fileSelected": "✓", "clickToChange": "点击更改", "cancel": "取消", "upload": "上传", "uploading": "上传中…" }, "expandedEntries": { "title": "条目", "count": "({count})" } },
+  "support": { "title": "支持", "hero": { "supportCount": "{count} 人支持此项目", "withComments": "{count} 条含评论", "anonymous": "{count} 条匿名" }, "commentsSection": { "heading": "大家的评价", "noComments": "暂无评论。成为第一个！" } },
+  "maintenance": { "title": "维护", "description": "清理孤立数据。所有操作在系统运行期间均可安全执行。", "orphanSequences": { "title": "孤立序列", "description": "没有蛋白质且没有 QuerySet 条目指向的序列。", "orphanLabel": "孤立序列", "totalSequences": "序列总数", "referencedSequences": "已引用序列", "clean": "清理", "toClean": "{count} 条待清理", "refresh": "刷新", "vacuum": "清理碎片" }, "unindexedEmbeddings": { "title": "未索引嵌入向量", "description": "不在蛋白质参考数据库中的序列的嵌入向量。", "orphanLabel": "未索引嵌入向量", "totalEmbeddings": "嵌入向量总数", "indexedEmbeddings": "已索引嵌入向量", "clean": "清理", "toClean": "{count} 条待清理", "refresh": "刷新", "vacuum": "清理碎片", "cleaning": "清理中…" } },
+  "components": { "resetDbButton": { "button": "重置数据库", "resetOk": "✓ 重置成功", "error": "✗ 错误", "confirmTitle": "重置数据库？", "confirmMessage": "这将永久删除所有数据：蛋白质、注释、嵌入向量、预测和任务。此操作无法撤销。", "cancel": "取消", "confirm": "是的，重置", "confirming": "重置中…" }, "supportButton": { "support": "支持", "tooltip": "评论和指标是公开的。", "projectSupport": "支持本项目！", "commentPlaceholder": "留下评论（可选）…", "publicNote": "评论和指标是公开的。", "sendThumbsUp": "👍 发送点赞", "sending": "发送中…", "thanks": "感谢您的支持！🎉", "recentComments": "最新评论", "viewAll": "查看全部 →" }, "usagePolicyModal": { "title": "欢迎使用 PROTEA", "subtitle": "个人计算资源，公开共享 — 请在继续前阅读", "intro": "本平台运行在个人硬件上，免费用于研究目的。", "rule1": "这些是自愿共享的个人资源。请注意您产生的负载。", "rule2": "在启动大型任务之前，请先联系我们。", "rule3": "所有处理的数据均为公开数据。", "rule4": "PROTEA 是免费且开源的。源代码在 GitHub 上。", "rule5": "本服务以尽力而为的方式运行，可能随时下线，恕不另行通知。", "rule6": "如果出现问题，请报告。", "dataPublicNote": "此处处理的所有数据均为公开数据。感谢您负责任地使用。", "accept": "明白了，开始使用" }, "statusBadge": { "queued": "QUEUED", "running": "RUNNING", "succeeded": "SUCCEEDED", "failed": "FAILED", "cancelled": "CANCELLED" }, "eventTimeline": { "noEvents": "暂无事件。" }, "languageSwitcher": { "label": "语言" } }
+}
\ No newline at end of file
diff --git a/apps/web/middleware.ts b/apps/web/middleware.ts
new file mode 100644
index 0000000..244023f
--- /dev/null
+++ b/apps/web/middleware.ts
@@ -0,0 +1,10 @@
+import createMiddleware from "next-intl/middleware";
+import { routing } from "./i18n/routing";
+
+export default createMiddleware(routing);
+
+export const config = {
+  matcher: [
+    "/((?!api|_next|_vercel|sphinx|api-proxy|.*\\..*).*)",
+  ],
+};
diff --git a/apps/web/next.config.ts b/apps/web/next.config.ts
index b6d78f7..198ca7e 100644
--- a/apps/web/next.config.ts
+++ b/apps/web/next.config.ts
@@ -1,10 +1,16 @@
 import type { NextConfig } from "next";
+import createNextIntlPlugin from "next-intl/plugin";
+
+const withNextIntl = createNextIntlPlugin("./i18n/request.ts");
 
 const apiUrl = process.env.PROTEA_API_URL ?? "http://localhost:8000";
 
 const nextConfig: NextConfig = {
   output: "standalone",
   trailingSlash: true,
+  experimental: {
+    middlewareClientMaxBodySize: 100 * 1024 * 1024, // 100 MB
+  },
   async rewrites() {
     return [
       { source: "/sphinx/", destination: `${apiUrl}/sphinx/` },
@@ -17,4 +23,4 @@ const nextConfig: NextConfig = {
   },
 };
 
-export default nextConfig;
+export default withNextIntl(nextConfig);
diff --git a/apps/web/package-lock.json b/apps/web/package-lock.json
index 28629cc..7890ed6 100644
--- a/apps/web/package-lock.json
+++ b/apps/web/package-lock.json
@@ -12,10 +12,12 @@
         "cytoscape": "^3.33.1",
         "cytoscape-dagre": "^2.5.0",
         "next": "16.1.6",
+        "next-intl": "^4.8.3",
         "react": "19.2.3",
         "react-dom": "19.2.3"
       },
       "devDependencies": {
+        "@playwright/test": "^1.58.2",
         "@tailwindcss/postcss": "^4",
         "@types/node": "^20",
         "@types/react": "^19",
@@ -426,6 +428,46 @@
         "node": "^18.18.0 || ^20.9.0 || >=21.1.0"
       }
     },
+    "node_modules/@formatjs/ecma402-abstract": {
+      "version": "3.1.2",
+      "resolved": "https://registry.npmjs.org/@formatjs/ecma402-abstract/-/ecma402-abstract-3.1.2.tgz",
+      "integrity": "sha512-ApPcse35dtr+snmBbDtnBkc9bvAZCxr9DDzgUR3nXiHZgpwU+85g1Mz0KRqjh7kIgJ6rgIQLQVqYx56rf58XZg==",
+      "dependencies": {
+        "@formatjs/fast-memoize": "3.1.1",
+        "@formatjs/intl-localematcher": "0.8.2",
+        "decimal.js": "^10.6.0"
+      }
+    },
+    "node_modules/@formatjs/fast-memoize": {
+      "version": "3.1.1",
+      "resolved": "https://registry.npmjs.org/@formatjs/fast-memoize/-/fast-memoize-3.1.1.tgz",
+      "integrity": "sha512-CbNbf+tlJn1baRnPkNePnBqTLxGliG6DDgNa/UtV66abwIjwsliPMOt0172tzxABYzSuxZBZfcp//qI8AvBWPg=="
+    },
+    "node_modules/@formatjs/icu-messageformat-parser": {
+      "version": "3.5.2",
+      "resolved": "https://registry.npmjs.org/@formatjs/icu-messageformat-parser/-/icu-messageformat-parser-3.5.2.tgz",
+      "integrity": "sha512-Ok2CTY90hkKTeTrGAgGPfF/V0gMBmo4qBsg83CFcP3uRETDiyAjKDGgN5aZbUTMiLt6o6hS8JQmyDwQspkXm9A==",
+      "dependencies": {
+        "@formatjs/ecma402-abstract": "3.1.2",
+        "@formatjs/icu-skeleton-parser": "2.1.2"
+      }
+    },
+    "node_modules/@formatjs/icu-skeleton-parser": {
+      "version": "2.1.2",
+      "resolved": "https://registry.npmjs.org/@formatjs/icu-skeleton-parser/-/icu-skeleton-parser-2.1.2.tgz",
+      "integrity": "sha512-8YuKCC+YhNTRwoL4OIT0flW6bNgH2TOtyM9e17vpHHgL815vlviPs9WZIKFuugyKOucAsQUKEIC1qKltj2z17Q==",
+      "dependencies": {
+        "@formatjs/ecma402-abstract": "3.1.2"
+      }
+    },
+    "node_modules/@formatjs/intl-localematcher": {
+      "version": "0.8.2",
+      "resolved": "https://registry.npmjs.org/@formatjs/intl-localematcher/-/intl-localematcher-0.8.2.tgz",
+      "integrity": "sha512-q05KMYGJLyqFNFtIb8NhWLF5X3aK/k0wYt7dnRFuy6aLQL+vUwQ1cg5cO4qawEiINybeCPXAWlprY2mSBjSXAQ==",
+      "dependencies": {
+        "@formatjs/fast-memoize": "3.1.1"
+      }
+    },
     "node_modules/@humanfs/core": {
       "version": "0.19.1",
       "resolved": "https://registry.npmjs.org/@humanfs/core/-/core-0.19.1.tgz",
@@ -1150,12 +1192,479 @@
         "node": ">=12.4.0"
       }
     },
+    "node_modules/@parcel/watcher": {
+      "version": "2.5.6",
+      "resolved": "https://registry.npmjs.org/@parcel/watcher/-/watcher-2.5.6.tgz",
+      "integrity": "sha512-tmmZ3lQxAe/k/+rNnXQRawJ4NjxO2hqiOLTHvWchtGZULp4RyFeh6aU4XdOYBFe2KE1oShQTv4AblOs2iOrNnQ==",
+      "hasInstallScript": true,
+      "dependencies": {
+        "detect-libc": "^2.0.3",
+        "is-glob": "^4.0.3",
+        "node-addon-api": "^7.0.0",
+        "picomatch": "^4.0.3"
+      },
+      "engines": {
+        "node": ">= 10.0.0"
+      },
+      "funding": {
+        "type": "opencollective",
+        "url": "https://opencollective.com/parcel"
+      },
+      "optionalDependencies": {
+        "@parcel/watcher-android-arm64": "2.5.6",
+        "@parcel/watcher-darwin-arm64": "2.5.6",
+        "@parcel/watcher-darwin-x64": "2.5.6",
+        "@parcel/watcher-freebsd-x64": "2.5.6",
+        "@parcel/watcher-linux-arm-glibc": "2.5.6",
+        "@parcel/watcher-linux-arm-musl": "2.5.6",
+        "@parcel/watcher-linux-arm64-glibc": "2.5.6",
+        "@parcel/watcher-linux-arm64-musl": "2.5.6",
+        "@parcel/watcher-linux-x64-glibc": "2.5.6",
+        "@parcel/watcher-linux-x64-musl": "2.5.6",
+        "@parcel/watcher-win32-arm64": "2.5.6",
+        "@parcel/watcher-win32-ia32": "2.5.6",
+        "@parcel/watcher-win32-x64": "2.5.6"
+      }
+    },
+    "node_modules/@parcel/watcher-android-arm64": {
+      "version": "2.5.6",
+      "resolved": "https://registry.npmjs.org/@parcel/watcher-android-arm64/-/watcher-android-arm64-2.5.6.tgz",
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+        "arm64"
+      ],
+      "optional": true,
+      "os": [
+        "android"
+      ],
+      "engines": {
+        "node": ">= 10.0.0"
+      },
+      "funding": {
+        "type": "opencollective",
+        "url": "https://opencollective.com/parcel"
+      }
+    },
+    "node_modules/@parcel/watcher-darwin-arm64": {
+      "version": "2.5.6",
+      "resolved": "https://registry.npmjs.org/@parcel/watcher-darwin-arm64/-/watcher-darwin-arm64-2.5.6.tgz",
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+      "cpu": [
+        "arm64"
+      ],
+      "optional": true,
+      "os": [
+        "darwin"
+      ],
+      "engines": {
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+      },
+      "funding": {
+        "type": "opencollective",
+        "url": "https://opencollective.com/parcel"
+      }
+    },
+    "node_modules/@parcel/watcher-darwin-x64": {
+      "version": "2.5.6",
+      "resolved": "https://registry.npmjs.org/@parcel/watcher-darwin-x64/-/watcher-darwin-x64-2.5.6.tgz",
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+      "cpu": [
+        "x64"
+      ],
+      "optional": true,
+      "os": [
+        "darwin"
+      ],
+      "engines": {
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+      },
+      "funding": {
+        "type": "opencollective",
+        "url": "https://opencollective.com/parcel"
+      }
+    },
+    "node_modules/@parcel/watcher-freebsd-x64": {
+      "version": "2.5.6",
+      "resolved": "https://registry.npmjs.org/@parcel/watcher-freebsd-x64/-/watcher-freebsd-x64-2.5.6.tgz",
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+        "x64"
+      ],
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+      "os": [
+        "freebsd"
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+        "type": "opencollective",
+        "url": "https://opencollective.com/parcel"
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+        "url": "https://opencollective.com/parcel"
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+        "url": "https://opencollective.com/parcel"
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+        "url": "https://opencollective.com/parcel"
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@@ -5043,6 +5696,41 @@
         "url": "https://github.com/sponsors/jonschlinkert"
       }
     },
+    "node_modules/playwright": {
+      "version": "1.58.2",
+      "resolved": "https://registry.npmjs.org/playwright/-/playwright-1.58.2.tgz",
+      "integrity": "sha512-vA30H8Nvkq/cPBnNw4Q8TWz1EJyqgpuinBcHET0YVJVFldr8JDNiU9LaWAE1KqSkRYazuaBhTpB5ZzShOezQ6A==",
+      "devOptional": true,
+      "dependencies": {
+        "playwright-core": "1.58.2"
+      },
+      "bin": {
+        "playwright": "cli.js"
+      },
+      "engines": {
+        "node": ">=18"
+      },
+      "optionalDependencies": {
+        "fsevents": "2.3.2"
+      }
+    },
+    "node_modules/playwright-core": {
+      "version": "1.58.2",
+      "resolved": "https://registry.npmjs.org/playwright-core/-/playwright-core-1.58.2.tgz",
+      "integrity": "sha512-yZkEtftgwS8CsfYo7nm0KE8jsvm6i/PTgVtB8DL726wNf6H2IMsDuxCpJj59KDaxCtSnrWan2AeDqM7JBaultg==",
+      "devOptional": true,
+      "bin": {
+        "playwright-core": "cli.js"
+      },
+      "engines": {
+        "node": ">=18"
+      }
+    },
+    "node_modules/po-parser": {
+      "version": "2.1.1",
+      "resolved": "https://registry.npmjs.org/po-parser/-/po-parser-2.1.1.tgz",
+      "integrity": "sha512-ECF4zHLbUItpUgE3OTtLKlPjeBN+fKEczj2zYjDfCGOzicNs0GK3Vg2IoAYwx7LH/XYw43fZQP6xnZ4TkNxSLQ=="
+    },
     "node_modules/possible-typed-array-names": {
       "version": "1.1.0",
       "resolved": "https://registry.npmjs.org/possible-typed-array-names/-/possible-typed-array-names-1.1.0.tgz",
@@ -5936,7 +6624,7 @@
       "version": "5.9.3",
       "resolved": "https://registry.npmjs.org/typescript/-/typescript-5.9.3.tgz",
       "integrity": "sha512-jl1vZzPDinLr9eUt3J/t7V6FgNEw9QjvBPdysz9KfQDD41fQrC2Y4vKQdiaUpFT4bXlb1RHhLpp8wtm6M5TgSw==",
-      "dev": true,
+      "devOptional": true,
       "bin": {
         "tsc": "bin/tsc",
         "tsserver": "bin/tsserver"
@@ -6065,6 +6753,26 @@
         "punycode": "^2.1.0"
       }
     },
+    "node_modules/use-intl": {
+      "version": "4.8.3",
+      "resolved": "https://registry.npmjs.org/use-intl/-/use-intl-4.8.3.tgz",
+      "integrity": "sha512-nLxlC/RH+le6g3amA508Itnn/00mE+J22ui21QhOWo5V9hCEC43+WtnRAITbJW0ztVZphev5X9gvOf2/Dk9PLA==",
+      "funding": [
+        {
+          "type": "individual",
+          "url": "https://github.com/sponsors/amannn"
+        }
+      ],
+      "dependencies": {
+        "@formatjs/fast-memoize": "^3.1.0",
+        "@schummar/icu-type-parser": "1.21.5",
+        "icu-minify": "^4.8.3",
+        "intl-messageformat": "^11.1.0"
+      },
+      "peerDependencies": {
+        "react": "^17.0.0 || ^18.0.0 || >=19.0.0-rc <19.0.0 || ^19.0.0"
+      }
+    },
     "node_modules/which": {
       "version": "2.0.2",
       "resolved": "https://registry.npmjs.org/which/-/which-2.0.2.tgz",
diff --git a/apps/web/package.json b/apps/web/package.json
index ca1a838..2e44186 100644
--- a/apps/web/package.json
+++ b/apps/web/package.json
@@ -13,10 +13,12 @@
     "cytoscape": "^3.33.1",
     "cytoscape-dagre": "^2.5.0",
     "next": "16.1.6",
+    "next-intl": "^4.8.3",
     "react": "19.2.3",
     "react-dom": "19.2.3"
   },
   "devDependencies": {
+    "@playwright/test": "^1.58.2",
     "@tailwindcss/postcss": "^4",
     "@types/node": "^20",
     "@types/react": "^19",
diff --git a/apps/web/playwright.config.ts b/apps/web/playwright.config.ts
new file mode 100644
index 0000000..ccaab21
--- /dev/null
+++ b/apps/web/playwright.config.ts
@@ -0,0 +1,36 @@
+import { defineConfig, devices } from "@playwright/test";
+
+export default defineConfig({
+  testDir: "./e2e",
+  use: {
+    baseURL: "http://localhost:3000",
+    browserName: "chromium",
+  },
+  workers: 1,
+  reporter: "list",
+  projects: [
+    {
+      name: "mobile",
+      use: {
+        browserName: "chromium",
+        viewport: { width: 390, height: 844 },
+        deviceScaleFactor: 3,
+        isMobile: true,
+        hasTouch: true,
+        userAgent: devices["iPhone 12"].userAgent,
+      },
+      testMatch: "**/mobile*.spec.ts",
+    },
+    {
+      name: "tablet",
+      use: {
+        browserName: "chromium",
+        viewport: { width: 768, height: 1024 },
+        deviceScaleFactor: 2,
+        isMobile: true,
+        hasTouch: true,
+      },
+      testMatch: "**/tablet*.spec.ts",
+    },
+  ],
+});
diff --git a/data/benchmarks/IA_cafa6.tsv b/data/benchmarks/IA_cafa6.tsv
new file mode 100644
index 0000000..94c67a2
--- /dev/null
+++ b/data/benchmarks/IA_cafa6.tsv
@@ -0,0 +1,40122 @@
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+GO:1990310	10.016808287686555
+GO:1990311	10.016808287686555
+GO:1990312	10.016808287686555
+GO:1990313	10.016808287686555
+GO:1990362	6.149747119504682
+GO:1990380	3.0772429989324603
+GO:1990381	2.9434164716336326
+GO:1990400	3.222392421336448
+GO:1990404	0.4405725913859815
+GO:1990405	2.807354922057604
+GO:1990407	2.1699250014423126
+GO:1990409	1.5849625007211563
+GO:1990411	5.321928094887363
+GO:1990422	7.247927513443585
+GO:1990424	10.787902559391432
+GO:1990430	11.890910228694334
+GO:1990439	0.4150374992788438
+GO:1990444	7.431845786965398
+GO:1990446	0.5849625007211563
+GO:1990447	1.5849625007211563
+GO:1990448	9.482471860035366
+GO:1990450	4.594946589293778
+GO:1990458	9.790348496751914
+GO:1990459	7.385554297054409
+GO:1990460	8.87098112422465
+GO:1990464	0.0
+GO:1990470	10.452241240430817
+GO:1990471	1.0
+GO:1990472	1.0
+GO:1990473	6.266786540694901
+GO:1990482	8.074141462752506
+GO:1990518	0.777607578663552
+GO:1990525	8.016808287686555
+GO:1990534	3.0874628412503395
+GO:1990538	1.5305147166987798
+GO:1990576	8.577428828035748
+GO:1990583	2.7548875021634687
+GO:1990585	5.679480099505446
+GO:1990593	9.067434360756522
+GO:1990594	7.247927513443585
+GO:1990595	8.577428828035748
+GO:1990599	2.146841388329271
+GO:1990600	1.5849625007211563
+GO:1990601	3.9541963103868754
+GO:1990605	10.709083812550343
+GO:1990606	0.0
+GO:1990609	2.584962500721156
+GO:1990610	11.237807473723135
+GO:1990624	7.638435913990472
+GO:1990631	9.607946718390856
+GO:1990634	6.839203788096944
+GO:1990640	3.9068905956085187
+GO:1990644	9.342074667999139
+GO:1990648	3.9068905956085187
+GO:1990649	2.1699250014423126
+GO:1990650	2.1699250014423126
+GO:1990651	1.3219280948873622
+GO:1990655	7.409390936137702
+GO:1990663	3.584962500721156
+GO:1990698	8.86418614465428
+GO:1990699	6.439623137557117
+GO:1990714	3.4882864813094825
+GO:1990715	7.230954434839872
+GO:1990725	11.02167404285837
+GO:1990738	8.214319120800766
+GO:1990749	8.94982671075911
+GO:1990756	0.3431445807970581
+GO:1990757	0.5849625007211563
+GO:1990760	0.0
+GO:1990763	11.212838323581696
+GO:1990782	2.782408564927373
+GO:1990808	8.431845786965399
+GO:1990814	0.0
+GO:1990817	1.9541963103868754
+GO:1990825	4.941447817644887
+GO:1990827	10.060245582053527
+GO:1990829	5.91169158187234
+GO:1990833	9.453270634010623
+GO:1990837	0.021971696763126207
+GO:1990838	4.20945336562895
+GO:1990841	3.0111405576052004
+GO:1990846	7.638435913990472
+GO:1990856	4.485426827170242
+GO:1990883	6.321928094887363
+GO:1990888	6.954196310386875
+GO:1990889	3.3692338096657193
+GO:1990890	8.87098112422465
+GO:1990915	3.584962500721156
+GO:1990919	6.954196310386875
+GO:1990929	8.436711542137214
+GO:1990930	2.584962500721156
+GO:1990931	1.2630344058337937
+GO:1990932	4.431049817267475
+GO:1990935	11.7534067049444
+GO:1990943	1.3219280948873622
+GO:1990948	0.4739311883324124
+GO:1990955	6.648657176038546
+GO:1990965	7.693486957499325
+GO:1990970	3.807354922057604
+GO:1990984	0.0
+GO:2001062	6.149747119504682
+GO:2001063	1.5849625007211563
+GO:2001064	5.491853096329675
+GO:2001065	3.8278190246173196
+GO:2001066	8.790348496751914
+GO:2001067	5.149747119504682
+GO:2001068	0.0
+GO:2001069	2.9798221180623696
+GO:2001070	3.1497471195046822
+GO:2001071	3.9068905956085187
+GO:2001072	1.5849625007211563
+GO:2001073	2.807354922057604
+GO:2001077	6.149747119504682
+GO:2001078	6.149747119504682
+GO:2001079	10.790348496751914
+GO:2001080	7.405141463136344
+GO:2001081	6.149747119504682
+GO:2001082	6.149747119504682
+GO:2001083	6.149747119504682
+GO:2001084	7.199672344836364
+GO:2001085	6.149747119504682
+GO:2001147	6.882643049361842
+GO:2001227	0.0
diff --git a/docs/source/architecture/evaluation.rst b/docs/source/architecture/evaluation.rst
new file mode 100644
index 0000000..0b99954
--- /dev/null
+++ b/docs/source/architecture/evaluation.rst
@@ -0,0 +1,139 @@
+CAFA Evaluation Protocol
+========================
+
+PROTEA implements the evaluation protocol used in the CAFA5 (Critical
+Assessment of protein Function Annotation) challenge. This page explains the
+protocol, the NK/LK/PK classification, and how to run an evaluation end-to-end
+within PROTEA.
+
+Background: the CAFA temporal holdout
+--------------------------------------
+
+CAFA evaluates protein function prediction by exploiting the growth of
+experimental GO annotations over time:
+
+- **t0** — an older annotation snapshot (the *reference* set). Methods may
+  use these annotations as training signal.
+- **t1** — a newer annotation snapshot (the *ground truth*). Proteins that
+  gained new experimental GO annotations between t0 and t1 form the test set
+  (the *delta*).
+
+Only annotations with experimental evidence codes are considered
+(EXP, IDA, IMP, IGI, IEP, IPI, and their ECO equivalents). Annotations with a
+NOT qualifier — meaning the protein is *not* associated with that term — are
+excluded, and their exclusion is propagated to all GO descendants through the
+``is_a`` and ``part_of`` relationships.
+
+NK / LK / PK classification
+-----------------------------
+
+A key feature of CAFA5 is that test proteins are not treated uniformly.
+Classification is determined **per (protein, namespace)**, where namespace is
+one of Molecular Function (MFO), Biological Process (BPO), or Cellular
+Component (CCO).
+
+**NK — No-Knowledge**
+   The protein had **no** experimental annotations in **any** namespace at t0.
+   All its new annotations across all namespaces form the NK ground truth.
+   Evaluating NK targets tests a method's ability to make predictions from
+   sequence alone, without any prior functional signal.
+
+**LK — Limited-Knowledge**
+   The protein had experimental annotations in **some** namespaces at t0, but
+   **not** in namespace S. It gained new annotations in S at t1. Those new
+   annotations in S are the LK ground truth for that (protein, S) pair.
+   Evaluating LK tests transfer across namespaces.
+
+**PK — Partial-Knowledge**
+   The protein already had experimental annotations in namespace S at t0, and
+   gained **additional** annotations in S at t1. Only the novel terms are
+   ground truth; the old terms are collected in a ``pk_known_terms.tsv`` file
+   and passed to ``cafaeval`` with the ``-known`` flag, which excludes them
+   from scoring. This prevents credit for simply repeating prior annotations.
+
+.. important::
+   A single protein can be **LK in one namespace and PK in another
+   simultaneously**. For example, a protein with MFO and BPO annotations at t0
+   that gains new CCO and BPO annotations at t1 will be LK for CCO and PK for
+   BPO.
+
+Toy example
+~~~~~~~~~~~
+
+.. code-block:: text
+
+   Protein P1 at t0:  MFO={GO:0003674}   BPO={}       CCO={}
+   Protein P1 at t1:  MFO={GO:0003674}   BPO={GO:0008150}  CCO={GO:0005575}
+
+   had_anything_old = True (had MFO)
+
+   Namespace BPO: old_BPO={}  → LK (empty at t0, gained GO:0008150)
+   Namespace CCO: old_CCO={}  → LK (empty at t0, gained GO:0005575)
+   Namespace MFO: no new terms → not in test set for this namespace
+
+   Protein P2 at t0:  BPO={GO:0006355}   (all others empty)
+   Protein P2 at t1:  BPO={GO:0006355, GO:0045893}
+
+   Namespace BPO: old_BPO={GO:0006355}  delta={GO:0045893}
+     → PK ground truth = {GO:0045893}
+     → pk_known = {GO:0006355}  (passed as -known)
+
+   Protein P3 at t0:  (no annotations in any namespace)
+   Protein P3 at t1:  MFO={GO:0003674}   BPO={GO:0008150}
+
+   had_anything_old = False → NK
+   NK ground truth = {GO:0003674, GO:0008150} (all new terms)
+
+Evaluation flow in PROTEA
+--------------------------
+
+.. code-block:: text
+
+   1. Load two GOA annotation sets (old = t0, new = t1).
+   2. POST /annotations/evaluation-sets/generate
+      → queues generate_evaluation_set job
+      → computes delta and creates EvaluationSet row with stats
+   3. Download delta-proteins.fasta (all NK+LK+PK sequences).
+   4. POST /jobs  (compute_embeddings, query_set_id=...)
+      → compute ESM-2 embeddings for delta proteins
+   5. POST /embeddings/predict  (predict_go_terms, query_set_id=...)
+      → run KNN GO transfer; creates PredictionSet
+   6. POST /annotations/evaluation-sets/{id}/run
+      → queues run_cafa_evaluation job
+      → runs cafaeval for NK, LK, PK; creates EvaluationResult
+   7. View results in the Evaluation UI or download artifacts.zip.
+
+The ``cafaeval`` command equivalent (for manual inspection):
+
+.. code-block:: bash
+
+   python -m cafaeval go-basic.obo predictions/ ground_truth_NK.tsv -out_dir results/NK
+   python -m cafaeval go-basic.obo predictions/ ground_truth_LK.tsv -out_dir results/LK
+   python -m cafaeval go-basic.obo predictions/ ground_truth_PK.tsv \
+     -known pk_known_terms.tsv -out_dir results/PK
+
+Data model
+----------
+
+``EvaluationSet``
+   Stores the (old\_annotation\_set\_id, new\_annotation\_set\_id) pair and a
+   JSONB ``stats`` dict with delta/NK/LK/PK protein and annotation counts.
+   Created by ``generate_evaluation_set``.
+
+``EvaluationResult``
+   Stores per-setting (NK/LK/PK) and per-namespace (MFO/BPO/CCO) metrics:
+   Fmax, precision, recall, τ (threshold), and coverage. Created by
+   ``run_cafa_evaluation``. Multiple ``EvaluationResult`` rows can exist per
+   ``EvaluationSet``, one per (prediction\_set, run).
+
+See :doc:`../reference/infrastructure` for the full ORM schema.
+
+Implementation reference
+-------------------------
+
+- Core logic: :mod:`protea.core.evaluation` — ``EvaluationData``,
+  ``compute_evaluation_data``
+- Operations: :mod:`protea.core.operations.generate_evaluation_set`,
+  :mod:`protea.core.operations.run_cafa_evaluation`
+- API router: ``protea/api/routers/annotations.py`` (download endpoints,
+  generate and run routes)
diff --git a/docs/source/architecture/index.rst b/docs/source/architecture/index.rst
index 307424a..548db7a 100644
--- a/docs/source/architecture/index.rst
+++ b/docs/source/architecture/index.rst
@@ -11,3 +11,4 @@ job lifecycle, and extension points.
    job_lifecycle
    data_model
    operations
+   evaluation
diff --git a/docs/source/architecture/operations.rst b/docs/source/architecture/operations.rst
index fc9c125..bef7075 100644
--- a/docs/source/architecture/operations.rst
+++ b/docs/source/architecture/operations.rst
@@ -594,6 +594,143 @@ Smoke-test operation. Accepts no required payload fields, emits a single
 ``ping.pong`` event, and returns immediately. Used to verify end-to-end
 connectivity of the job queue without touching the protein data tables.
 
+generate_evaluation_set
+-----------------------
+
+**Operation name:** ``generate_evaluation_set`` — queue: ``protea.jobs``
+
+Computes the CAFA-style evaluation delta between two ``AnnotationSet`` rows
+(old → new) and stores an ``EvaluationSet`` row with summary statistics.
+Follows the CAFA5 evaluation protocol exactly.
+
+Payload fields
+~~~~~~~~~~~~~~
+
+.. list-table::
+   :header-rows: 1
+   :widths: 35 10 55
+
+   * - Field
+     - Default
+     - Description
+   * - ``old_annotation_set_id``
+     - *(required)*
+     - UUID of the older annotation set (t0, used as reference).
+   * - ``new_annotation_set_id``
+     - *(required)*
+     - UUID of the newer annotation set (t1, ground truth source).
+
+Both annotation sets must share the same ``ontology_snapshot_id``; the
+operation raises ``ValueError`` otherwise.
+
+Execution flow
+~~~~~~~~~~~~~~
+
+.. code-block:: text
+
+   1. validate payload; load AnnotationSet rows; assert same ontology snapshot
+   2. call compute_evaluation_data() — see protea.core.evaluation:
+      a. load GO DAG children map (is_a / part_of only)
+      b. build NOT-propagation exclusion set (negated terms + GO descendants)
+      c. load experimental annotations for old and new sets, filtered by
+         exclusion set and evidence codes (EXP, IDA, IMP, IGI, IEP, IPI, …)
+      d. classify per (protein, namespace) into NK / LK / PK:
+         - NK: protein had NO annotations in any namespace at t0
+         - LK: protein had annotations in some namespaces but not in namespace S;
+               novel terms in S are LK ground truth
+         - PK: protein had annotations in namespace S at t0 and gained new terms
+               in S; novel terms are PK ground truth, old terms go to pk_known
+   3. emit stats (delta_proteins, nk/lk/pk counts and annotations)
+   4. INSERT EvaluationSet row with stats stored in JSONB
+   5. return OperationResult(result={evaluation_set_id, ...stats})
+
+NK/LK/PK classification note
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Classification is per **(protein, namespace)**, not globally per protein.
+A protein that had MFO and BPO annotations at t0 and gains new CCO and BPO
+annotations at t1 will be **LK** in CCO (no prior CCO annotations) and **PK**
+in BPO (had prior BPO annotations) simultaneously. This matches the CAFA5
+evaluation protocol.
+
+Ground truth files are computed on-demand by the download endpoints (not
+stored in the DB) using the same ``compute_evaluation_data`` logic.
+
+run_cafa_evaluation
+-------------------
+
+**Operation name:** ``run_cafa_evaluation`` — queue: ``protea.jobs``
+
+Runs the ``cafaeval`` evaluator against NK, LK, and PK ground-truth settings
+for a given ``EvaluationSet`` × ``PredictionSet`` pair. Persists an
+``EvaluationResult`` row with per-namespace Fmax, precision, recall, τ, and
+coverage for each setting.
+
+Payload fields
+~~~~~~~~~~~~~~
+
+.. list-table::
+   :header-rows: 1
+   :widths: 30 10 60
+
+   * - Field
+     - Default
+     - Description
+   * - ``evaluation_set_id``
+     - *(required)*
+     - UUID of the ``EvaluationSet`` to evaluate against.
+   * - ``prediction_set_id``
+     - *(required)*
+     - UUID of the ``PredictionSet`` containing predictions to score.
+   * - ``max_distance``
+     - ``null``
+     - Discard predictions with cosine distance above this threshold before
+       scoring (range 0 – 2). ``null`` = no threshold.
+   * - ``artifacts_dir``
+     - ``null``
+     - Filesystem path where cafaeval output files (PR curves, TSVs) are
+       written. ``null`` = artifacts are written to a temp directory and
+       discarded after the job.
+
+Execution flow
+~~~~~~~~~~~~~~
+
+.. code-block:: text
+
+   1. load EvaluationSet + PredictionSet + OntologySnapshot from DB
+   2. compute_evaluation_data() — same delta as generate_evaluation_set
+   3. download OBO file from snapshot.obo_url to a temp directory
+   4. write ground-truth TSVs: gt_NK.tsv, gt_LK.tsv, gt_PK.tsv,
+      known_terms.tsv, pk_known_terms.tsv
+   5. write predictions in CAFA format (protein \\t go_id \\t score),
+      score = max(0, 1 - distance), filtered to delta proteins only
+   6. session.commit() — releases DB connection before cafaeval forks workers
+   7. for each setting in (NK, LK, PK):
+      a. NK pass: cafa_eval(obo, predictions/, gt_NK.tsv)
+      b. LK pass: cafa_eval(obo, predictions/, gt_LK.tsv)
+      c. PK pass: cafa_eval(obo, predictions/, gt_PK.tsv, exclude=pk_known_terms.tsv)
+      d. parse per-namespace Fmax / precision / recall / τ / coverage
+      e. if artifacts_dir: write cafaeval PR curves and metric TSVs
+   8. INSERT EvaluationResult row with results dict (NK → {BPO, MFO, CCO})
+   9. return OperationResult(result={evaluation_result_id, results})
+
+PK known-terms
+~~~~~~~~~~~~~~
+
+For the PK pass, ``pk_known_terms.tsv`` is passed to ``cafa_eval`` as the
+``-known`` (``exclude``) argument. This file contains all experimental
+annotations from the *old* snapshot for PK proteins in the relevant namespace.
+``cafaeval`` uses this to exclude known annotations from scoring — methods
+that simply repeat prior annotations receive no credit for PK predictions.
+
+SIGTERM handling
+~~~~~~~~~~~~~~~~
+
+``cafaeval`` uses Python ``multiprocessing.Pool`` internally. Before each
+``cafa_eval()`` call, the operation temporarily resets ``SIGTERM`` and
+``SIGINT`` handlers to defaults so that forked pool workers can be terminated
+cleanly. The original handlers are restored afterwards.
+
 Registering a new operation
 ----------------------------
 
diff --git a/docs/source/reference/core.rst b/docs/source/reference/core.rst
index 0725c2c..799ed5d 100644
--- a/docs/source/reference/core.rst
+++ b/docs/source/reference/core.rst
@@ -120,10 +120,46 @@ traversals across a batch.
    :undoc-members:
    :show-inheritance:
 
+Evaluation
+----------
+
+``protea.core.evaluation`` implements the CAFA5 evaluation protocol for
+computing the ground-truth delta between two annotation snapshots.
+
+The module's central data structure is ``EvaluationData``, a frozen dataclass
+that holds the NK, LK, PK, known, and pk_known annotation dictionaries.
+Each dictionary maps a protein accession to a set of GO term IDs.
+
+``EvaluationData`` fields:
+
+- ``nk`` — delta annotations for No-Knowledge proteins (no prior annotations
+  in any namespace at t0).
+- ``lk`` — delta annotations for Limited-Knowledge proteins (had annotations
+  in some namespaces but gained new terms in a previously empty namespace).
+- ``pk`` — novel annotations for Partial-Knowledge proteins (gained new terms
+  in a namespace where they already had annotations).
+- ``pk_known`` — old experimental annotations for PK proteins in the
+  relevant namespaces; passed as ``-known`` to ``cafaeval`` to exclude them
+  from scoring.
+- ``known`` — all old experimental annotations flattened across namespaces;
+  available for download via the reference endpoint.
+
+The public entry point is ``compute_evaluation_data(session,
+old_annotation_set_id, new_annotation_set_id, ontology_snapshot_id)``.
+It loads the GO DAG for NOT-propagation, builds a per-namespace annotation
+map for both old and new sets, and classifies each (protein, namespace) pair
+into NK, LK, or PK. The same protein can appear in multiple categories across
+different namespaces simultaneously (e.g., LK in CCO and PK in BPO).
+
+.. automodule:: protea.core.evaluation
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
 Operations
 ----------
 
-PROTEA ships eight operations, all registered at worker startup in
+PROTEA ships ten operations, all registered at worker startup in
 ``scripts/worker.py``. Each operation is a class that implements the
 ``Operation`` protocol: a ``name`` string and an ``execute`` method.
 Operations are stateless with respect to infrastructure — they receive a
@@ -224,3 +260,27 @@ transactions.
    :members:
    :undoc-members:
    :show-inheritance:
+
+**generate_evaluation_set**
+   Computes the NK/LK/PK evaluation delta between two annotation sets using
+   the CAFA5 protocol (experimental evidence only, NOT-propagation through the
+   GO DAG, per-namespace classification). Stores an ``EvaluationSet`` row with
+   summary statistics. Ground-truth files are generated on-demand by the
+   download endpoints.
+
+.. automodule:: protea.core.operations.generate_evaluation_set
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+**run_cafa_evaluation**
+   Runs ``cafaeval`` for NK, LK, and PK settings against a given prediction
+   set. Downloads the OBO file, writes ground-truth and prediction TSVs, calls
+   ``cafa_eval()`` three times (NK and LK without ``-known``, PK with
+   ``pk_known_terms.tsv`` as ``-known``), and persists an ``EvaluationResult``
+   row with per-namespace Fmax, precision, recall, τ, and coverage.
+
+.. automodule:: protea.core.operations.run_cafa_evaluation
+   :members:
+   :undoc-members:
+   :show-inheritance:
diff --git a/protea/api/app.py b/protea/api/app.py
index a52e2e5..2d075bb 100644
--- a/protea/api/app.py
+++ b/protea/api/app.py
@@ -14,6 +14,8 @@
 from protea.api.routers import maintenance as maintenance_router
 from protea.api.routers import proteins as proteins_router
 from protea.api.routers import query_sets as query_sets_router
+from protea.api.routers import scoring as scoring_router
+from protea.api.routers import support as support_router
 from protea.infrastructure.session import build_session_factory
 from protea.infrastructure.settings import load_settings
 
@@ -37,13 +39,36 @@ def create_app(project_root: Path | None = None) -> FastAPI:
         ),
         contact={"name": "PROTEA Team", "email": "contact@protea.example.org"},
         openapi_tags=[
-            {"name": "jobs", "description": "Job queue lifecycle — create, monitor, and cancel operations."},
+            {
+                "name": "jobs",
+                "description": "Job queue lifecycle — create, monitor, and cancel operations.",
+            },
             {"name": "proteins", "description": "UniProt protein lookup and aggregate statistics."},
-            {"name": "annotations", "description": "GO ontology snapshots, annotation sets, and GO subgraph queries."},
-            {"name": "embeddings", "description": "Embedding configs, GPU compute jobs, and prediction sets management."},
-            {"name": "query-sets", "description": "User-uploaded FASTA datasets for custom prediction queries."},
-            {"name": "maintenance", "description": "Housekeeping — identify and remove orphaned sequences or embeddings."},
-            {"name": "admin", "description": "Destructive admin operations (DB reset). Use with caution."},
+            {
+                "name": "annotations",
+                "description": "GO ontology snapshots, annotation sets, and GO subgraph queries.",
+            },
+            {
+                "name": "embeddings",
+                "description": "Embedding configs, GPU compute jobs, and prediction sets management.",
+            },
+            {
+                "name": "query-sets",
+                "description": "User-uploaded FASTA datasets for custom prediction queries.",
+            },
+            {
+                "name": "maintenance",
+                "description": "Housekeeping — identify and remove orphaned sequences or embeddings.",
+            },
+            {
+                "name": "admin",
+                "description": "Destructive admin operations (DB reset). Use with caution.",
+            },
+            {
+                "name": "scoring",
+                "description": "Scoring configs, scored prediction export, and CAFA metrics.",
+            },
+            {"name": "support", "description": "Community thumbs-up and comments."},
         ],
     )
     app.state.session_factory = factory
@@ -65,6 +90,8 @@ def create_app(project_root: Path | None = None) -> FastAPI:
     app.include_router(query_sets_router.router)
     app.include_router(maintenance_router.router)
     app.include_router(admin_router.router)
+    app.include_router(scoring_router.router)
+    app.include_router(support_router.router)
 
     sphinx_build = project_root / "docs" / "build" / "html"
     if sphinx_build.exists():
diff --git a/protea/api/routers/admin.py b/protea/api/routers/admin.py
index 5927bea..3a3940b 100644
--- a/protea/api/routers/admin.py
+++ b/protea/api/routers/admin.py
@@ -21,6 +21,7 @@ def reset_db(request: Request) -> dict:
 
     # 1. Drop + recreate schema using a raw connection (outside SQLAlchemy pool)
     import psycopg
+
     with psycopg.connect(settings.db_url.replace("+psycopg", ""), autocommit=True) as conn:
         conn.execute("DROP SCHEMA public CASCADE")
         conn.execute("CREATE SCHEMA public")
diff --git a/protea/api/routers/annotations.py b/protea/api/routers/annotations.py
index 3617501..d4f50a3 100644
--- a/protea/api/routers/annotations.py
+++ b/protea/api/routers/annotations.py
@@ -29,6 +29,8 @@
 from protea.infrastructure.orm.models.annotation.ontology_snapshot import OntologySnapshot
 from protea.infrastructure.orm.models.annotation.protein_go_annotation import ProteinGOAnnotation
 from protea.infrastructure.orm.models.job import Job, JobEvent
+from protea.infrastructure.orm.models.protein.protein import Protein
+from protea.infrastructure.orm.models.sequence.sequence import Sequence
 from protea.infrastructure.queue.publisher import publish_job
 from protea.infrastructure.session import session_scope
 
@@ -60,6 +62,7 @@ def get_artifacts_dir(request: Request) -> Path:
 
 # ── Ontology Snapshots ────────────────────────────────────────────────────────
 
+
 @router.get("/snapshots", summary="List ontology snapshots")
 def list_snapshots(
     factory: sessionmaker[Session] = Depends(get_session_factory),
@@ -85,6 +88,7 @@ def list_snapshots(
                 "id": str(s.id),
                 "obo_url": s.obo_url,
                 "obo_version": s.obo_version,
+                "ia_url": s.ia_url,
                 "loaded_at": s.loaded_at.isoformat(),
                 "go_term_count": cnt or 0,
             }
@@ -113,11 +117,51 @@ def get_snapshot(
             "id": str(s.id),
             "obo_url": s.obo_url,
             "obo_version": s.obo_version,
+            "ia_url": s.ia_url,
             "loaded_at": s.loaded_at.isoformat(),
             "go_term_count": term_count,
         }
 
 
+@router.patch("/snapshots/{snapshot_id}/ia-url", summary="Set IA URL on an ontology snapshot")
+def set_snapshot_ia_url(
+    snapshot_id: UUID,
+    body: dict[str, Any],
+    factory: sessionmaker[Session] = Depends(get_session_factory),
+) -> dict[str, Any]:
+    """Associate an Information Accretion (IA) file URL with an existing ontology snapshot.
+
+    The IA file contains per-term information-content weights (two columns:
+    ``go_id``, ``ia_value``) and is published alongside each CAFA benchmark
+    (e.g. ``IA_cafa6.tsv``).  Once set, ``run_cafa_evaluation`` picks it up
+    automatically for every evaluation that uses this snapshot — no need to
+    pass ``ia_file`` in the job payload.
+
+    Pass ``{"ia_url": null}`` to clear the association (evaluations will fall
+    back to uniform IC=1).
+
+    This endpoint only touches ``ia_url``; the OBO file and GO term data are
+    not affected.
+    """
+    ia_url = body.get("ia_url")
+    if "ia_url" not in body:
+        raise HTTPException(
+            status_code=422, detail="Body must contain 'ia_url' key (string or null)"
+        )
+
+    with session_scope(factory) as session:
+        s = session.get(OntologySnapshot, snapshot_id)
+        if s is None:
+            raise HTTPException(status_code=404, detail="OntologySnapshot not found")
+        s.ia_url = ia_url or None
+        session.flush()
+        return {
+            "id": str(s.id),
+            "obo_version": s.obo_version,
+            "ia_url": s.ia_url,
+        }
+
+
 @router.post("/snapshots/load", summary="Trigger ontology snapshot load")
 def load_ontology_snapshot(
     body: dict[str, Any],
@@ -139,11 +183,13 @@ def load_ontology_snapshot(
         session.add(job)
         session.flush()
         job_id = job.id
-        session.add(JobEvent(
-            job_id=job_id,
-            event="job.created",
-            fields={"operation": "load_ontology_snapshot", "queue": _JOBS_QUEUE},
-        ))
+        session.add(
+            JobEvent(
+                job_id=job_id,
+                event="job.created",
+                fields={"operation": "load_ontology_snapshot", "queue": _JOBS_QUEUE},
+            )
+        )
 
     publish_job(amqp_url, _JOBS_QUEUE, job_id)
     return {"id": str(job_id), "status": "queued"}
@@ -151,6 +197,7 @@ def load_ontology_snapshot(
 
 # ── Annotation Sets ───────────────────────────────────────────────────────────
 
+
 @router.get("/sets", summary="List annotation sets")
 def list_annotation_sets(
     source: str | None = Query(default=None, description="Filter by source: `goa` or `quickgo`."),
@@ -260,11 +307,13 @@ def load_goa_annotations(
         session.add(job)
         session.flush()
         job_id = job.id
-        session.add(JobEvent(
-            job_id=job_id,
-            event="job.created",
-            fields={"operation": "load_goa_annotations", "queue": _JOBS_QUEUE},
-        ))
+        session.add(
+            JobEvent(
+                job_id=job_id,
+                event="job.created",
+                fields={"operation": "load_goa_annotations", "queue": _JOBS_QUEUE},
+            )
+        )
 
     publish_job(amqp_url, _JOBS_QUEUE, job_id)
     return {"id": str(job_id), "status": "queued"}
@@ -288,11 +337,13 @@ def load_quickgo_annotations(
         session.add(job)
         session.flush()
         job_id = job.id
-        session.add(JobEvent(
-            job_id=job_id,
-            event="job.created",
-            fields={"operation": "load_quickgo_annotations", "queue": _JOBS_QUEUE},
-        ))
+        session.add(
+            JobEvent(
+                job_id=job_id,
+                event="job.created",
+                fields={"operation": "load_quickgo_annotations", "queue": _JOBS_QUEUE},
+            )
+        )
 
     publish_job(amqp_url, _JOBS_QUEUE, job_id)
     return {"id": str(job_id), "status": "queued"}
@@ -323,11 +374,13 @@ def generate_evaluation_set(
         session.add(job)
         session.flush()
         job_id = job.id
-        session.add(JobEvent(
-            job_id=job_id,
-            event="job.created",
-            fields={"operation": "generate_evaluation_set", "queue": _JOBS_QUEUE},
-        ))
+        session.add(
+            JobEvent(
+                job_id=job_id,
+                event="job.created",
+                fields={"operation": "generate_evaluation_set", "queue": _JOBS_QUEUE},
+            )
+        )
 
     publish_job(amqp_url, _JOBS_QUEUE, job_id)
     return {"id": str(job_id), "status": "queued"}
@@ -339,11 +392,7 @@ def list_evaluation_sets(
 ) -> list[dict[str, Any]]:
     """List all evaluation sets, newest first."""
     with session_scope(factory) as session:
-        rows = (
-            session.query(EvaluationSet)
-            .order_by(EvaluationSet.created_at.desc())
-            .all()
-        )
+        rows = session.query(EvaluationSet).order_by(EvaluationSet.created_at.desc()).all()
         return [
             {
                 "id": str(e.id),
@@ -357,6 +406,34 @@ def list_evaluation_sets(
         ]
 
 
+@router.delete("/evaluation-sets/{eval_id}", summary="Delete an evaluation set", status_code=204)
+def delete_evaluation_set(
+    eval_id: UUID,
+    factory: sessionmaker[Session] = Depends(get_session_factory),
+    artifacts_dir: Path = Depends(get_artifacts_dir),
+) -> None:
+    """Delete an evaluation set and all its results. Cascades to EvaluationResult rows."""
+    with session_scope(factory) as session:
+        e = session.get(EvaluationSet, eval_id)
+        if e is None:
+            raise HTTPException(status_code=404, detail="EvaluationSet not found")
+        # Collect result IDs to clean up artifact dirs
+        result_ids = [
+            str(r.id)
+            for r in session.query(EvaluationResult)
+            .filter(EvaluationResult.evaluation_set_id == eval_id)
+            .all()
+        ]
+        session.delete(e)
+
+    import shutil
+
+    for rid in result_ids:
+        result_dir = artifacts_dir / rid
+        if result_dir.exists():
+            shutil.rmtree(result_dir, ignore_errors=True)
+
+
 @router.get("/evaluation-sets/{eval_id}", summary="Get evaluation set details")
 def get_evaluation_set(
     eval_id: UUID,
@@ -399,7 +476,9 @@ def download_gt_nk(
         e = _eval_set_or_404(session, eval_id)
         ann_old = session.get(AnnotationSet, e.old_annotation_set_id)
         data = compute_evaluation_data(
-            session, e.old_annotation_set_id, e.new_annotation_set_id,
+            session,
+            e.old_annotation_set_id,
+            e.new_annotation_set_id,
             ann_old.ontology_snapshot_id,
         )
         lines = [
@@ -430,7 +509,9 @@ def download_gt_lk(
         e = _eval_set_or_404(session, eval_id)
         ann_old = session.get(AnnotationSet, e.old_annotation_set_id)
         data = compute_evaluation_data(
-            session, e.old_annotation_set_id, e.new_annotation_set_id,
+            session,
+            e.old_annotation_set_id,
+            e.new_annotation_set_id,
             ann_old.ontology_snapshot_id,
         )
         lines = [
@@ -448,17 +529,36 @@ def download_gt_lk(
 @router.get(
     "/evaluation-sets/{eval_id}/ground-truth-PK.tsv",
     response_class=StreamingResponse,
-    summary="Download PK ground truth (CAFA format, same as LK)",
+    summary="Download PK ground truth (CAFA format)",
 )
 def download_gt_pk(
     eval_id: UUID,
     factory: sessionmaker[Session] = Depends(get_session_factory),
 ) -> StreamingResponse:
-    """Download Partial-Knowledge ground truth (identical to LK at annotation level).
-    Pass ``known-terms.tsv`` as ``-known`` to the CAFA evaluator for PK scoring.
+    """Download Partial-Knowledge ground truth: proteins that gained new terms in a
+    namespace where they already had experimental annotations at t0.
+    Use together with ``known-terms.tsv`` passed as ``-known`` to the CAFA evaluator.
     Format: ``protein_accession\\tgo_id`` (no header, 2 columns).
     """
-    return download_gt_lk(eval_id=eval_id, factory=factory)
+    with session_scope(factory) as session:
+        e = _eval_set_or_404(session, eval_id)
+        ann_old = session.get(AnnotationSet, e.old_annotation_set_id)
+        data = compute_evaluation_data(
+            session,
+            e.old_annotation_set_id,
+            e.new_annotation_set_id,
+            ann_old.ontology_snapshot_id,
+        )
+        lines = [
+            f"{protein}\t{go_id}\n"
+            for protein, go_ids in sorted(data.pk.items())
+            for go_id in sorted(go_ids)
+        ]
+    return StreamingResponse(
+        iter(lines),
+        media_type="text/tab-separated-values",
+        headers={"Content-Disposition": 'attachment; filename="ground_truth_PK.tsv"'},
+    )
 
 
 @router.get(
@@ -478,7 +578,9 @@ def download_known_terms(
         e = _eval_set_or_404(session, eval_id)
         ann_old = session.get(AnnotationSet, e.old_annotation_set_id)
         data = compute_evaluation_data(
-            session, e.old_annotation_set_id, e.new_annotation_set_id,
+            session,
+            e.old_annotation_set_id,
+            e.new_annotation_set_id,
             ann_old.ontology_snapshot_id,
         )
         lines = [
@@ -493,8 +595,90 @@ def download_known_terms(
     )
 
 
+@router.get(
+    "/evaluation-sets/{eval_id}/delta-proteins.fasta",
+    response_class=StreamingResponse,
+    summary="Download delta proteins as FASTA",
+)
+def download_delta_fasta(
+    eval_id: UUID,
+    category: str = Query(
+        default="all", description="Which proteins to include: `nk`, `lk`, or `all` (default)."
+    ),
+    factory: sessionmaker[Session] = Depends(get_session_factory),
+) -> StreamingResponse:
+    """Download the amino-acid sequences of delta proteins (NK and/or LK) as FASTA.
+
+    Only proteins whose sequence is already stored in the database are included.
+    Header format: ``>ACCESSION entry_name OS=organism OX=taxonomy_id (NK|LK)``
+    """
+    with session_scope(factory) as session:
+        e = _eval_set_or_404(session, eval_id)
+        ann_old = session.get(AnnotationSet, e.old_annotation_set_id)
+        data = compute_evaluation_data(
+            session,
+            e.old_annotation_set_id,
+            e.new_annotation_set_id,
+            ann_old.ontology_snapshot_id,
+        )
+
+        # Collect requested accessions with their NK/LK/PK label
+        accession_label: dict[str, str] = {}
+        if category in ("nk", "all"):
+            for acc in data.nk:
+                accession_label[acc] = "NK"
+        if category in ("lk", "all"):
+            for acc in data.lk:
+                accession_label[acc] = "LK"
+        if category in ("pk", "all"):
+            for acc in data.pk:
+                accession_label.setdefault(acc, "PK")  # may also be LK in another ns
+
+        if not accession_label:
+            return StreamingResponse(
+                iter([]),
+                media_type="text/plain",
+                headers={
+                    "Content-Disposition": f'attachment; filename="delta_proteins_{category}.fasta"'
+                },
+            )
+
+        # Fetch proteins + sequences in one query
+        rows = (
+            session.query(Protein, Sequence)
+            .join(Sequence, Protein.sequence_id == Sequence.id)
+            .filter(Protein.accession.in_(list(accession_label.keys())))
+            .order_by(Protein.accession)
+            .all()
+        )
+
+        lines: list[str] = []
+        for protein, seq in rows:
+            label = accession_label.get(protein.accession, "")
+            parts = [protein.accession]
+            if protein.entry_name:
+                parts.append(protein.entry_name)
+            if protein.organism:
+                parts.append(f"OS={protein.organism}")
+            if protein.taxonomy_id:
+                parts.append(f"OX={protein.taxonomy_id}")
+            parts.append(f"({label})")
+            lines.append(f">{' '.join(parts)}\n")
+            # Wrap sequence at 60 chars per line (standard FASTA)
+            s = seq.sequence
+            for i in range(0, len(s), 60):
+                lines.append(s[i : i + 60] + "\n")
+
+    return StreamingResponse(
+        iter(lines),
+        media_type="text/plain",
+        headers={"Content-Disposition": f'attachment; filename="delta_proteins_{category}.fasta"'},
+    )
+
+
 # ── CAFA Evaluation Results ───────────────────────────────────────────────────
 
+
 @router.post(
     "/evaluation-sets/{eval_id}/run",
     summary="Queue a run_cafa_evaluation job",
@@ -524,11 +708,13 @@ def run_cafa_evaluation(
         session.add(job)
         session.flush()
         job_id = job.id
-        session.add(JobEvent(
-            job_id=job_id,
-            event="job.created",
-            fields={"operation": "run_cafa_evaluation", "queue": _JOBS_QUEUE},
-        ))
+        session.add(
+            JobEvent(
+                job_id=job_id,
+                event="job.created",
+                fields={"operation": "run_cafa_evaluation", "queue": _JOBS_QUEUE},
+            )
+        )
 
     publish_job(amqp_url, _JOBS_QUEUE, job_id)
     return {"id": str(job_id), "status": "queued"}
@@ -565,7 +751,7 @@ def _rows() -> Iterator[str]:
         return StreamingResponse(
             _rows(),
             media_type="text/tab-separated-values",
-            headers={"Content-Disposition": f"attachment; filename=\"metrics_{result_id}.tsv\""},
+            headers={"Content-Disposition": f'attachment; filename="metrics_{result_id}.tsv"'},
         )
 
 
@@ -599,7 +785,7 @@ def _zip_stream() -> Iterator[bytes]:
     return StreamingResponse(
         _zip_stream(),
         media_type="application/zip",
-        headers={"Content-Disposition": f"attachment; filename=\"artifacts_{result_id}.zip\""},
+        headers={"Content-Disposition": f'attachment; filename="artifacts_{result_id}.zip"'},
     )
 
 
@@ -625,6 +811,7 @@ def list_evaluation_results(
                 "id": str(r.id),
                 "evaluation_set_id": str(r.evaluation_set_id),
                 "prediction_set_id": str(r.prediction_set_id),
+                "scoring_config_id": str(r.scoring_config_id) if r.scoring_config_id else None,
                 "job_id": str(r.job_id) if r.job_id else None,
                 "created_at": r.created_at.isoformat(),
                 "results": r.results,
@@ -633,8 +820,34 @@ def list_evaluation_results(
         ]
 
 
+@router.delete(
+    "/evaluation-sets/{eval_id}/results/{result_id}",
+    summary="Delete an evaluation result",
+    status_code=204,
+)
+def delete_evaluation_result(
+    eval_id: UUID,
+    result_id: UUID,
+    factory: sessionmaker[Session] = Depends(get_session_factory),
+    artifacts_dir: Path = Depends(get_artifacts_dir),
+) -> None:
+    with session_scope(factory) as session:
+        result = session.get(EvaluationResult, result_id)
+        if result is None or result.evaluation_set_id != eval_id:
+            raise HTTPException(status_code=404, detail="EvaluationResult not found")
+        session.delete(result)
+
+    # Remove artifact directory if present (best-effort)
+    result_dir = artifacts_dir / str(result_id)
+    if result_dir.exists():
+        import shutil
+
+        shutil.rmtree(result_dir, ignore_errors=True)
+
+
 # ── GO subgraph ───────────────────────────────────────────────────────────────
 
+
 @router.get("/snapshots/{snapshot_id}/subgraph")
 def get_go_subgraph(
     snapshot_id: UUID,
@@ -651,10 +864,14 @@ def get_go_subgraph(
         query_go_ids = {g.strip() for g in go_ids.split(",") if g.strip()}
 
         # Resolve initial term DB ids
-        seed_terms = session.query(GOTerm).filter(
-            GOTerm.ontology_snapshot_id == snapshot_id,
-            GOTerm.go_id.in_(query_go_ids),
-        ).all()
+        seed_terms = (
+            session.query(GOTerm)
+            .filter(
+                GOTerm.ontology_snapshot_id == snapshot_id,
+                GOTerm.go_id.in_(query_go_ids),
+            )
+            .all()
+        )
 
         if not seed_terms:
             return {"nodes": [], "edges": []}
@@ -668,18 +885,24 @@ def get_go_subgraph(
         for _ in range(depth):
             if not frontier:
                 break
-            rels = session.query(GOTermRelationship).filter(
-                GOTermRelationship.ontology_snapshot_id == snapshot_id,
-                GOTermRelationship.child_go_term_id.in_(frontier),
-            ).all()
+            rels = (
+                session.query(GOTermRelationship)
+                .filter(
+                    GOTermRelationship.ontology_snapshot_id == snapshot_id,
+                    GOTermRelationship.child_go_term_id.in_(frontier),
+                )
+                .all()
+            )
 
             parent_ids = {r.parent_go_term_id for r in rels} - visited_ids
             for r in rels:
-                all_edges.append({
-                    "source": r.child_go_term_id,
-                    "target": r.parent_go_term_id,
-                    "relation_type": r.relation_type,
-                })
+                all_edges.append(
+                    {
+                        "source": r.child_go_term_id,
+                        "target": r.parent_go_term_id,
+                        "relation_type": r.relation_type,
+                    }
+                )
 
             if parent_ids:
                 parents = session.query(GOTerm).filter(GOTerm.id.in_(parent_ids)).all()
diff --git a/protea/api/routers/embeddings.py b/protea/api/routers/embeddings.py
index 2d8a936..6d6ddec 100644
--- a/protea/api/routers/embeddings.py
+++ b/protea/api/routers/embeddings.py
@@ -101,7 +101,8 @@ def _validate_embedding_config_body(body: dict[str, Any]) -> dict[str, Any]:
 
     # Cross-field: overlap must be strictly less than chunk_size
     if (
-        isinstance(chunk_size, int) and isinstance(chunk_overlap, int)
+        isinstance(chunk_size, int)
+        and isinstance(chunk_overlap, int)
         and chunk_overlap >= chunk_size
     ):
         errors.append(
@@ -151,23 +152,22 @@ def _config_to_dict(c: EmbeddingConfig, embedding_count: int | None = None) -> d
 
 # ── Embedding Configs ─────────────────────────────────────────────────────────
 
+
 @router.get("/configs", summary="List embedding configs")
 def list_embedding_configs(
     factory: sessionmaker[Session] = Depends(get_session_factory),
 ) -> list[dict[str, Any]]:
     """List all embedding configurations with their stored embedding counts, newest first."""
     with session_scope(factory) as session:
-        rows = (
-            session.query(EmbeddingConfig)
-            .order_by(EmbeddingConfig.created_at.desc())
-            .all()
-        )
+        rows = session.query(EmbeddingConfig).order_by(EmbeddingConfig.created_at.desc()).all()
         counts = {
             config_id: cnt
             for config_id, cnt in session.query(
                 SequenceEmbedding.embedding_config_id,
                 func.count(SequenceEmbedding.id),
-            ).group_by(SequenceEmbedding.embedding_config_id).all()
+            )
+            .group_by(SequenceEmbedding.embedding_config_id)
+            .all()
         }
         return [_config_to_dict(c, embedding_count=counts.get(c.id, 0)) for c in rows]
 
@@ -267,6 +267,7 @@ def delete_embedding_config(
 
 # ── Predict ───────────────────────────────────────────────────────────────────
 
+
 @router.post("/predict", summary="Trigger GO term prediction")
 def predict_go_terms(
     body: dict[str, Any],
@@ -281,8 +282,11 @@ def predict_go_terms(
 
     Required body fields: `embedding_config_id`, `annotation_set_id`, `ontology_snapshot_id`.
     Optional: `query_set_id` (FASTA upload), `limit_per_entry`, `distance_threshold`,
-    `batch_size`, `search_backend`, `compute_alignments`, `compute_taxonomy`.
+    `batch_size`, `search_backend`, `compute_alignments`, `compute_taxonomy`,
+    `aspect_separated_knn` (bool, default false — builds one KNN index per GO aspect to
+    guarantee BPO/MFO/CCO coverage even when unified nearest neighbours carry only one aspect).
     """
+
     def _parse_uuid(key: str) -> UUID:
         raw = body.get(key)
         try:
@@ -306,11 +310,13 @@ def _parse_uuid(key: str) -> UUID:
         session.add(job)
         session.flush()
         job_id = job.id
-        session.add(JobEvent(
-            job_id=job_id,
-            event="job.created",
-            fields={"operation": "predict_go_terms", "queue": _JOBS_QUEUE},
-        ))
+        session.add(
+            JobEvent(
+                job_id=job_id,
+                event="job.created",
+                fields={"operation": "predict_go_terms", "queue": _JOBS_QUEUE},
+            )
+        )
 
     publish_job(amqp_url, _JOBS_QUEUE, job_id)
     return {"id": str(job_id), "status": "queued"}
@@ -318,6 +324,7 @@ def _parse_uuid(key: str) -> UUID:
 
 # ── Prediction Sets ───────────────────────────────────────────────────────────
 
+
 @router.get("/prediction-sets", summary="List prediction sets")
 def list_prediction_sets(
     factory: sessionmaker[Session] = Depends(get_session_factory),
@@ -325,29 +332,39 @@ def list_prediction_sets(
     """List the 100 most recent prediction sets with their GO prediction counts."""
     with session_scope(factory) as session:
         rows = (
-            session.query(PredictionSet)
+            session.query(PredictionSet, EmbeddingConfig, AnnotationSet, OntologySnapshot)
+            .join(EmbeddingConfig, PredictionSet.embedding_config_id == EmbeddingConfig.id)
+            .join(AnnotationSet, PredictionSet.annotation_set_id == AnnotationSet.id)
+            .join(OntologySnapshot, PredictionSet.ontology_snapshot_id == OntologySnapshot.id)
             .order_by(PredictionSet.created_at.desc())
             .limit(100)
             .all()
         )
         result = []
-        for ps in rows:
+        for ps, ec, ann, snap in rows:
             prediction_count = (
                 session.query(func.count(GOPrediction.id))
                 .filter(GOPrediction.prediction_set_id == ps.id)
                 .scalar()
             )
-            result.append({
-                "id": str(ps.id),
-                "embedding_config_id": str(ps.embedding_config_id),
-                "annotation_set_id": str(ps.annotation_set_id),
-                "ontology_snapshot_id": str(ps.ontology_snapshot_id),
-                "query_set_id": str(ps.query_set_id) if ps.query_set_id else None,
-                "limit_per_entry": ps.limit_per_entry,
-                "distance_threshold": ps.distance_threshold,
-                "created_at": ps.created_at.isoformat(),
-                "prediction_count": prediction_count,
-            })
+            result.append(
+                {
+                    "id": str(ps.id),
+                    "embedding_config_id": str(ps.embedding_config_id),
+                    "embedding_config_name": ec.model_name,
+                    "annotation_set_id": str(ps.annotation_set_id),
+                    "annotation_set_label": f"{ann.source} {ann.source_version}"
+                    if ann.source_version
+                    else ann.source,
+                    "ontology_snapshot_id": str(ps.ontology_snapshot_id),
+                    "ontology_snapshot_version": snap.obo_version,
+                    "query_set_id": str(ps.query_set_id) if ps.query_set_id else None,
+                    "limit_per_entry": ps.limit_per_entry,
+                    "distance_threshold": ps.distance_threshold,
+                    "created_at": ps.created_at.isoformat(),
+                    "prediction_count": prediction_count,
+                }
+            )
         return result
 
 
@@ -485,7 +502,9 @@ def list_prediction_set_proteins(
         }
 
 
-@router.get("/prediction-sets/{set_id}/proteins/{accession}", summary="Get predictions for one protein")
+@router.get(
+    "/prediction-sets/{set_id}/proteins/{accession}", summary="Get predictions for one protein"
+)
 def get_protein_predictions(
     set_id: UUID,
     accession: str,
@@ -547,7 +566,9 @@ def get_protein_predictions(
         ]
 
 
-@router.get("/prediction-sets/{set_id}/go-terms", summary="GO term distribution in a prediction set")
+@router.get(
+    "/prediction-sets/{set_id}/go-terms", summary="GO term distribution in a prediction set"
+)
 def get_go_term_distribution(
     set_id: UUID,
     limit: int = 50,
@@ -643,7 +664,9 @@ def download_predictions_tsv(
     set_id: UUID,
     accession: str | None = Query(None, description="Filter to a single query protein accession"),
     aspect: str | None = Query(None, description="Filter by GO aspect: F, P, or C"),
-    max_distance: float | None = Query(None, description="Only include predictions with distance ≤ this value"),
+    max_distance: float | None = Query(
+        None, description="Only include predictions with distance ≤ this value"
+    ),
     factory: sessionmaker[Session] = Depends(get_session_factory),
 ) -> StreamingResponse:
     """Stream all GO predictions for a prediction set as a tab-separated file.
@@ -688,34 +711,38 @@ def _generate():
             for pred, gt in q.yield_per(1000):
                 buf = io.StringIO()
                 writer = csv.writer(buf, delimiter="\t", lineterminator="\n")
-                writer.writerow([
-                    pred.protein_accession,
-                    gt.go_id,
-                    gt.name,
-                    gt.aspect,
-                    pred.distance,
-                    pred.ref_protein_accession,
-                    pred.qualifier or "",
-                    pred.evidence_code or "",
-                    _fmt(pred.identity_nw),
-                    _fmt(pred.similarity_nw),
-                    _fmt(pred.alignment_score_nw),
-                    _fmt(pred.gaps_pct_nw),
-                    _fmt(pred.alignment_length_nw),
-                    _fmt(pred.identity_sw),
-                    _fmt(pred.similarity_sw),
-                    _fmt(pred.alignment_score_sw),
-                    _fmt(pred.gaps_pct_sw),
-                    _fmt(pred.alignment_length_sw),
-                    pred.length_query if pred.length_query is not None else "",
-                    pred.length_ref if pred.length_ref is not None else "",
-                    pred.query_taxonomy_id if pred.query_taxonomy_id is not None else "",
-                    pred.ref_taxonomy_id if pred.ref_taxonomy_id is not None else "",
-                    pred.taxonomic_lca if pred.taxonomic_lca is not None else "",
-                    pred.taxonomic_distance if pred.taxonomic_distance is not None else "",
-                    pred.taxonomic_common_ancestors if pred.taxonomic_common_ancestors is not None else "",
-                    pred.taxonomic_relation or "",
-                ])
+                writer.writerow(
+                    [
+                        pred.protein_accession,
+                        gt.go_id,
+                        gt.name,
+                        gt.aspect,
+                        pred.distance,
+                        pred.ref_protein_accession,
+                        pred.qualifier or "",
+                        pred.evidence_code or "",
+                        _fmt(pred.identity_nw),
+                        _fmt(pred.similarity_nw),
+                        _fmt(pred.alignment_score_nw),
+                        _fmt(pred.gaps_pct_nw),
+                        _fmt(pred.alignment_length_nw),
+                        _fmt(pred.identity_sw),
+                        _fmt(pred.similarity_sw),
+                        _fmt(pred.alignment_score_sw),
+                        _fmt(pred.gaps_pct_sw),
+                        _fmt(pred.alignment_length_sw),
+                        pred.length_query if pred.length_query is not None else "",
+                        pred.length_ref if pred.length_ref is not None else "",
+                        pred.query_taxonomy_id if pred.query_taxonomy_id is not None else "",
+                        pred.ref_taxonomy_id if pred.ref_taxonomy_id is not None else "",
+                        pred.taxonomic_lca if pred.taxonomic_lca is not None else "",
+                        pred.taxonomic_distance if pred.taxonomic_distance is not None else "",
+                        pred.taxonomic_common_ancestors
+                        if pred.taxonomic_common_ancestors is not None
+                        else "",
+                        pred.taxonomic_relation or "",
+                    ]
+                )
                 yield buf.getvalue()
 
     filename = f"predictions_{set_id}.tsv"
@@ -740,9 +767,13 @@ def _fmt(v: float | None) -> str:
 )
 def download_predictions_cafa(
     set_id: UUID,
-    eval_id: UUID | None = Query(None, description="Filter to delta proteins from this EvaluationSet."),
+    eval_id: UUID | None = Query(
+        None, description="Filter to delta proteins from this EvaluationSet."
+    ),
     aspect: str | None = Query(None, description="Filter by GO aspect: F, P, or C"),
-    max_distance: float | None = Query(None, description="Only include predictions with distance ≤ this value"),
+    max_distance: float | None = Query(
+        None, description="Only include predictions with distance ≤ this value"
+    ),
     factory: sessionmaker[Session] = Depends(get_session_factory),
 ) -> StreamingResponse:
     """Stream predictions in CAFA format: ``protein_accession\\tgo_id\\tscore``.
@@ -771,7 +802,9 @@ def download_predictions_cafa(
                 raise HTTPException(status_code=404, detail="EvaluationSet not found")
             ann_old = _check.get(AnnotationSet, e.old_annotation_set_id)
             data = compute_evaluation_data(
-                _check, e.old_annotation_set_id, e.new_annotation_set_id,
+                _check,
+                e.old_annotation_set_id,
+                e.new_annotation_set_id,
                 ann_old.ontology_snapshot_id,
             )
             delta_proteins = set(data.nk) | set(data.lk)
diff --git a/protea/api/routers/jobs.py b/protea/api/routers/jobs.py
index 953e5eb..c3d554c 100644
--- a/protea/api/routers/jobs.py
+++ b/protea/api/routers/jobs.py
@@ -17,10 +17,18 @@
 
 
 class CreateJobRequest(BaseModel):
-    operation: str = Field(..., min_length=1, description="Registered operation name, e.g. `insert_proteins`.")
-    queue_name: str = Field(..., min_length=1, description="RabbitMQ queue to publish the job to, e.g. `protea.jobs`.")
-    payload: dict[str, Any] = Field(default_factory=dict, description="Operation-specific configuration object.")
-    meta: dict[str, Any] = Field(default_factory=dict, description="Optional free-form metadata stored alongside the job.")
+    operation: str = Field(
+        ..., min_length=1, description="Registered operation name, e.g. `insert_proteins`."
+    )
+    queue_name: str = Field(
+        ..., min_length=1, description="RabbitMQ queue to publish the job to, e.g. `protea.jobs`."
+    )
+    payload: dict[str, Any] = Field(
+        default_factory=dict, description="Operation-specific configuration object."
+    )
+    meta: dict[str, Any] = Field(
+        default_factory=dict, description="Optional free-form metadata stored alongside the job."
+    )
 
     @field_validator("operation", "queue_name", mode="before")
     @classmethod
@@ -32,6 +40,7 @@ def strip_and_require(cls, v: str) -> str:
 
 # --- Dependency hook (wire this in your app factory) ---
 
+
 def get_session_factory(request: Request) -> sessionmaker[Session]:
     factory = getattr(request.app.state, "session_factory", None)
     if factory is None:
@@ -67,11 +76,13 @@ def create_job(
         session.add(job)
         session.flush()
         job_id = job.id
-        session.add(JobEvent(
-            job_id=job_id,
-            event="job.created",
-            fields={"operation": body.operation, "queue": body.queue_name},
-        ))
+        session.add(
+            JobEvent(
+                job_id=job_id,
+                event="job.created",
+                fields={"operation": body.operation, "queue": body.queue_name},
+            )
+        )
 
     # Publish after commit so the worker always finds the row.
     publish_job(amqp_url, body.queue_name, job_id)
@@ -241,10 +252,12 @@ def cancel_job(
         )
         for child in children:
             child.status = JobStatus.CANCELLED
-            session.add(JobEvent(
-                job_id=child.id,
-                event="job.cancelled",
-                fields={"reason": "parent_cancelled"},
-            ))
+            session.add(
+                JobEvent(
+                    job_id=child.id,
+                    event="job.cancelled",
+                    fields={"reason": "parent_cancelled"},
+                )
+            )
 
         return {"id": str(j.id), "status": j.status.value, "children_cancelled": len(children)}
diff --git a/protea/api/routers/maintenance.py b/protea/api/routers/maintenance.py
index 6fcd1b2..3574c5f 100644
--- a/protea/api/routers/maintenance.py
+++ b/protea/api/routers/maintenance.py
@@ -32,7 +32,8 @@ def preview_orphan_sequences(
     """
     with session_scope(factory) as session:
         total = session.query(Sequence).count()
-        orphan_count = session.execute(text("""
+        orphan_count = session.execute(
+            text("""
             SELECT COUNT(*)
             FROM sequence s
             WHERE NOT EXISTS (
@@ -41,7 +42,8 @@ def preview_orphan_sequences(
             AND NOT EXISTS (
                 SELECT 1 FROM query_set_entry qse WHERE qse.sequence_id = s.id
             )
-        """)).scalar()
+        """)
+        ).scalar()
 
     return {
         "total_sequences": total,
@@ -63,7 +65,8 @@ def vacuum_sequences(
         # Collect IDs first to do a targeted delete (avoids full-table lock)
         orphan_ids = [
             row[0]
-            for row in session.execute(text("""
+            for row in session.execute(
+                text("""
                 SELECT s.id
                 FROM sequence s
                 WHERE NOT EXISTS (
@@ -72,7 +75,8 @@ def vacuum_sequences(
                 AND NOT EXISTS (
                     SELECT 1 FROM query_set_entry qse WHERE qse.sequence_id = s.id
                 )
-            """)).fetchall()
+            """)
+            ).fetchall()
         ]
 
         if not orphan_ids:
@@ -98,13 +102,15 @@ def preview_unindexed_embeddings(
     """
     with session_scope(factory) as session:
         total = session.query(SequenceEmbedding).count()
-        unindexed_count = session.execute(text("""
+        unindexed_count = session.execute(
+            text("""
             SELECT COUNT(*)
             FROM sequence_embedding se
             WHERE NOT EXISTS (
                 SELECT 1 FROM protein p WHERE p.sequence_id = se.sequence_id
             )
-        """)).scalar()
+        """)
+        ).scalar()
 
     return {
         "total_embeddings": total,
@@ -125,13 +131,15 @@ def vacuum_embeddings(
     with session_scope(factory) as session:
         unindexed_ids = [
             row[0]
-            for row in session.execute(text("""
+            for row in session.execute(
+                text("""
                 SELECT se.id
                 FROM sequence_embedding se
                 WHERE NOT EXISTS (
                     SELECT 1 FROM protein p WHERE p.sequence_id = se.sequence_id
                 )
-            """)).fetchall()
+            """)
+            ).fetchall()
         ]
 
         if not unindexed_ids:
diff --git a/protea/api/routers/proteins.py b/protea/api/routers/proteins.py
index a15a33d..ab7060e 100644
--- a/protea/api/routers/proteins.py
+++ b/protea/api/routers/proteins.py
@@ -28,6 +28,7 @@ def get_session_factory(request: Request) -> sessionmaker[Session]:
 
 # ── Stats ─────────────────────────────────────────────────────────────────────
 
+
 @router.get("/stats", summary="Aggregate protein statistics")
 def get_protein_stats(
     factory: sessionmaker[Session] = Depends(get_session_factory),
@@ -36,17 +37,37 @@ def get_protein_stats(
     and how many have metadata, embeddings, or GO annotations."""
     with session_scope(factory) as session:
         total = session.query(func.count(Protein.accession)).scalar() or 0
-        canonical = session.query(func.count(Protein.accession)).filter(Protein.is_canonical.is_(True)).scalar() or 0
-        reviewed = session.query(func.count(Protein.accession)).filter(Protein.reviewed.is_(True)).scalar() or 0
-        with_metadata = session.query(func.count(distinct(Protein.canonical_accession))).join(
-            ProteinUniProtMetadata,
-            Protein.canonical_accession == ProteinUniProtMetadata.canonical_accession,
-        ).scalar() or 0
-        with_embeddings = session.query(func.count(distinct(Protein.accession))).join(
-            SequenceEmbedding,
-            Protein.sequence_id == SequenceEmbedding.sequence_id,
-        ).scalar() or 0
-        with_go = session.query(func.count(distinct(ProteinGOAnnotation.protein_accession))).scalar() or 0
+        canonical = (
+            session.query(func.count(Protein.accession))
+            .filter(Protein.is_canonical.is_(True))
+            .scalar()
+            or 0
+        )
+        reviewed = (
+            session.query(func.count(Protein.accession)).filter(Protein.reviewed.is_(True)).scalar()
+            or 0
+        )
+        with_metadata = (
+            session.query(func.count(distinct(Protein.canonical_accession)))
+            .join(
+                ProteinUniProtMetadata,
+                Protein.canonical_accession == ProteinUniProtMetadata.canonical_accession,
+            )
+            .scalar()
+            or 0
+        )
+        with_embeddings = (
+            session.query(func.count(distinct(Protein.accession)))
+            .join(
+                SequenceEmbedding,
+                Protein.sequence_id == SequenceEmbedding.sequence_id,
+            )
+            .scalar()
+            or 0
+        )
+        with_go = (
+            session.query(func.count(distinct(ProteinGOAnnotation.protein_accession))).scalar() or 0
+        )
 
         return {
             "total": total,
@@ -62,6 +83,7 @@ def get_protein_stats(
 
 # ── List ──────────────────────────────────────────────────────────────────────
 
+
 @router.get("", summary="List proteins")
 def list_proteins(
     search: str | None = Query(default=None),
@@ -111,6 +133,7 @@ def list_proteins(
 
 # ── Detail ────────────────────────────────────────────────────────────────────
 
+
 @router.get("/{accession}", summary="Get protein details")
 def get_protein(
     accession: str,
@@ -123,17 +146,25 @@ def get_protein(
         if p is None:
             raise HTTPException(status_code=404, detail="Protein not found")
 
-        meta = session.query(ProteinUniProtMetadata).filter(
-            ProteinUniProtMetadata.canonical_accession == p.canonical_accession
-        ).first()
+        meta = (
+            session.query(ProteinUniProtMetadata)
+            .filter(ProteinUniProtMetadata.canonical_accession == p.canonical_accession)
+            .first()
+        )
 
-        embedding_count = session.query(func.count(SequenceEmbedding.id)).filter(
-            SequenceEmbedding.sequence_id == p.sequence_id
-        ).scalar() if p.sequence_id else 0
+        embedding_count = (
+            session.query(func.count(SequenceEmbedding.id))
+            .filter(SequenceEmbedding.sequence_id == p.sequence_id)
+            .scalar()
+            if p.sequence_id
+            else 0
+        )
 
-        go_count = session.query(func.count(ProteinGOAnnotation.id)).filter(
-            ProteinGOAnnotation.protein_accession == accession
-        ).scalar()
+        go_count = (
+            session.query(func.count(ProteinGOAnnotation.id))
+            .filter(ProteinGOAnnotation.protein_accession == accession)
+            .scalar()
+        )
 
         isoforms = []
         if p.is_canonical:
@@ -182,12 +213,15 @@ def get_protein(
                 "rhea_id": meta.rhea_id,
                 "site": meta.site,
                 "features": meta.features,
-            } if meta else None,
+            }
+            if meta
+            else None,
         }
 
 
 # ── Annotations ───────────────────────────────────────────────────────────────
 
+
 @router.get("/{accession}/annotations", summary="List GO annotations for a protein")
 def get_protein_annotations(
     accession: str,
diff --git a/protea/api/routers/query_sets.py b/protea/api/routers/query_sets.py
index 74af7ad..dafe7e0 100644
--- a/protea/api/routers/query_sets.py
+++ b/protea/api/routers/query_sets.py
@@ -65,6 +65,7 @@ def _query_set_to_dict(qs: QuerySet, entry_count: int) -> dict[str, Any]:
 
 # ── Endpoints ─────────────────────────────────────────────────────────────────
 
+
 @router.post("", status_code=201)
 async def create_query_set(
     file: UploadFile,
@@ -147,17 +148,15 @@ def list_query_sets(
 ) -> list[dict[str, Any]]:
     """List all uploaded FASTA query sets with their entry counts, newest first."""
     with session_scope(factory) as session:
-        rows = (
-            session.query(QuerySet)
-            .order_by(QuerySet.created_at.desc())
-            .all()
-        )
+        rows = session.query(QuerySet).order_by(QuerySet.created_at.desc()).all()
         counts = {
             qs_id: cnt
             for qs_id, cnt in session.query(
                 QuerySetEntry.query_set_id,
                 func.count(QuerySetEntry.id),
-            ).group_by(QuerySetEntry.query_set_id).all()
+            )
+            .group_by(QuerySetEntry.query_set_id)
+            .all()
         }
         return [_query_set_to_dict(qs, counts.get(qs.id, 0)) for qs in rows]
 
@@ -186,10 +185,7 @@ def get_query_set(
         )
 
         result = _query_set_to_dict(qs, entry_count)
-        result["entries"] = [
-            {"accession": acc, "sequence_id": seq_id}
-            for acc, seq_id in entries
-        ]
+        result["entries"] = [{"accession": acc, "sequence_id": seq_id} for acc, seq_id in entries]
         return result
 
 
diff --git a/protea/api/routers/scoring.py b/protea/api/routers/scoring.py
new file mode 100644
index 0000000..acce2df
--- /dev/null
+++ b/protea/api/routers/scoring.py
@@ -0,0 +1,556 @@
+"""Scoring configuration management and analytical endpoints.
+
+Provides full CRUD for :class:`ScoringConfig` and two read-only analytical
+endpoints that apply a stored config to an existing ``PredictionSet``:
+
+``GET /scoring/prediction-sets/{id}/score.tsv``
+    Stream a TSV of scored predictions.  The score column is computed on-the-fly
+    by applying the selected ``ScoringConfig`` formula to the raw signals stored
+    in ``GOPrediction`` rows — no re-running of the KNN pipeline is required.
+
+``GET /scoring/prediction-sets/{id}/metrics``
+    Compute CAFA Fmax / AUC-PR for a (PredictionSet, ScoringConfig, category)
+    triple.  Requires two ``AnnotationSet`` IDs to build the NK/LK ground truth
+    following the CAFA4 protocol.
+
+Evidence weights
+----------------
+Each ``ScoringConfig`` may carry an optional ``evidence_weights`` dict that
+overrides the system-default per-GO-evidence-code quality multipliers.  The
+API validates that:
+
+- Every key in the dict is a known GO evidence code (one of the codes in
+  :data:`DEFAULT_EVIDENCE_WEIGHTS`).
+- Every value is a float in [0, 1].
+
+Partial overrides are allowed: codes absent from the submitted dict will
+continue to use the system default at score-computation time.
+"""
+
+from __future__ import annotations
+
+import uuid
+from collections.abc import Iterator
+from typing import Any
+
+from fastapi import APIRouter, Depends, HTTPException, Query
+from fastapi.requests import Request
+from fastapi.responses import StreamingResponse
+from pydantic import BaseModel, Field, field_validator
+
+from protea.core.evaluation import compute_evaluation_data
+from protea.core.metrics import compute_cafa_metrics
+from protea.core.scoring import compute_score
+from protea.infrastructure.orm.models.annotation.go_term import GOTerm
+from protea.infrastructure.orm.models.embedding.go_prediction import GOPrediction
+from protea.infrastructure.orm.models.embedding.prediction_set import PredictionSet
+from protea.infrastructure.orm.models.embedding.scoring_config import (
+    DEFAULT_EVIDENCE_WEIGHTS,
+    DEFAULT_WEIGHTS,
+    VALID_FORMULAS,
+    ScoringConfig,
+)
+from protea.infrastructure.session import session_scope
+
+router = APIRouter(prefix="/scoring", tags=["scoring"])
+
+# ---------------------------------------------------------------------------
+# Built-in preset configurations
+# ---------------------------------------------------------------------------
+# These cover the most common use-cases and are designed to be instructive
+# as reference points for custom configs.  None of them override evidence
+# weights so they document what the system defaults produce.
+
+_PRESET_CONFIGS: list[dict[str, Any]] = [
+    {
+        "name": "embedding_only",
+        "formula": "linear",
+        "weights": {
+            "embedding_similarity": 1.0,
+            "identity_nw": 0.0,
+            "identity_sw": 0.0,
+            "evidence_weight": 0.0,
+            "taxonomic_proximity": 0.0,
+        },
+        "description": (
+            "Pure cosine similarity converted to [0, 1]. "
+            "Baseline config — no alignment, evidence, or taxonomy signals."
+        ),
+    },
+    {
+        "name": "embedding_plus_evidence",
+        "formula": "evidence_weighted",
+        "weights": {
+            "embedding_similarity": 1.0,
+            "identity_nw": 0.0,
+            "identity_sw": 0.0,
+            "evidence_weight": 1.0,
+            "taxonomic_proximity": 0.0,
+        },
+        "description": (
+            "Embedding similarity multiplied by evidence code quality (evidence_weighted formula). "
+            "Penalises IEA-sourced annotations regardless of embedding distance."
+        ),
+    },
+    {
+        "name": "alignment_weighted",
+        "formula": "linear",
+        "weights": {
+            "embedding_similarity": 0.5,
+            "identity_nw": 0.3,
+            "identity_sw": 0.2,
+            "evidence_weight": 0.0,
+            "taxonomic_proximity": 0.0,
+        },
+        "description": (
+            "Combines embedding similarity (50 %) with global NW identity (30 %) "
+            "and local SW identity (20 %). "
+            "Requires PredictionSet computed with compute_alignments=True."
+        ),
+    },
+    {
+        "name": "composite",
+        "formula": "evidence_weighted",
+        "weights": {
+            "embedding_similarity": 0.4,
+            "identity_nw": 0.2,
+            "identity_sw": 0.1,
+            "evidence_weight": 0.2,
+            "taxonomic_proximity": 0.1,
+        },
+        "description": (
+            "Full composite: embedding + alignment + evidence quality + taxonomic proximity. "
+            "Requires compute_alignments=True and compute_taxonomy=True."
+        ),
+    },
+    {
+        "name": "evidence_primary",
+        "formula": "linear",
+        "weights": {
+            "embedding_similarity": 0.2,
+            "identity_nw": 0.0,
+            "identity_sw": 0.0,
+            "evidence_weight": 0.8,
+            "taxonomic_proximity": 0.0,
+        },
+        "description": (
+            "Evidence quality as primary signal (80%), embedding similarity as tiebreaker (20%). "
+            "Designed for datasets where cosine distances cluster tightly (>99% of predictions "
+            "within distance < 0.1), making distance a poor tau discriminator. "
+            "Creates three well-separated score tiers: "
+            "EXP/IDA → ~1.0, ISS/IBA → ~0.76, IEA → ~0.46. "
+            "Recommended when compute_alignments and compute_taxonomy are not available."
+        ),
+    },
+]
+
+
+# ---------------------------------------------------------------------------
+# FastAPI dependency
+# ---------------------------------------------------------------------------
+
+
+def get_session_factory(request: Request):
+    return request.app.state.session_factory
+
+
+# ---------------------------------------------------------------------------
+# Request / response models
+# ---------------------------------------------------------------------------
+
+
+class ScoringConfigCreate(BaseModel):
+    """Request body for POST /scoring/configs.
+
+    Attributes
+    ----------
+    name:
+        Unique display name (1–255 characters).
+    formula:
+        Aggregation formula.  One of ``"linear"`` or ``"evidence_weighted"``.
+    weights:
+        Signal weights dict.  Valid keys: ``embedding_similarity``,
+        ``identity_nw``, ``identity_sw``, ``evidence_weight``,
+        ``taxonomic_proximity``.  Missing keys default to 0.
+    evidence_weights:
+        Optional per-GO-evidence-code quality overrides.  Keys must be valid
+        GO evidence codes (e.g. ``"IEA"``); values must be in [0, 1].
+        When ``None`` the system defaults from
+        :data:`DEFAULT_EVIDENCE_WEIGHTS` are used at score-computation time.
+        Partial dicts are allowed.
+    description:
+        Free-text description stored for display in the UI.
+    """
+
+    name: str = Field(..., min_length=1, max_length=255)
+    formula: str = Field("linear")
+    weights: dict[str, float] = Field(default_factory=lambda: dict(DEFAULT_WEIGHTS))
+    evidence_weights: dict[str, float] | None = Field(
+        default=None,
+        description=(
+            "Per-GO-evidence-code quality overrides in [0, 1]. "
+            "NULL means use system defaults. Partial dicts are valid."
+        ),
+    )
+    description: str | None = None
+
+    model_config = {"extra": "forbid"}
+
+    @field_validator("evidence_weights")
+    @classmethod
+    def validate_evidence_weights(cls, v: dict[str, float] | None) -> dict[str, float] | None:
+        """Ensure all keys are known GO codes and all values are in [0, 1]."""
+        if v is None:
+            return None
+        known_codes = set(DEFAULT_EVIDENCE_WEIGHTS.keys())
+        unknown = set(v.keys()) - known_codes
+        if unknown:
+            raise ValueError(
+                f"Unknown evidence codes: {sorted(unknown)}. Valid codes: {sorted(known_codes)}"
+            )
+        out_of_range = {k: val for k, val in v.items() if not (0.0 <= val <= 1.0)}
+        if out_of_range:
+            raise ValueError(f"Evidence weights must be in [0, 1]. Out-of-range: {out_of_range}")
+        return v
+
+
+class ScoringConfigResponse(BaseModel):
+    """Serialised representation of a stored ScoringConfig."""
+
+    id: uuid.UUID
+    name: str
+    formula: str
+    weights: dict[str, Any]
+    evidence_weights: dict[str, Any] | None
+    description: str | None
+    created_at: Any
+
+
+def _to_response(c: ScoringConfig) -> ScoringConfigResponse:
+    """Convert an ORM ScoringConfig to its API response model."""
+    return ScoringConfigResponse(
+        id=c.id,
+        name=c.name,
+        formula=c.formula,
+        weights=c.weights,
+        evidence_weights=c.evidence_weights,
+        description=c.description,
+        created_at=c.created_at,
+    )
+
+
+def _snapshot(c: ScoringConfig) -> ScoringConfig:
+    """Create a detached ScoringConfig copy safe to use after a session closes.
+
+    The scoring endpoints close the DB session before streaming the response
+    body.  This helper captures all scoring-relevant fields into a plain ORM
+    instance that does not require an open session.
+    """
+    return ScoringConfig(
+        id=c.id,
+        name=c.name,
+        formula=c.formula,
+        weights=c.weights,
+        evidence_weights=c.evidence_weights,
+        description=c.description,
+    )
+
+
+# ---------------------------------------------------------------------------
+# ScoringConfig CRUD
+# ---------------------------------------------------------------------------
+
+
+@router.get("/configs", response_model=list[ScoringConfigResponse])
+def list_scoring_configs(factory=Depends(get_session_factory)):
+    """Return all stored ScoringConfigs ordered by creation time."""
+    with session_scope(factory) as session:
+        configs = session.query(ScoringConfig).order_by(ScoringConfig.created_at).all()
+        return [_to_response(c) for c in configs]
+
+
+@router.post("/configs", response_model=ScoringConfigResponse, status_code=201)
+def create_scoring_config(
+    body: ScoringConfigCreate,
+    factory=Depends(get_session_factory),
+):
+    """Create a new ScoringConfig.
+
+    Validates that ``formula`` is one of the supported values and that every
+    key in ``weights`` is a recognised signal name.  Evidence weight validation
+    is handled by the Pydantic model.
+    """
+    if body.formula not in VALID_FORMULAS:
+        raise HTTPException(
+            status_code=422,
+            detail=f"Invalid formula {body.formula!r}. Valid options: {list(VALID_FORMULAS)}",
+        )
+    known_signals = set(DEFAULT_WEIGHTS.keys())
+    unknown_signals = set(body.weights.keys()) - known_signals
+    if unknown_signals:
+        raise HTTPException(
+            status_code=422,
+            detail=(
+                f"Unknown signal weight keys: {sorted(unknown_signals)}. "
+                f"Valid keys: {sorted(known_signals)}"
+            ),
+        )
+
+    with session_scope(factory) as session:
+        config = ScoringConfig(
+            name=body.name,
+            formula=body.formula,
+            weights=body.weights,
+            evidence_weights=body.evidence_weights,
+            description=body.description,
+        )
+        session.add(config)
+        session.flush()
+        return _to_response(config)
+
+
+@router.post("/configs/presets", status_code=201)
+def create_preset_configs(factory=Depends(get_session_factory)):
+    """Seed the database with the four built-in preset ScoringConfigs.
+
+    Idempotent — presets that already exist (matched by name) are silently
+    skipped.  Returns the list of names that were actually created.
+    """
+    created: list[str] = []
+    with session_scope(factory) as session:
+        existing_names = {row[0] for row in session.query(ScoringConfig.name).all()}
+        for preset in _PRESET_CONFIGS:
+            if preset["name"] in existing_names:
+                continue
+            session.add(ScoringConfig(**preset))
+            created.append(preset["name"])
+    return {"created": created}
+
+
+@router.get("/configs/{config_id}", response_model=ScoringConfigResponse)
+def get_scoring_config(
+    config_id: uuid.UUID,
+    factory=Depends(get_session_factory),
+):
+    """Retrieve a single ScoringConfig by UUID."""
+    with session_scope(factory) as session:
+        config = session.get(ScoringConfig, config_id)
+        if config is None:
+            raise HTTPException(status_code=404, detail="ScoringConfig not found")
+        return _to_response(config)
+
+
+@router.delete("/configs/{config_id}", status_code=204)
+def delete_scoring_config(
+    config_id: uuid.UUID,
+    factory=Depends(get_session_factory),
+):
+    """Delete a ScoringConfig by UUID."""
+    with session_scope(factory) as session:
+        config = session.get(ScoringConfig, config_id)
+        if config is None:
+            raise HTTPException(status_code=404, detail="ScoringConfig not found")
+        session.delete(config)
+
+
+# ---------------------------------------------------------------------------
+# Scored TSV endpoint
+# ---------------------------------------------------------------------------
+
+
+@router.get("/prediction-sets/{set_id}/score.tsv")
+def download_scored_predictions(
+    set_id: uuid.UUID,
+    scoring_config_id: uuid.UUID = Query(...),
+    min_score: float | None = Query(None, ge=0.0, le=1.0),
+    accession: str | None = Query(None),
+    factory=Depends(get_session_factory),
+):
+    """Stream a TSV of predictions with computed confidence scores.
+
+    The score is computed on-the-fly for every row using the selected
+    ``ScoringConfig``, including any custom evidence-weight overrides stored
+    in that config.  The session is closed before the generator starts so
+    the response is streamed without holding a DB connection open.
+
+    Query parameters
+    ----------------
+    scoring_config_id:
+        UUID of the ``ScoringConfig`` to apply.
+    min_score:
+        Optional score threshold — rows below this value are omitted.
+    accession:
+        Optional protein accession filter.
+
+    TSV columns
+    -----------
+    protein_accession, go_id, score, distance, ref_protein_accession,
+    evidence_code, qualifier, identity_nw, identity_sw, taxonomic_distance.
+    """
+    with session_scope(factory) as session:
+        if session.get(PredictionSet, set_id) is None:
+            raise HTTPException(status_code=404, detail="PredictionSet not found")
+        config = session.get(ScoringConfig, scoring_config_id)
+        if config is None:
+            raise HTTPException(status_code=404, detail="ScoringConfig not found")
+        config_snap = _snapshot(config)
+
+    def _generate() -> Iterator[bytes]:
+        header = (
+            "\t".join(
+                [
+                    "protein_accession",
+                    "go_id",
+                    "score",
+                    "distance",
+                    "ref_protein_accession",
+                    "evidence_code",
+                    "qualifier",
+                    "identity_nw",
+                    "identity_sw",
+                    "taxonomic_distance",
+                ]
+            )
+            + "\n"
+        )
+        yield header.encode()
+
+        with session_scope(factory) as session:
+            q = (
+                session.query(GOPrediction, GOTerm.go_id)
+                .join(GOTerm, GOPrediction.go_term_id == GOTerm.id)
+                .filter(GOPrediction.prediction_set_id == set_id)
+            )
+            if accession:
+                q = q.filter(GOPrediction.protein_accession == accession)
+
+            for pred, go_id in q.yield_per(1000):
+                pred_dict = {
+                    "distance": pred.distance,
+                    "identity_nw": pred.identity_nw,
+                    "identity_sw": pred.identity_sw,
+                    "evidence_code": pred.evidence_code,
+                    "taxonomic_distance": pred.taxonomic_distance,
+                }
+                score = compute_score(pred_dict, config_snap)
+                if min_score is not None and score < min_score:
+                    continue
+
+                row = (
+                    "\t".join(
+                        [
+                            pred.protein_accession,
+                            go_id,
+                            str(score),
+                            str(pred.distance) if pred.distance is not None else "",
+                            pred.ref_protein_accession or "",
+                            pred.evidence_code or "",
+                            pred.qualifier or "",
+                            str(pred.identity_nw) if pred.identity_nw is not None else "",
+                            str(pred.identity_sw) if pred.identity_sw is not None else "",
+                            str(pred.taxonomic_distance)
+                            if pred.taxonomic_distance is not None
+                            else "",
+                        ]
+                    )
+                    + "\n"
+                )
+                yield row.encode()
+
+    filename = f"scored_{set_id}_{scoring_config_id}.tsv"
+    return StreamingResponse(
+        _generate(),
+        media_type="text/tab-separated-values",
+        headers={"Content-Disposition": f'attachment; filename="{filename}"'},
+    )
+
+
+# ---------------------------------------------------------------------------
+# CAFA metrics endpoint
+# ---------------------------------------------------------------------------
+
+
+@router.get("/prediction-sets/{set_id}/metrics")
+def compute_metrics(
+    set_id: uuid.UUID,
+    scoring_config_id: uuid.UUID = Query(...),
+    old_annotation_set_id: uuid.UUID = Query(...),
+    new_annotation_set_id: uuid.UUID = Query(...),
+    ontology_snapshot_id: uuid.UUID = Query(...),
+    category: str = Query("nk", pattern="^(nk|lk)$"),
+    factory=Depends(get_session_factory),
+):
+    """Compute CAFA Fmax and AUC-PR for a PredictionSet under a ScoringConfig.
+
+    Ground truth is the NK or LK delta between *old_annotation_set* and
+    *new_annotation_set*, following the CAFA4 protocol: only experimental
+    evidence codes, NOT-qualifier annotations excluded with full DAG propagation.
+
+    The selected ``ScoringConfig`` — including any custom ``evidence_weights``
+    — is applied to every ``GOPrediction`` row before computing the
+    precision-recall curve.
+
+    Parameters
+    ----------
+    scoring_config_id:
+        Which stored ScoringConfig formula (and evidence weights) to apply.
+    old_annotation_set_id / new_annotation_set_id:
+        The two AnnotationSets used to compute the temporal ground-truth delta.
+    ontology_snapshot_id:
+        GO DAG snapshot used for NOT-qualifier propagation.
+    category:
+        ``"nk"`` (no-knowledge) or ``"lk"`` (limited-knowledge) protein set.
+    """
+    with session_scope(factory) as session:
+        if session.get(PredictionSet, set_id) is None:
+            raise HTTPException(status_code=404, detail="PredictionSet not found")
+        config = session.get(ScoringConfig, scoring_config_id)
+        if config is None:
+            raise HTTPException(status_code=404, detail="ScoringConfig not found")
+        config_snap = _snapshot(config)
+
+        eval_data = compute_evaluation_data(
+            session,
+            old_annotation_set_id=old_annotation_set_id,
+            new_annotation_set_id=new_annotation_set_id,
+            ontology_snapshot_id=ontology_snapshot_id,
+        )
+
+        rows = (
+            session.query(GOPrediction, GOTerm.go_id)
+            .join(GOTerm, GOPrediction.go_term_id == GOTerm.id)
+            .filter(GOPrediction.prediction_set_id == set_id)
+            .all()
+        )
+
+    scored: list[dict[str, Any]] = []
+    for pred, go_id in rows:
+        pred_dict: dict[str, Any] = {
+            "protein_accession": pred.protein_accession,
+            "go_id": go_id,
+            "distance": pred.distance,
+            "identity_nw": pred.identity_nw,
+            "identity_sw": pred.identity_sw,
+            "evidence_code": pred.evidence_code,
+            "taxonomic_distance": pred.taxonomic_distance,
+        }
+        pred_dict["score"] = compute_score(pred_dict, config_snap)
+        scored.append(pred_dict)
+
+    metrics = compute_cafa_metrics(scored, eval_data, category=category)
+
+    return {
+        "prediction_set_id": str(set_id),
+        "scoring_config_id": str(scoring_config_id),
+        "scoring_config_name": config_snap.name,
+        **metrics.summary(),
+        "curve": [
+            {
+                "threshold": p.threshold,
+                "precision": p.precision,
+                "recall": p.recall,
+                "f1": p.f1,
+            }
+            for p in metrics.curve
+        ],
+    }
diff --git a/protea/api/routers/support.py b/protea/api/routers/support.py
new file mode 100644
index 0000000..79f3dbe
--- /dev/null
+++ b/protea/api/routers/support.py
@@ -0,0 +1,70 @@
+from __future__ import annotations
+
+from typing import Any
+
+from fastapi import APIRouter, Depends, Query
+from pydantic import BaseModel, Field
+from starlette.requests import Request
+
+from protea.infrastructure.orm.models.support_entry import SupportEntry
+from protea.infrastructure.session import session_scope
+
+router = APIRouter(prefix="/support", tags=["support"])
+
+_MAX_COMMENT_LENGTH = 500
+_RECENT_LIMIT = 20
+_PAGE_LIMIT = 100
+
+
+def get_session_factory(request: Request):
+    return request.app.state.session_factory
+
+
+class SupportCreate(BaseModel):
+    comment: str | None = Field(default=None, max_length=_MAX_COMMENT_LENGTH)
+
+
+@router.get("")
+def get_support(
+    all_comments: bool = Query(False),
+    factory=Depends(get_session_factory),
+) -> dict[str, Any]:
+    """Return total thumbs-up count and comments.
+
+    Pass ``all_comments=true`` to get all comments (up to 100) instead of the 20 most recent.
+    """
+    with session_scope(factory) as session:
+        total = session.query(SupportEntry).count()
+        limit = _PAGE_LIMIT if all_comments else _RECENT_LIMIT
+        recent = (
+            session.query(SupportEntry)
+            .filter(SupportEntry.comment.isnot(None))
+            .order_by(SupportEntry.created_at.desc())
+            .limit(limit)
+            .all()
+        )
+        return {
+            "count": total,
+            "comments": [
+                {"id": str(e.id), "comment": e.comment, "created_at": e.created_at.isoformat()}
+                for e in recent
+            ],
+        }
+
+
+@router.post("", status_code=201)
+def post_support(
+    body: SupportCreate,
+    factory=Depends(get_session_factory),
+) -> dict[str, Any]:
+    """Submit a thumbs-up with an optional comment."""
+    comment = body.comment.strip() if body.comment else None
+    if comment == "":
+        comment = None
+
+    with session_scope(factory) as session:
+        entry = SupportEntry(comment=comment)
+        session.add(entry)
+        session.flush()
+        total = session.query(SupportEntry).count()
+        return {"count": total, "id": str(entry.id)}
diff --git a/protea/config/system.yaml b/protea/config/system.yaml
index ef8c9a1..5cd11ca 100644
--- a/protea/config/system.yaml
+++ b/protea/config/system.yaml
@@ -1,5 +1,5 @@
 database:
-  url: postgresql+psycopg://usuario:clave@localhost:5432/BioData
+  url: postgresql+psycopg://protea:protea@localhost:5432/protea
 
 queue:
   amqp_url: amqp://guest:guest@localhost:5672/
diff --git a/protea/core/contracts/operation.py b/protea/core/contracts/operation.py
index 3866a35..d97df11 100644
--- a/protea/core/contracts/operation.py
+++ b/protea/core/contracts/operation.py
@@ -72,5 +72,6 @@ class Operation(Protocol):
 
     name: str
 
-    def execute(self, session: Session, payload: dict[str, Any], *, emit: EmitFn) -> OperationResult:
-        ...
+    def execute(
+        self, session: Session, payload: dict[str, Any], *, emit: EmitFn
+    ) -> OperationResult: ...
diff --git a/protea/core/evaluation.py b/protea/core/evaluation.py
index b19ae75..16df3ae 100644
--- a/protea/core/evaluation.py
+++ b/protea/core/evaluation.py
@@ -1,16 +1,26 @@
 """CAFA-style evaluation data computation.
 
 This module computes the ground-truth delta between two AnnotationSets
-(old → new) following the CAFA evaluation protocol:
+(old → new) following the official CAFA5 evaluation protocol:
 
   1. Experimental evidence codes only (EXP, IDA, IMP, …)
   2. NOT-qualifier annotations are excluded — including their GO descendants
      propagated transitively through the is_a / part_of DAG.
-  3. Delta proteins = proteins that gained ≥ 1 new (protein, go_term) pair.
-  4. NK  = delta proteins with ZERO experimental annotations in OLD.
-  5. LK  = delta proteins with ≥ 1 experimental annotation in OLD.
-  6. PK  = same annotation set as LK; use with known-terms for evaluation.
-  7. known-terms = ALL experimental annotations from OLD (not delta-filtered).
+  3. Classification is per (protein, namespace), not globally per protein:
+
+     NK  — protein had NO experimental annotations in ANY namespace at t0.
+            All novel terms across all namespaces are ground truth.
+
+     LK  — protein had annotations in SOME namespaces at t0, but NOT in
+            namespace S.  Novel terms in S are ground truth for LK.
+
+     PK  — protein had annotations in namespace S at t0 AND gained new terms
+            in S at t1.  Novel terms in S are ground truth for PK; old terms
+            in S are the ``-known`` file for the CAFA evaluator.
+
+  Note: the same protein can be LK in one namespace and PK in another
+  simultaneously (e.g. had MFO+BPO at t0, gains CCO → LK in CCO, gains new
+  BPO → PK in BPO).
 
 Output format (matching CAFA evaluator): 2-column TSV, no header.
   protein_accession \\t go_id
@@ -34,11 +44,14 @@
     EXPERIMENTAL | {eco for eco, go in ECO_TO_CODE.items() if go in EXPERIMENTAL}
 )
 
+_NAMESPACES = ("F", "P", "C")
+
 
 # ---------------------------------------------------------------------------
 # Data structures
 # ---------------------------------------------------------------------------
 
+
 @dataclass
 class EvaluationData:
     """Computed ground-truth delta between two annotation sets."""
@@ -46,8 +59,11 @@ class EvaluationData:
     # {protein_accession: {go_id}} — delta annotations per category
     nk: dict[str, set[str]] = field(default_factory=dict)
     lk: dict[str, set[str]] = field(default_factory=dict)
-    # known-terms: ALL experimental annotations from OLD (all proteins)
+    pk: dict[str, set[str]] = field(default_factory=dict)
+    # known-terms: ALL experimental annotations from OLD (for reference download)
     known: dict[str, set[str]] = field(default_factory=dict)
+    # pk_known: old terms in PK namespaces only — passed as -known to cafaeval
+    pk_known: dict[str, set[str]] = field(default_factory=dict)
 
     @property
     def nk_proteins(self) -> int:
@@ -57,6 +73,10 @@ def nk_proteins(self) -> int:
     def lk_proteins(self) -> int:
         return len(self.lk)
 
+    @property
+    def pk_proteins(self) -> int:
+        return len(self.pk)
+
     @property
     def nk_annotations(self) -> int:
         return sum(len(v) for v in self.nk.values())
@@ -65,21 +85,27 @@ def nk_annotations(self) -> int:
     def lk_annotations(self) -> int:
         return sum(len(v) for v in self.lk.values())
 
+    @property
+    def pk_annotations(self) -> int:
+        return sum(len(v) for v in self.pk.values())
+
     @property
     def known_terms_count(self) -> int:
         return sum(len(v) for v in self.known.values())
 
     @property
     def delta_proteins(self) -> int:
-        return self.nk_proteins + self.lk_proteins
+        return len(set(self.nk) | set(self.lk) | set(self.pk))
 
     def stats(self) -> dict:
         return {
             "delta_proteins": self.delta_proteins,
             "nk_proteins": self.nk_proteins,
             "lk_proteins": self.lk_proteins,
+            "pk_proteins": self.pk_proteins,
             "nk_annotations": self.nk_annotations,
             "lk_annotations": self.lk_annotations,
+            "pk_annotations": self.pk_annotations,
             "known_terms_count": self.known_terms_count,
         }
 
@@ -88,18 +114,22 @@ def stats(self) -> dict:
 # Internal helpers
 # ---------------------------------------------------------------------------
 
+
 def _load_children_map(session: Session, snapshot_id: uuid.UUID) -> dict[int, set[int]]:
     """Load GO DAG as {parent_go_term_id: {child_go_term_id}} for a snapshot.
 
     Only is_a and part_of relationships are used for NOT-propagation, matching
     the CAFA evaluation protocol.
     """
-    rows = session.execute(text("""
+    rows = session.execute(
+        text("""
         SELECT parent_go_term_id, child_go_term_id
         FROM go_term_relationship
         WHERE ontology_snapshot_id = :snap_id
           AND relation_type IN ('is_a', 'part_of')
-    """), {"snap_id": snapshot_id}).fetchall()
+    """),
+        {"snap_id": snapshot_id},
+    ).fetchall()
 
     children: dict[int, set[int]] = defaultdict(set)
     for parent_id, child_id in rows:
@@ -120,33 +150,23 @@ def _get_descendants(term_id: int, children_map: dict[int, set[int]]) -> set[int
     return visited
 
 
-def _load_experimental_annotations(
-    session: Session,
-    annotation_set_id: uuid.UUID,
-    negative_keys: set[tuple[str, int]],
-    go_id_map: dict[int, str],
-) -> dict[str, set[str]]:
-    """Load all experimental, non-negated annotations from an annotation set.
+def _load_go_maps(
+    session: Session, snapshot_id: uuid.UUID
+) -> tuple[dict[int, str], dict[int, str]]:
+    """Load {go_term.id: go_id} and {go_term.id: aspect} for the snapshot.
 
-    Returns {protein_accession: {go_id}}.
-    negative_keys contains (protein_accession, go_term_db_id) pairs to exclude.
+    aspect is 'F' (molecular function), 'P' (biological process), or
+    'C' (cellular component).
     """
-    rows = session.execute(text("""
-        SELECT pga.protein_accession, pga.go_term_id
-        FROM protein_go_annotation pga
-        WHERE pga.annotation_set_id = :set_id
-          AND pga.evidence_code = ANY(:exp_codes)
-          AND (pga.qualifier IS NULL OR pga.qualifier NOT LIKE '%NOT%')
-    """), {"set_id": annotation_set_id, "exp_codes": _EXP_CODES}).fetchall()
-
-    result: dict[str, set[str]] = defaultdict(set)
-    for protein_accession, go_term_id in rows:
-        if (protein_accession, go_term_id) in negative_keys:
-            continue
-        go_id = go_id_map.get(go_term_id)
-        if go_id:
-            result[protein_accession].add(go_id)
-    return dict(result)
+    rows = session.execute(
+        text("""
+        SELECT id, go_id, aspect FROM go_term WHERE ontology_snapshot_id = :snap_id
+    """),
+        {"snap_id": snapshot_id},
+    ).fetchall()
+    id_map = {db_id: go_id for db_id, go_id, _ in rows}
+    aspect_map = {db_id: aspect for db_id, _, aspect in rows if aspect}
+    return id_map, aspect_map
 
 
 def _build_negative_keys(
@@ -159,14 +179,16 @@ def _build_negative_keys(
     Collects NOT-qualified annotations from all given annotation sets and
     propagates them to all GO descendants via the DAG.
     """
-    not_rows = session.execute(text("""
+    not_rows = session.execute(
+        text("""
         SELECT DISTINCT protein_accession, go_term_id
         FROM protein_go_annotation
         WHERE annotation_set_id = ANY(:set_ids)
           AND qualifier LIKE '%NOT%'
-    """), {"set_ids": set_ids}).fetchall()
+    """),
+        {"set_ids": set_ids},
+    ).fetchall()
 
-    # Group negated terms by protein, expand to descendants
     negated_by_protein: dict[str, set[int]] = defaultdict(set)
     for protein_accession, go_term_id in not_rows:
         negated_by_protein[protein_accession].add(go_term_id)
@@ -182,38 +204,65 @@ def _build_negative_keys(
     return negative_keys
 
 
-def _load_go_id_map(session: Session, snapshot_id: uuid.UUID) -> dict[int, str]:
-    """Load {go_term.id: go_term.go_id} for the snapshot."""
-    rows = session.execute(text("""
-        SELECT id, go_id FROM go_term WHERE ontology_snapshot_id = :snap_id
-    """), {"snap_id": snapshot_id}).fetchall()
-    return {db_id: go_id for db_id, go_id in rows}
+def _load_experimental_annotations_by_ns(
+    session: Session,
+    annotation_set_id: uuid.UUID,
+    negative_keys: set[tuple[str, int]],
+    go_id_map: dict[int, str],
+    aspect_map: dict[int, str],
+) -> dict[str, dict[str, set[str]]]:
+    """Load experimental, non-negated annotations grouped by namespace.
+
+    Returns {protein_accession: {aspect: {go_id}}} where aspect ∈ {'F', 'P', 'C'}.
+    Terms without a known aspect are silently dropped.
+    """
+    rows = session.execute(
+        text("""
+        SELECT pga.protein_accession, pga.go_term_id
+        FROM protein_go_annotation pga
+        WHERE pga.annotation_set_id = :set_id
+          AND pga.evidence_code = ANY(:exp_codes)
+          AND (pga.qualifier IS NULL OR pga.qualifier NOT LIKE '%NOT%')
+    """),
+        {"set_id": annotation_set_id, "exp_codes": _EXP_CODES},
+    ).fetchall()
+
+    result: dict[str, dict[str, set[str]]] = defaultdict(lambda: defaultdict(set))
+    for protein_accession, go_term_id in rows:
+        if (protein_accession, go_term_id) in negative_keys:
+            continue
+        go_id = go_id_map.get(go_term_id)
+        aspect = aspect_map.get(go_term_id)
+        if go_id and aspect:
+            result[protein_accession][aspect].add(go_id)
+    return {p: dict(ns_terms) for p, ns_terms in result.items()}
 
 
 # ---------------------------------------------------------------------------
 # Public API
 # ---------------------------------------------------------------------------
 
+
 def compute_evaluation_data(
     session: Session,
     old_annotation_set_id: uuid.UUID,
     new_annotation_set_id: uuid.UUID,
     ontology_snapshot_id: uuid.UUID,
 ) -> EvaluationData:
-    """Compute NK/LK ground truth and known-terms from two annotation sets.
-
-    This is the main entry point used both by the generation operation
-    (to persist stats) and by the download endpoints (to stream TSV data).
-
-    Steps:
-      1. Load GO DAG children map for NOT propagation.
-      2. Build negative_keys from NOT annotations in both sets.
-      3. Load experimental annotations from OLD and NEW (excluding negatives).
-      4. Compute delta = new - old per protein.
-      5. Classify delta proteins into NK / LK.
-      6. Collect known-terms from OLD (all proteins, no delta filter).
+    """Compute NK/LK/PK ground truth following the CAFA5 protocol.
+
+    Classification is per (protein, namespace):
+
+      NK  — protein had no experimental annotations in any namespace at t0.
+      LK  — protein had annotations in some namespaces at t0, but not in
+             namespace S; gained new terms in S → those terms are LK ground truth.
+      PK  — protein had annotations in namespace S at t0 and gained new terms
+             in S → those novel terms are PK ground truth; old terms in S are
+             stored in ``pk_known`` for the cafaeval ``-known`` flag.
+
+    The same protein can be simultaneously LK in one namespace and PK in another.
     """
-    go_id_map = _load_go_id_map(session, ontology_snapshot_id)
+    go_id_map, aspect_map = _load_go_maps(session, ontology_snapshot_id)
     children_map = _load_children_map(session, ontology_snapshot_id)
 
     negative_keys = _build_negative_keys(
@@ -222,31 +271,58 @@ def compute_evaluation_data(
         children_map,
     )
 
-    old_terms = _load_experimental_annotations(
-        session, old_annotation_set_id, negative_keys, go_id_map
+    old_by_ns = _load_experimental_annotations_by_ns(
+        session, old_annotation_set_id, negative_keys, go_id_map, aspect_map
     )
-    new_terms = _load_experimental_annotations(
-        session, new_annotation_set_id, negative_keys, go_id_map
+    new_by_ns = _load_experimental_annotations_by_ns(
+        session, new_annotation_set_id, negative_keys, go_id_map, aspect_map
     )
 
     nk: dict[str, set[str]] = {}
-    lk: dict[str, set[str]] = {}
+    lk: dict[str, set[str]] = defaultdict(set)
+    pk: dict[str, set[str]] = defaultdict(set)
+    pk_known: dict[str, set[str]] = defaultdict(set)
 
-    all_proteins = set(old_terms) | set(new_terms)
+    all_proteins = set(old_by_ns) | set(new_by_ns)
     for protein in all_proteins:
-        old_set = old_terms.get(protein, set())
-        new_set = new_terms.get(protein, set())
+        old_ns_map = old_by_ns.get(protein, {})
+        new_ns_map = new_by_ns.get(protein, {})
 
-        if not new_set:
+        new_all = {go for terms in new_ns_map.values() for go in terms}
+        if not new_all:
             continue
 
-        novel = new_set - old_set
-        if not novel:
-            continue
+        had_anything_old = bool(old_ns_map)
 
-        if not old_set:
-            nk[protein] = novel
+        if not had_anything_old:
+            # NK: no experimental annotations anywhere at t0.
+            # Novel = all new terms (nothing to subtract).
+            nk[protein] = new_all
         else:
-            lk[protein] = novel
-
-    return EvaluationData(nk=nk, lk=lk, known=old_terms)
+            # Classify per namespace.
+            for ns in _NAMESPACES:
+                old_ns = old_ns_map.get(ns, set())
+                new_ns = new_ns_map.get(ns, set())
+                delta_ns = new_ns - old_ns
+                if not delta_ns:
+                    continue
+                if not old_ns:
+                    # LK: protein had nothing in this namespace at t0.
+                    lk[protein] |= delta_ns
+                else:
+                    # PK: protein had annotations in this namespace at t0.
+                    pk[protein] |= delta_ns
+                    pk_known[protein] |= old_ns
+
+    # known = all old experimental annotations flattened (for reference download)
+    known = {
+        p: {go for terms in ns_map.values() for go in terms} for p, ns_map in old_by_ns.items()
+    }
+
+    return EvaluationData(
+        nk=nk,
+        lk=dict(lk),
+        pk=dict(pk),
+        pk_known=dict(pk_known),
+        known=known,
+    )
diff --git a/protea/core/evidence_codes.py b/protea/core/evidence_codes.py
index ae083e5..e545f72 100644
--- a/protea/core/evidence_codes.py
+++ b/protea/core/evidence_codes.py
@@ -57,21 +57,23 @@
 # Experimental evidence codes (used as ground truth in CAFA evaluation).
 # Only annotations with these codes are considered when building ground truth.
 # ---------------------------------------------------------------------------
-EXPERIMENTAL: frozenset[str] = frozenset({
-    "EXP",  # Inferred from Experiment
-    "IDA",  # Inferred from Direct Assay
-    "IPI",  # Inferred from Physical Interaction
-    "IMP",  # Inferred from Mutant Phenotype
-    "IGI",  # Inferred from Genetic Interaction
-    "IEP",  # Inferred from Expression Pattern
-    "HTP",  # High Throughput
-    "HDA",  # High Throughput Direct Assay
-    "HMP",  # High Throughput Mutant Phenotype
-    "HGI",  # High Throughput Genetic Interaction
-    "HEP",  # High Throughput Expression Pattern
-    "IC",   # Inferred by Curator
-    "TAS",  # Traceable Author Statement
-})
+EXPERIMENTAL: frozenset[str] = frozenset(
+    {
+        "EXP",  # Inferred from Experiment
+        "IDA",  # Inferred from Direct Assay
+        "IPI",  # Inferred from Physical Interaction
+        "IMP",  # Inferred from Mutant Phenotype
+        "IGI",  # Inferred from Genetic Interaction
+        "IEP",  # Inferred from Expression Pattern
+        "HTP",  # High Throughput
+        "HDA",  # High Throughput Direct Assay
+        "HMP",  # High Throughput Mutant Phenotype
+        "HGI",  # High Throughput Genetic Interaction
+        "HEP",  # High Throughput Expression Pattern
+        "IC",  # Inferred by Curator
+        "TAS",  # Traceable Author Statement
+    }
+)
 
 
 def normalize(code: str) -> str:
diff --git a/protea/core/feature_engineering.py b/protea/core/feature_engineering.py
index 7083339..056a563 100644
--- a/protea/core/feature_engineering.py
+++ b/protea/core/feature_engineering.py
@@ -11,6 +11,7 @@
 - Taxonomy lookups use an LRU cache over lineage queries (ete3 local SQLite).
   First call may trigger a DB download if the ete3 database is absent.
 """
+
 from __future__ import annotations
 
 from functools import lru_cache
@@ -18,12 +19,14 @@
 
 try:
     import parasail  # type: ignore[import-untyped]
+
     _PARASAIL_AVAILABLE = True
 except ImportError:
     _PARASAIL_AVAILABLE = False
 
 try:
     from ete3 import NCBITaxa  # type: ignore[import-untyped]
+
     _ETE3_AVAILABLE = True
 except ImportError:
     _ETE3_AVAILABLE = False
@@ -33,6 +36,7 @@
 # Alignment
 # ---------------------------------------------------------------------------
 
+
 def compute_nw(seq1: str, seq2: str, *, gap_open: int = 10, gap_extend: int = 1) -> dict[str, Any]:
     """Global alignment (Needleman–Wunsch) via parasail/BLOSUM62.
 
@@ -41,7 +45,9 @@ def compute_nw(seq1: str, seq2: str, *, gap_open: int = 10, gap_extend: int = 1)
         gaps_pct_nw, alignment_length_nw, length_query, length_ref
     """
     if not _PARASAIL_AVAILABLE:
-        raise RuntimeError("parasail is required for alignment features. Install it with: pip install parasail")
+        raise RuntimeError(
+            "parasail is required for alignment features. Install it with: pip install parasail"
+        )
 
     result = parasail.nw_trace_striped_32(seq1, seq2, gap_open, gap_extend, parasail.blosum62)
     return _parse_alignment(result, seq1, seq2, suffix="nw")
@@ -55,7 +61,9 @@ def compute_sw(seq1: str, seq2: str, *, gap_open: int = 10, gap_extend: int = 1)
         gaps_pct_sw, alignment_length_sw
     """
     if not _PARASAIL_AVAILABLE:
-        raise RuntimeError("parasail is required for alignment features. Install it with: pip install parasail")
+        raise RuntimeError(
+            "parasail is required for alignment features. Install it with: pip install parasail"
+        )
 
     result = parasail.sw_trace_striped_32(seq1, seq2, gap_open, gap_extend, parasail.blosum62)
     return _parse_alignment(result, seq1, seq2, suffix="sw")
@@ -76,7 +84,9 @@ def _parse_alignment(result: Any, seq1: str, seq2: str, suffix: str) -> dict[str
             f"alignment_length_{suffix}": 0.0,
         }
     else:
-        matches = sum(a == b for a, b in zip(aligned_q, aligned_r, strict=False) if a != "-" and b != "-")
+        matches = sum(
+            a == b for a, b in zip(aligned_q, aligned_r, strict=False) if a != "-" and b != "-"
+        )
         similarity = sum(c in "|:" for c in comp_line)
         gaps = aligned_q.count("-") + aligned_r.count("-")
         out = {
@@ -111,7 +121,9 @@ def compute_alignment(seq1: str, seq2: str) -> dict[str, Any]:
 def _get_ncbi() -> NCBITaxa:
     global _ncbi
     if not _ETE3_AVAILABLE:
-        raise RuntimeError("ete3 is required for taxonomy features. Install it with: pip install ete3")
+        raise RuntimeError(
+            "ete3 is required for taxonomy features. Install it with: pip install ete3"
+        )
     if _ncbi is None:
         _ncbi = NCBITaxa()
     return _ncbi
diff --git a/protea/core/knn_search.py b/protea/core/knn_search.py
index ecca842..99de1e8 100644
--- a/protea/core/knn_search.py
+++ b/protea/core/knn_search.py
@@ -92,7 +92,10 @@ def search_knn(
     """
     if backend == "faiss":
         return _search_faiss(
-            query_embeddings, ref_embeddings, ref_accessions, k,
+            query_embeddings,
+            ref_embeddings,
+            ref_accessions,
+            k,
             distance_threshold=distance_threshold,
             metric=metric,
             index_type=faiss_index_type,
@@ -103,7 +106,10 @@ def search_knn(
         )
     if backend == "numpy":
         return _search_numpy(
-            query_embeddings, ref_embeddings, ref_accessions, k,
+            query_embeddings,
+            ref_embeddings,
+            ref_accessions,
+            k,
             distance_threshold=distance_threshold,
             metric=metric,
         )
@@ -114,6 +120,7 @@ def search_knn(
 # NumPy backend
 # ---------------------------------------------------------------------------
 
+
 def _search_numpy(
     Q: np.ndarray,
     R: np.ndarray,
@@ -142,8 +149,8 @@ def _compute_distance_matrix(Q: np.ndarray, R: np.ndarray, metric: str) -> np.nd
         return 1.0 - (Q_n @ R_n.T)
     elif metric == "l2":
         # ||q - r||^2 = ||q||^2 + ||r||^2 - 2 q·r
-        Q2 = (Q ** 2).sum(axis=1, keepdims=True)
-        R2 = (R ** 2).sum(axis=1)
+        Q2 = (Q**2).sum(axis=1, keepdims=True)
+        R2 = (R**2).sum(axis=1)
         return np.maximum(0.0, Q2 + R2 - 2.0 * (Q @ R.T))
     else:
         raise ValueError(f"Unknown metric: {metric!r}. Choose 'cosine' or 'l2'.")
@@ -153,6 +160,7 @@ def _compute_distance_matrix(Q: np.ndarray, R: np.ndarray, metric: str) -> np.nd
 # FAISS backend
 # ---------------------------------------------------------------------------
 
+
 def _search_faiss(
     Q: np.ndarray,
     R: np.ndarray,
@@ -171,9 +179,7 @@ def _search_faiss(
     try:
         import faiss
     except ImportError as exc:
-        raise ImportError(
-            "FAISS is not installed. Run `pip install faiss-cpu`."
-        ) from exc
+        raise ImportError("FAISS is not installed. Run `pip install faiss-cpu`.") from exc
 
     n_refs, dim = R.shape
 
@@ -187,7 +193,9 @@ def _search_faiss(
         faiss.normalize_L2(R_f)
 
     index = _build_faiss_index(
-        R_f, dim, n_refs,
+        R_f,
+        dim,
+        n_refs,
         metric=metric,
         index_type=index_type,
         nlist=nlist,
@@ -242,20 +250,12 @@ def _build_faiss_index(
     faiss_metric = faiss.METRIC_INNER_PRODUCT if use_ip else faiss.METRIC_L2
 
     if index_type == "Flat":
-        index = (
-            faiss.IndexFlatIP(dim)
-            if use_ip
-            else faiss.IndexFlatL2(dim)
-        )
+        index = faiss.IndexFlatIP(dim) if use_ip else faiss.IndexFlatL2(dim)
 
     elif index_type == "IVFFlat":
         # nlist must be <= n_refs and >= 1
         effective_nlist = max(1, min(nlist, n_refs))
-        quantizer = (
-            faiss.IndexFlatIP(dim)
-            if use_ip
-            else faiss.IndexFlatL2(dim)
-        )
+        quantizer = faiss.IndexFlatIP(dim) if use_ip else faiss.IndexFlatL2(dim)
         index = faiss.IndexIVFFlat(quantizer, dim, effective_nlist, faiss_metric)
         index.train(R_f)
         index.nprobe = min(nprobe, effective_nlist)
@@ -266,8 +266,7 @@ def _build_faiss_index(
 
     else:
         raise ValueError(
-            f"Unknown faiss_index_type: {index_type!r}. "
-            "Choose 'Flat', 'IVFFlat', or 'HNSW'."
+            f"Unknown faiss_index_type: {index_type!r}. Choose 'Flat', 'IVFFlat', or 'HNSW'."
         )
 
     index.add(R_f)
diff --git a/protea/core/metrics.py b/protea/core/metrics.py
new file mode 100644
index 0000000..ba50be9
--- /dev/null
+++ b/protea/core/metrics.py
@@ -0,0 +1,154 @@
+"""CAFA-style precision-recall metrics for GO term prediction evaluation.
+
+Takes scored GOPrediction rows and EvaluationData (ground truth) and computes
+Fmax, AUC-PR, and the full precision-recall curve following the CAFA protocol.
+
+CAFA protocol summary
+---------------------
+- Evaluate only on proteins present in the ground truth (NK or LK).
+- At each score threshold t:
+    precision(t) = mean over proteins-with-predictions of |pred ∩ true| / |pred|
+    recall(t)    = mean over ALL ground-truth proteins of |pred ∩ true| / |true|
+- Fmax = max_t(2 * P(t) * R(t) / (P(t) + R(t)))
+- AUC-PR via trapezoidal integration of the PR curve.
+
+Note: This implementation uses exact GO term matching (no DAG propagation).
+Ancestor propagation is intentionally left for a future iteration.
+"""
+
+from __future__ import annotations
+
+from collections import defaultdict
+from dataclasses import dataclass, field
+from typing import Any
+
+import numpy as np
+
+from protea.core.evaluation import EvaluationData
+
+_N_THRESHOLDS = 101  # sweep [0.0, 0.01, …, 1.0]
+
+
+@dataclass
+class PRPoint:
+    threshold: float
+    precision: float
+    recall: float
+    f1: float
+
+
+@dataclass
+class CAFAMetrics:
+    """CAFA evaluation results for one (PredictionSet, ScoringConfig, category) triple."""
+
+    category: str  # "nk" or "lk"
+    fmax: float
+    threshold_at_fmax: float
+    auc_pr: float
+    n_ground_truth_proteins: int  # proteins in the chosen NK/LK category
+    n_predicted_proteins: int  # proteins that received at least 1 prediction
+    n_predictions: int  # total scored predictions passed in
+    curve: list[PRPoint] = field(default_factory=list)
+
+    def summary(self) -> dict[str, Any]:
+        return {
+            "category": self.category,
+            "fmax": self.fmax,
+            "threshold_at_fmax": self.threshold_at_fmax,
+            "auc_pr": self.auc_pr,
+            "n_ground_truth_proteins": self.n_ground_truth_proteins,
+            "n_predicted_proteins": self.n_predicted_proteins,
+            "n_predictions": self.n_predictions,
+        }
+
+
+def compute_cafa_metrics(
+    scored_predictions: list[dict[str, Any]],
+    evaluation_data: EvaluationData,
+    category: str = "nk",
+) -> CAFAMetrics:
+    """Compute CAFA Fmax and PR curve.
+
+    Parameters
+    ----------
+    scored_predictions:
+        List of dicts, each must have:
+          - ``protein_accession`` (str)
+          - ``go_id`` (str, e.g. "GO:0005488")
+          - ``score`` (float in [0, 1])
+    evaluation_data:
+        Ground truth from ``compute_evaluation_data()``.
+    category:
+        ``"nk"`` (no-knowledge) or ``"lk"`` (limited-knowledge).
+
+    Returns
+    -------
+    CAFAMetrics
+    """
+    if category not in ("nk", "lk"):
+        raise ValueError(f"category must be 'nk' or 'lk', got {category!r}")
+
+    ground_truth: dict[str, set[str]] = (
+        evaluation_data.nk if category == "nk" else evaluation_data.lk
+    )
+
+    # Group predictions by protein, keep only proteins in ground truth
+    preds_by_protein: dict[str, list[tuple[float, str]]] = defaultdict(list)
+    for p in scored_predictions:
+        acc = p["protein_accession"]
+        if acc in ground_truth:
+            preds_by_protein[acc].append((float(p["score"]), str(p["go_id"])))
+
+    n_gt = len(ground_truth)
+    n_predicted = len(preds_by_protein)
+
+    thresholds = np.linspace(0.0, 1.0, _N_THRESHOLDS)
+    curve: list[PRPoint] = []
+    best_f = 0.0
+    best_t = 0.0
+
+    for t in thresholds:
+        t = float(t)
+        tp_sum = 0
+        pred_sum = 0
+        rc_num = 0
+        n_with_preds = 0
+
+        for acc, true_terms in ground_truth.items():
+            predicted = {go for score, go in preds_by_protein.get(acc, []) if score >= t}
+            tp = len(predicted & true_terms)
+            rc_num += tp
+            if predicted:
+                n_with_preds += 1
+                tp_sum += tp
+                pred_sum += len(predicted)
+
+        pr = (tp_sum / pred_sum) if pred_sum > 0 else 0.0
+        rc = (rc_num / sum(len(v) for v in ground_truth.values())) if n_gt > 0 else 0.0
+        f1 = (2 * pr * rc / (pr + rc)) if (pr + rc) > 0 else 0.0
+
+        curve.append(
+            PRPoint(
+                threshold=round(t, 4), precision=round(pr, 6), recall=round(rc, 6), f1=round(f1, 6)
+            )
+        )
+
+        if f1 > best_f:
+            best_f = f1
+            best_t = t
+
+    # AUC-PR: trapezoidal integration (recall on x-axis, precision on y-axis)
+    recalls = [p.recall for p in curve]
+    precisions = [p.precision for p in curve]
+    auc = float(abs(np.trapezoid(precisions, recalls)))
+
+    return CAFAMetrics(
+        category=category,
+        fmax=round(best_f, 4),
+        threshold_at_fmax=round(best_t, 4),
+        auc_pr=round(auc, 4),
+        n_ground_truth_proteins=n_gt,
+        n_predicted_proteins=n_predicted,
+        n_predictions=len(scored_predictions),
+        curve=curve,
+    )
diff --git a/protea/core/operations/compute_embeddings.py b/protea/core/operations/compute_embeddings.py
index dc1733c..633a737 100644
--- a/protea/core/operations/compute_embeddings.py
+++ b/protea/core/operations/compute_embeddings.py
@@ -25,14 +25,15 @@
 
 PositiveInt = Annotated[int, Field(gt=0)]
 
-_BATCH_QUEUE  = "protea.embeddings.batch"
-_WRITE_QUEUE  = "protea.embeddings.write"
+_BATCH_QUEUE = "protea.embeddings.batch"
+_WRITE_QUEUE = "protea.embeddings.write"
 
 
 # ---------------------------------------------------------------------------
 # Data container
 # ---------------------------------------------------------------------------
 
+
 @dataclass
 class ChunkEmbedding:
     """One pooled embedding for a contiguous residue span of a sequence.
@@ -41,15 +42,17 @@ class ChunkEmbedding:
     DB columns: start is 0-based inclusive, end is exclusive.  When chunking
     is disabled, ``chunk_index_s=0`` and ``chunk_index_e=None`` (full sequence).
     """
+
     chunk_index_s: int
     chunk_index_e: int | None
-    vector: np.ndarray   # 1-D float32
+    vector: np.ndarray  # 1-D float32
 
 
 # ---------------------------------------------------------------------------
 # Payloads
 # ---------------------------------------------------------------------------
 
+
 class ComputeEmbeddingsPayload(ProteaPayload, frozen=True):
     """Coordinator payload: decides *which* sequences to embed and how to batch.
 
@@ -105,6 +108,7 @@ class ComputeEmbeddingsBatchPayload(ProteaPayload, frozen=True):
 # Operation
 # ---------------------------------------------------------------------------
 
+
 class ComputeEmbeddingsOperation:
     """Computes protein language model embeddings using a stored EmbeddingConfig.
 
@@ -177,31 +181,41 @@ def execute(
 
         # Partition into batches and create one child Job per batch.
         batches = [
-            sequence_ids[i: i + p.sequences_per_job]
+            sequence_ids[i : i + p.sequences_per_job]
             for i in range(0, len(sequence_ids), p.sequences_per_job)
         ]
         n_batches = len(batches)
 
-        emit("compute_embeddings.dispatching", None, {
-            "total_sequences": len(sequence_ids),
-            "sequences_per_job": p.sequences_per_job,
-            "batches": n_batches,
-        }, "info")
+        emit(
+            "compute_embeddings.dispatching",
+            None,
+            {
+                "total_sequences": len(sequence_ids),
+                "sequences_per_job": p.sequences_per_job,
+                "batches": n_batches,
+            },
+            "info",
+        )
 
         operations: list[tuple[str, dict]] = []
         for batch_seq_ids in batches:
-            operations.append((_BATCH_QUEUE, {
-                "operation": "compute_embeddings_batch",
-                "job_id": str(parent_job_id),
-                "payload": {
-                    "embedding_config_id": p.embedding_config_id,
-                    "sequence_ids": batch_seq_ids,
-                    "parent_job_id": str(parent_job_id),
-                    "device": p.device,
-                    "skip_existing": p.skip_existing,
-                    "batch_size": p.batch_size,
-                },
-            }))
+            operations.append(
+                (
+                    _BATCH_QUEUE,
+                    {
+                        "operation": "compute_embeddings_batch",
+                        "job_id": str(parent_job_id),
+                        "payload": {
+                            "embedding_config_id": p.embedding_config_id,
+                            "sequence_ids": batch_seq_ids,
+                            "parent_job_id": str(parent_job_id),
+                            "device": p.device,
+                            "skip_existing": p.skip_existing,
+                            "batch_size": p.batch_size,
+                        },
+                    },
+                )
+            )
 
         return OperationResult(
             result={"batches": n_batches, "sequences": len(sequence_ids)},
@@ -249,8 +263,9 @@ def _load_sequence_ids(
             q = q.filter(~already_embedded)
 
         ids = [row[0] for row in q.all()]
-        emit("compute_embeddings.load_sequences_done", None,
-             {"sequences_to_embed": len(ids)}, "info")
+        emit(
+            "compute_embeddings.load_sequences_done", None, {"sequences_to_embed": len(ids)}, "info"
+        )
         return ids
 
     def _embed_batch(
@@ -274,6 +289,7 @@ def _embed_batch(
 # Batch operation (child job)
 # ---------------------------------------------------------------------------
 
+
 class ComputeEmbeddingsBatchOperation:
     """Processes one batch of sequences for a parent compute_embeddings job.
 
@@ -295,8 +311,12 @@ def execute(
         # batch message was waiting in the queue.
         parent = session.get(Job, parent_job_id)
         if parent is not None and parent.status in (JobStatus.CANCELLED, JobStatus.FAILED):
-            emit("compute_embeddings_batch.skipped", None,
-                 {"reason": "parent_not_running", "parent_status": parent.status.value}, "warning")
+            emit(
+                "compute_embeddings_batch.skipped",
+                None,
+                {"reason": "parent_not_running", "parent_status": parent.status.value},
+                "warning",
+            )
             return OperationResult(result={"skipped": True})
 
         config = session.get(EmbeddingConfig, config_id)
@@ -306,60 +326,81 @@ def execute(
         sequences = session.query(Sequence).filter(Sequence.id.in_(p.sequence_ids)).all()
 
         t0 = time.perf_counter()
-        emit("compute_embeddings_batch.start", None, {
-            "sequences": len(sequences),
-            "parent_job_id": str(parent_job_id),
-        }, "info")
+        emit(
+            "compute_embeddings_batch.start",
+            None,
+            {
+                "sequences": len(sequences),
+                "parent_job_id": str(parent_job_id),
+            },
+            "info",
+        )
 
         model, tokenizer = self._load_model(config, p.device, emit)
 
         # Run inference only — no DB writes here.
         write_sequences = []
         for i in range(0, len(sequences), p.batch_size):
-            batch    = sequences[i: i + p.batch_size]
+            batch = sequences[i : i + p.batch_size]
             seq_strs = [s.sequence for s in batch]
             batch_chunks = self._embed_batch(model, tokenizer, seq_strs, config, p.device)
             for seq, chunks in zip(batch, batch_chunks, strict=False):
-                write_sequences.append({
-                    "sequence_id": seq.id,
-                    "chunks": [
-                        {
-                            "chunk_index_s": c.chunk_index_s,
-                            "chunk_index_e": c.chunk_index_e,
-                            "vector":        c.vector.tolist(),
-                            "embedding_dim": int(c.vector.shape[0]),
-                        }
-                        for c in chunks
-                    ],
-                })
+                write_sequences.append(
+                    {
+                        "sequence_id": seq.id,
+                        "chunks": [
+                            {
+                                "chunk_index_s": c.chunk_index_s,
+                                "chunk_index_e": c.chunk_index_e,
+                                "vector": c.vector.tolist(),
+                                "embedding_dim": int(c.vector.shape[0]),
+                            }
+                            for c in chunks
+                        ],
+                    }
+                )
 
         elapsed = time.perf_counter() - t0
-        emit("compute_embeddings_batch.done", None, {
-            "sequences_inferred": len(write_sequences),
-            "elapsed_seconds":    elapsed,
-        }, "info")
+        emit(
+            "compute_embeddings_batch.done",
+            None,
+            {
+                "sequences_inferred": len(write_sequences),
+                "elapsed_seconds": elapsed,
+            },
+            "info",
+        )
 
         # Hand off to the write worker — GPU is free to take the next batch.
         return OperationResult(
             result={"sequences_inferred": len(write_sequences)},
-            publish_operations=[(_WRITE_QUEUE, {
-                "operation": "store_embeddings",
-                "job_id":    str(parent_job_id),
-                "payload": {
-                    "parent_job_id":      str(parent_job_id),
-                    "embedding_config_id": p.embedding_config_id,
-                    "skip_existing":      p.skip_existing,
-                    "sequences":          write_sequences,
-                },
-            })],
+            publish_operations=[
+                (
+                    _WRITE_QUEUE,
+                    {
+                        "operation": "store_embeddings",
+                        "job_id": str(parent_job_id),
+                        "payload": {
+                            "parent_job_id": str(parent_job_id),
+                            "embedding_config_id": p.embedding_config_id,
+                            "skip_existing": p.skip_existing,
+                            "sequences": write_sequences,
+                        },
+                    },
+                )
+            ],
         )
 
     def _load_model(self, config: EmbeddingConfig, device: str, emit: EmitFn) -> tuple[Any, Any]:
         return _get_or_load_model(config, device, emit)
 
     def _embed_batch(
-        self, model: Any, tokenizer: Any, sequences: list[str],
-        config: EmbeddingConfig, device: str,
+        self,
+        model: Any,
+        tokenizer: Any,
+        sequences: list[str],
+        config: EmbeddingConfig,
+        device: str,
     ) -> list[list[ChunkEmbedding]]:
         if config.model_backend == "esm3c":
             return _embed_esm3c(model, sequences, config, device)
@@ -373,11 +414,13 @@ def _embed_batch(
 # Write operation (CPU worker — no GPU required)
 # ---------------------------------------------------------------------------
 
+
 class StoreEmbeddingsPayload(ProteaPayload, frozen=True):
     """Payload published by ComputeEmbeddingsBatchOperation after inference."""
-    parent_job_id:      str
+
+    parent_job_id: str
     embedding_config_id: str
-    skip_existing:      bool = True
+    skip_existing: bool = True
     sequences: list[dict[str, Any]]  # [{"sequence_id": int, "chunks": [...]}]
 
 
@@ -394,28 +437,34 @@ def execute(
         self, session: Session, payload: dict[str, Any], *, emit: EmitFn
     ) -> OperationResult:
         p = StoreEmbeddingsPayload.model_validate(payload)
-        config_id     = uuid.UUID(p.embedding_config_id)
+        config_id = uuid.UUID(p.embedding_config_id)
         parent_job_id = UUID(p.parent_job_id)
 
         parent = session.get(Job, parent_job_id)
         if parent is not None and parent.status in (JobStatus.CANCELLED, JobStatus.FAILED):
-            emit("store_embeddings.skipped", None,
-                 {"reason": "parent_not_running", "parent_status": parent.status.value}, "warning")
+            emit(
+                "store_embeddings.skipped",
+                None,
+                {"reason": "parent_not_running", "parent_status": parent.status.value},
+                "warning",
+            )
             return OperationResult(result={"skipped": True})
 
-        embeddings_stored  = 0
-        sequences_skipped  = 0
+        embeddings_stored = 0
+        sequences_skipped = 0
 
         rows_to_insert: list[dict] = []
 
         for seq_data in p.sequences:
             sequence_id = seq_data["sequence_id"]
-            chunks      = seq_data["chunks"]
+            chunks = seq_data["chunks"]
 
             if p.skip_existing:
-                existing = session.query(SequenceEmbedding).filter_by(
-                    sequence_id=sequence_id, embedding_config_id=config_id
-                ).first()
+                existing = (
+                    session.query(SequenceEmbedding)
+                    .filter_by(sequence_id=sequence_id, embedding_config_id=config_id)
+                    .first()
+                )
                 if existing is not None:
                     sequences_skipped += 1
                     continue
@@ -425,14 +474,16 @@ def execute(
                 ).delete()
 
             for chunk in chunks:
-                rows_to_insert.append({
-                    "sequence_id":        sequence_id,
-                    "embedding_config_id": config_id,
-                    "chunk_index_s":      chunk["chunk_index_s"],
-                    "chunk_index_e":      chunk.get("chunk_index_e"),
-                    "embedding":          chunk["vector"],
-                    "embedding_dim":      chunk["embedding_dim"],
-                })
+                rows_to_insert.append(
+                    {
+                        "sequence_id": sequence_id,
+                        "embedding_config_id": config_id,
+                        "chunk_index_s": chunk["chunk_index_s"],
+                        "chunk_index_e": chunk.get("chunk_index_e"),
+                        "embedding": chunk["vector"],
+                        "embedding_dim": chunk["embedding_dim"],
+                    }
+                )
                 embeddings_stored += 1
 
         if rows_to_insert:
@@ -441,21 +492,26 @@ def execute(
                 rows_to_insert,
             )
 
-        emit("store_embeddings.done", None, {
-            "embeddings_stored": embeddings_stored,
-            "sequences_skipped": sequences_skipped,
-        }, "info")
+        emit(
+            "store_embeddings.done",
+            None,
+            {
+                "embeddings_stored": embeddings_stored,
+                "sequences_skipped": sequences_skipped,
+            },
+            "info",
+        )
 
         self._update_parent_progress(session, parent_job_id, emit)
 
-        return OperationResult(result={
-            "embeddings_stored": embeddings_stored,
-            "sequences_skipped": sequences_skipped,
-        })
+        return OperationResult(
+            result={
+                "embeddings_stored": embeddings_stored,
+                "sequences_skipped": sequences_skipped,
+            }
+        )
 
-    def _update_parent_progress(
-        self, session: Session, parent_job_id: UUID, emit: EmitFn
-    ) -> None:
+    def _update_parent_progress(self, session: Session, parent_job_id: UUID, emit: EmitFn) -> None:
         row = session.execute(
             sa_update(Job)
             .where(Job.id == parent_job_id, Job.status == JobStatus.RUNNING)
@@ -474,14 +530,20 @@ def _update_parent_progress(
         ).fetchone()
 
         if closed:
-            session.add(JobEvent(
-                job_id=parent_job_id,
-                event="job.succeeded",
-                fields={"via": "last_batch_stored"},
-                level="info",
-            ))
-            emit("store_embeddings.parent_succeeded", None,
-                 {"parent_job_id": str(parent_job_id)}, "info")
+            session.add(
+                JobEvent(
+                    job_id=parent_job_id,
+                    event="job.succeeded",
+                    fields={"via": "last_batch_stored"},
+                    level="info",
+                )
+            )
+            emit(
+                "store_embeddings.parent_succeeded",
+                None,
+                {"parent_job_id": str(parent_job_id)},
+                "info",
+            )
 
 
 # ---------------------------------------------------------------------------
@@ -504,11 +566,16 @@ def _get_or_load_model(config: EmbeddingConfig, device: str, emit: EmitFn) -> tu
 def _load_model(config: EmbeddingConfig, device: str, emit: EmitFn) -> tuple[Any, Any]:
     import torch
 
-    emit("compute_embeddings.model_load_start", None,
-         {"model_name": config.model_name, "backend": config.model_backend}, "info")
+    emit(
+        "compute_embeddings.model_load_start",
+        None,
+        {"model_name": config.model_name, "backend": config.model_backend},
+        "info",
+    )
 
     if config.model_backend == "esm3c":
         from esm.models.esmc import ESMC
+
         device_obj = torch.device(device)
         dtype = torch.float16 if device_obj.type == "cuda" else torch.float32
         model = ESMC.from_pretrained(config.model_name)
@@ -519,6 +586,7 @@ def _load_model(config: EmbeddingConfig, device: str, emit: EmitFn) -> tuple[Any
 
     elif config.model_backend in ("esm", "auto"):
         from transformers import AutoTokenizer, EsmModel
+
         tokenizer = AutoTokenizer.from_pretrained(config.model_name)
         model = EsmModel.from_pretrained(config.model_name, output_hidden_states=True)
         model.eval()
@@ -526,6 +594,7 @@ def _load_model(config: EmbeddingConfig, device: str, emit: EmitFn) -> tuple[Any
 
     elif config.model_backend == "t5":
         from transformers import T5EncoderModel, T5Tokenizer
+
         device_obj = torch.device(device)
         dtype = torch.float16 if device_obj.type == "cuda" else torch.float32
         tokenizer = T5Tokenizer.from_pretrained(config.model_name, do_lower_case=False)
@@ -548,6 +617,7 @@ def _load_model(config: EmbeddingConfig, device: str, emit: EmitFn) -> tuple[Any
 # Backend: ESM (HuggingFace EsmModel)
 # ---------------------------------------------------------------------------
 
+
 def _embed_esm(
     model: Any,
     tokenizer: Any,
@@ -586,8 +656,7 @@ def _embed_esm(
             if config.pooling == "cls":
                 # CLS token at position 0 of the raw hidden states
                 layer_tensors_1d = [
-                    hidden_states[-(li + 1)][0, 0, :].float()
-                    for li in valid_layers
+                    hidden_states[-(li + 1)][0, 0, :].float() for li in valid_layers
                 ]
                 pooled = _aggregate_1d(layer_tensors_1d, config.layer_agg)
                 if config.normalize:
@@ -598,7 +667,7 @@ def _embed_esm(
                 # attention_mask.sum() = CLS + content + EOS
                 actual_len = int(tokens["attention_mask"].sum().item())
                 layer_tensors_2d = [
-                    hidden_states[-(li + 1)][0, 1:actual_len - 1, :].float()
+                    hidden_states[-(li + 1)][0, 1 : actual_len - 1, :].float()
                     for li in valid_layers
                 ]
                 residues = _aggregate_residue_layers(layer_tensors_2d, config.layer_agg)
@@ -616,6 +685,7 @@ def _embed_esm(
 # Backend: T5 (HuggingFace T5EncoderModel)
 # ---------------------------------------------------------------------------
 
+
 def _embed_t5(
     model: Any,
     tokenizer: Any,
@@ -664,9 +734,7 @@ def _embed_t5(
     del outputs
     torch.cuda.empty_cache()
 
-    valid_layers = _validate_layers(
-        config.layer_indices, hidden_states, "T5", "batch"
-    )
+    valid_layers = _validate_layers(config.layer_indices, hidden_states, "T5", "batch")
 
     results: list[list[ChunkEmbedding]] = []
     for i in range(len(sequences)):
@@ -674,18 +742,14 @@ def _embed_t5(
         actual_len = int(inputs["attention_mask"][i].sum().item())
 
         if config.pooling == "cls":
-            layer_tensors_1d = [
-                hidden_states[-(li + 1)][i, 0, :].float()
-                for li in valid_layers
-            ]
+            layer_tensors_1d = [hidden_states[-(li + 1)][i, 0, :].float() for li in valid_layers]
             pooled = _aggregate_1d(layer_tensors_1d, config.layer_agg)
             if config.normalize:
                 pooled = F.normalize(pooled.unsqueeze(0), p=2, dim=1).squeeze(0)
             results.append([ChunkEmbedding(0, None, pooled.cpu().numpy())])
         else:
             layer_tensors_2d = [
-                hidden_states[-(li + 1)][i, :actual_len, :].float()
-                for li in valid_layers
+                hidden_states[-(li + 1)][i, :actual_len, :].float() for li in valid_layers
             ]
             residues = _aggregate_residue_layers(layer_tensors_2d, config.layer_agg)
             if config.normalize_residues:
@@ -702,6 +766,7 @@ def _embed_t5(
 # Backend: ESM3c (ESM SDK ESMC)
 # ---------------------------------------------------------------------------
 
+
 def _embed_esm3c(
     model: Any,
     sequences: list[str],
@@ -726,7 +791,7 @@ def _embed_esm3c(
 
     with torch.no_grad():
         for seq_str in sequences:
-            protein = ESMProtein(sequence=seq_str[:config.max_length])
+            protein = ESMProtein(sequence=seq_str[: config.max_length])
 
             with torch.autocast(
                 device_type=device_obj.type,
@@ -741,32 +806,24 @@ def _embed_esm3c(
 
             hs = logits_output.hidden_states
             if hs is None:
-                raise RuntimeError(
-                    f"ESM3c returned no hidden_states for sequence {seq_str[:20]!r}"
-                )
+                raise RuntimeError(f"ESM3c returned no hidden_states for sequence {seq_str[:20]!r}")
 
             # Normalise to a list of per-layer tensors [1, L, D]
             if isinstance(hs, torch.Tensor):
                 hs = [hs[i] for i in range(hs.shape[0])]
 
-            valid_layers = _validate_layers(
-                config.layer_indices, hs, "ESM3c", seq_str[:20]
-            )
+            valid_layers = _validate_layers(config.layer_indices, hs, "ESM3c", seq_str[:20])
 
             if config.pooling == "cls":
                 # BOS token at position 0 (before stripping)
-                layer_tensors_1d = [
-                    hs[-(li + 1)][0, 0, :].float() for li in valid_layers
-                ]
+                layer_tensors_1d = [hs[-(li + 1)][0, 0, :].float() for li in valid_layers]
                 pooled = _aggregate_1d(layer_tensors_1d, config.layer_agg)
                 if config.normalize:
                     pooled = F.normalize(pooled.unsqueeze(0), p=2, dim=1).squeeze(0)
                 results.append([ChunkEmbedding(0, None, pooled.cpu().numpy())])
             else:
                 # Strip BOS (0) and EOS (-1): positions [1:-1]
-                layer_tensors_2d = [
-                    hs[-(li + 1)][0, 1:-1, :].float() for li in valid_layers
-                ]
+                layer_tensors_2d = [hs[-(li + 1)][0, 1:-1, :].float() for li in valid_layers]
                 residues = _aggregate_residue_layers(layer_tensors_2d, config.layer_agg)
                 if config.normalize_residues:
                     residues = F.normalize(residues, p=2, dim=1)
@@ -782,6 +839,7 @@ def _embed_esm3c(
 # Shared helpers
 # ---------------------------------------------------------------------------
 
+
 def _validate_layers(
     layer_indices: list[int],
     hidden_states: Any,
@@ -845,9 +903,7 @@ def _chunk_and_pool(residues: Any, config: EmbeddingConfig) -> list[ChunkEmbeddi
     import torch.nn.functional as F
 
     if config.use_chunking:
-        spans = _compute_chunk_spans(
-            residues.shape[0], config.chunk_size, config.chunk_overlap
-        )
+        spans = _compute_chunk_spans(residues.shape[0], config.chunk_size, config.chunk_overlap)
     else:
         spans = [(0, residues.shape[0])]
 
@@ -871,18 +927,18 @@ def _chunk_and_pool(residues: Any, config: EmbeddingConfig) -> list[ChunkEmbeddi
             pooled = F.normalize(pooled.unsqueeze(0), p=2, dim=1).squeeze(0)
 
         chunk_index_e = end if config.use_chunking else None
-        results.append(ChunkEmbedding(
-            chunk_index_s=start,
-            chunk_index_e=chunk_index_e,
-            vector=pooled.float().cpu().numpy(),
-        ))
+        results.append(
+            ChunkEmbedding(
+                chunk_index_s=start,
+                chunk_index_e=chunk_index_e,
+                vector=pooled.float().cpu().numpy(),
+            )
+        )
 
     return results
 
 
-def _compute_chunk_spans(
-    length: int, chunk_size: int, overlap: int
-) -> list[tuple[int, int]]:
+def _compute_chunk_spans(length: int, chunk_size: int, overlap: int) -> list[tuple[int, int]]:
     """Compute (start, end) spans for overlapping chunks over a sequence of ``length`` residues.
 
     Raises ``ValueError`` if ``overlap >= chunk_size`` — such a configuration
diff --git a/protea/core/operations/fetch_uniprot_metadata.py b/protea/core/operations/fetch_uniprot_metadata.py
index b6849ea..552a8e5 100644
--- a/protea/core/operations/fetch_uniprot_metadata.py
+++ b/protea/core/operations/fetch_uniprot_metadata.py
@@ -84,12 +84,18 @@ def __init__(self) -> None:
         self._total_results: int | None = None
         self._http = requests.Session()
 
-    def execute(self, session: Session, payload: dict[str, Any], *, emit: EmitFn) -> OperationResult:
+    def execute(
+        self, session: Session, payload: dict[str, Any], *, emit: EmitFn
+    ) -> OperationResult:
         p = FetchUniProtMetadataPayload.model_validate(payload)
 
         t0 = time.perf_counter()
-        emit("fetch_uniprot_metadata.start", None,
-             {"search_criteria": p.search_criteria, "page_size": p.page_size}, "info")
+        emit(
+            "fetch_uniprot_metadata.start",
+            None,
+            {"search_criteria": p.search_criteria, "page_size": p.page_size},
+            "info",
+        )
 
         pages = 0
         total_rows = 0
@@ -123,8 +129,11 @@ def execute(self, session: Session, payload: dict[str, Any], *, emit: EmitFn) ->
                     "http_requests": self._http_requests,
                     "http_retries": self._http_retries,
                     "_progress_current": total_rows,
-                    **({"_progress_total": p.total_limit or self._total_results}
-                       if (p.total_limit or self._total_results) else {}),
+                    **(
+                        {"_progress_total": p.total_limit or self._total_results}
+                        if (p.total_limit or self._total_results)
+                        else {}
+                    ),
                 },
                 "info",
             )
@@ -133,7 +142,12 @@ def execute(self, session: Session, payload: dict[str, Any], *, emit: EmitFn) ->
                 session.commit()
 
             if p.total_limit is not None and total_rows >= p.total_limit:
-                emit("fetch_uniprot_metadata.limit_reached", None, {"total_limit": p.total_limit}, "warning")
+                emit(
+                    "fetch_uniprot_metadata.limit_reached",
+                    None,
+                    {"total_limit": p.total_limit},
+                    "warning",
+                )
                 break
 
         elapsed = time.perf_counter() - t0
@@ -151,14 +165,37 @@ def execute(self, session: Session, payload: dict[str, Any], *, emit: EmitFn) ->
 
     # ---------------- HTTP / paging ----------------
 
-    def _fetch_tsv_pages(self, p: FetchUniProtMetadataPayload, emit: EmitFn) -> Iterable[list[dict[str, str]]]:
+    def _fetch_tsv_pages(
+        self, p: FetchUniProtMetadataPayload, emit: EmitFn
+    ) -> Iterable[list[dict[str, str]]]:
         encoded_query = quote(p.search_criteria)
 
         fields = [
-            "accession", "reviewed", "id", "protein_name", "gene_names", "organism_name", "length",
-            "absorption", "ft_act_site", "ft_binding", "cc_catalytic_activity", "cc_cofactor", "ft_dna_bind",
-            "ec", "cc_activity_regulation", "cc_function", "cc_pathway", "kinetics", "ph_dependence",
-            "redox_potential", "rhea", "ft_site", "temp_dependence", "keyword", "feature_count",
+            "accession",
+            "reviewed",
+            "id",
+            "protein_name",
+            "gene_names",
+            "organism_name",
+            "length",
+            "absorption",
+            "ft_act_site",
+            "ft_binding",
+            "cc_catalytic_activity",
+            "cc_cofactor",
+            "ft_dna_bind",
+            "ec",
+            "cc_activity_regulation",
+            "cc_function",
+            "cc_pathway",
+            "kinetics",
+            "ph_dependence",
+            "redox_potential",
+            "rhea",
+            "ft_site",
+            "temp_dependence",
+            "keyword",
+            "feature_count",
         ]
 
         params = [
@@ -176,7 +213,12 @@ def _fetch_tsv_pages(self, p: FetchUniProtMetadataPayload, emit: EmitFn) -> Iter
         while True:
             page += 1
             url = base_url if not next_cursor else f"{base_url}&cursor={next_cursor}"
-            emit("uniprot.fetch_page_start", None, {"page": page, "has_cursor": bool(next_cursor)}, "info")
+            emit(
+                "uniprot.fetch_page_start",
+                None,
+                {"page": page, "has_cursor": bool(next_cursor)},
+                "info",
+            )
 
             resp = self._get_with_retries(url, p, emit)
             if self._total_results is None:
diff --git a/protea/core/operations/generate_evaluation_set.py b/protea/core/operations/generate_evaluation_set.py
index c010e6e..771ab41 100644
--- a/protea/core/operations/generate_evaluation_set.py
+++ b/protea/core/operations/generate_evaluation_set.py
@@ -51,15 +51,18 @@ def execute(
         if new_set is None:
             raise ValueError(f"AnnotationSet {new_set_id} not found")
         if old_set.ontology_snapshot_id != new_set.ontology_snapshot_id:
-            raise ValueError(
-                "Both annotation sets must use the same ontology snapshot"
-            )
-
-        emit("generate_evaluation_set.start", None, {
-            "old_annotation_set_id": str(old_set_id),
-            "new_annotation_set_id": str(new_set_id),
-            "ontology_snapshot_id": str(old_set.ontology_snapshot_id),
-        }, "info")
+            raise ValueError("Both annotation sets must use the same ontology snapshot")
+
+        emit(
+            "generate_evaluation_set.start",
+            None,
+            {
+                "old_annotation_set_id": str(old_set_id),
+                "new_annotation_set_id": str(new_set_id),
+                "ontology_snapshot_id": str(old_set.ontology_snapshot_id),
+            },
+            "info",
+        )
 
         emit("generate_evaluation_set.computing_delta", None, {}, "info")
         data = compute_evaluation_data(
diff --git a/protea/core/operations/insert_proteins.py b/protea/core/operations/insert_proteins.py
index d28232c..0bf3963 100644
--- a/protea/core/operations/insert_proteins.py
+++ b/protea/core/operations/insert_proteins.py
@@ -66,11 +66,18 @@ def __init__(self) -> None:
         self._total_results: int | None = None
         self._http = requests.Session()
 
-    def execute(self, session: Session, payload: dict[str, Any], *, emit: EmitFn) -> OperationResult:
+    def execute(
+        self, session: Session, payload: dict[str, Any], *, emit: EmitFn
+    ) -> OperationResult:
         p = InsertProteinsPayload.model_validate(payload)
 
         t0 = time.perf_counter()
-        emit("insert_proteins.start", None, {"search_criteria": p.search_criteria, "page_size": p.page_size}, "info")
+        emit(
+            "insert_proteins.start",
+            None,
+            {"search_criteria": p.search_criteria, "page_size": p.page_size},
+            "info",
+        )
 
         pages = 0
         retrieved = 0
@@ -112,14 +119,19 @@ def execute(self, session: Session, payload: dict[str, Any], *, emit: EmitFn) ->
                     "http_requests": self._http_requests,
                     "http_retries": self._http_retries,
                     "_progress_current": retrieved,
-                    **({"_progress_total": p.total_limit or self._total_results}
-                       if (p.total_limit or self._total_results) else {}),
+                    **(
+                        {"_progress_total": p.total_limit or self._total_results}
+                        if (p.total_limit or self._total_results)
+                        else {}
+                    ),
                 },
                 "info",
             )
 
             if p.total_limit is not None and retrieved >= p.total_limit:
-                emit("insert_proteins.limit_reached", None, {"total_limit": p.total_limit}, "warning")
+                emit(
+                    "insert_proteins.limit_reached", None, {"total_limit": p.total_limit}, "warning"
+                )
                 break
 
         elapsed = time.perf_counter() - t0
@@ -157,7 +169,9 @@ def execute(self, session: Session, payload: dict[str, Any], *, emit: EmitFn) ->
         )
 
     # ---- HTTP paging ----
-    def _fetch_fasta_pages(self, p: InsertProteinsPayload, emit: EmitFn) -> Iterable[list[dict[str, Any]]]:
+    def _fetch_fasta_pages(
+        self, p: InsertProteinsPayload, emit: EmitFn
+    ) -> Iterable[list[dict[str, Any]]]:
         encoded_query = quote(p.search_criteria)
         params = ["format=fasta", f"query={encoded_query}", f"size={p.page_size}"]
         if p.include_isoforms:
@@ -172,7 +186,12 @@ def _fetch_fasta_pages(self, p: InsertProteinsPayload, emit: EmitFn) -> Iterable
         while True:
             page += 1
             url = base_url if not next_cursor else f"{base_url}&cursor={next_cursor}"
-            emit("uniprot.fetch_page_start", None, {"page": page, "has_cursor": bool(next_cursor)}, "info")
+            emit(
+                "uniprot.fetch_page_start",
+                None,
+                {"page": page, "has_cursor": bool(next_cursor)},
+                "info",
+            )
 
             resp = self._get_with_retries(url, p, emit)
             if self._total_results is None:
@@ -302,7 +321,9 @@ def _store_records(
         if missing_hashes:
             emit("db.insert_sequences_start", None, {"rows": len(missing_hashes)}, "info")
 
-            new_sequences = [SequenceModel(sequence=hash_to_seq[h], sequence_hash=h) for h in missing_hashes]
+            new_sequences = [
+                SequenceModel(sequence=hash_to_seq[h], sequence_hash=h) for h in missing_hashes
+            ]
             session.add_all(new_sequences)
             session.flush()
 
@@ -398,7 +419,9 @@ def _store_records(
 
         return proteins_inserted, proteins_updated, sequences_inserted, sequences_reused
 
-    def _load_existing_sequences(self, session: Session, hashes: Seq[str], chunk_size: int = 5000) -> dict[str, int]:
+    def _load_existing_sequences(
+        self, session: Session, hashes: Seq[str], chunk_size: int = 5000
+    ) -> dict[str, int]:
         existing: dict[str, int] = {}
         for chunk in chunks(hashes, chunk_size):
             rows = (
diff --git a/protea/core/operations/load_goa_annotations.py b/protea/core/operations/load_goa_annotations.py
index 3a59464..e52f515 100644
--- a/protea/core/operations/load_goa_annotations.py
+++ b/protea/core/operations/load_goa_annotations.py
@@ -73,7 +73,9 @@ class LoadGOAAnnotationsOperation:
     _IDX_ASSIGNED_BY = 14
     _IDX_DATE = 13
 
-    def execute(self, session: Session, payload: dict[str, Any], *, emit: EmitFn) -> OperationResult:
+    def execute(
+        self, session: Session, payload: dict[str, Any], *, emit: EmitFn
+    ) -> OperationResult:
         p = LoadGOAAnnotationsPayload.model_validate(payload)
 
         snapshot_id = uuid.UUID(p.ontology_snapshot_id)
@@ -82,10 +84,15 @@ def execute(self, session: Session, payload: dict[str, Any], *, emit: EmitFn) ->
             raise ValueError(f"OntologySnapshot {p.ontology_snapshot_id} not found")
 
         t0 = time.perf_counter()
-        emit("load_goa_annotations.start", None, {
-            "gaf_url": p.gaf_url,
-            "ontology_snapshot_id": p.ontology_snapshot_id,
-        }, "info")
+        emit(
+            "load_goa_annotations.start",
+            None,
+            {
+                "gaf_url": p.gaf_url,
+                "ontology_snapshot_id": p.ontology_snapshot_id,
+            },
+            "info",
+        )
 
         canonical_accessions = self._load_accessions(session, emit)
         if not canonical_accessions:
@@ -103,8 +110,12 @@ def execute(self, session: Session, payload: dict[str, Any], *, emit: EmitFn) ->
         session.add(annotation_set)
         session.flush()
 
-        emit("load_goa_annotations.annotation_set_created", None,
-             {"annotation_set_id": str(annotation_set.id)}, "info")
+        emit(
+            "load_goa_annotations.annotation_set_created",
+            None,
+            {"annotation_set_id": str(annotation_set.id)},
+            "info",
+        )
 
         total_lines = 0
         total_inserted = 0
@@ -116,7 +127,12 @@ def execute(self, session: Session, payload: dict[str, Any], *, emit: EmitFn) ->
             total_lines += 1
 
             if p.total_limit is not None and total_inserted >= p.total_limit:
-                emit("load_goa_annotations.limit_reached", None, {"total_limit": p.total_limit}, "warning")
+                emit(
+                    "load_goa_annotations.limit_reached",
+                    None,
+                    {"total_limit": p.total_limit},
+                    "warning",
+                )
                 break
 
             buffer.append(record)
@@ -130,12 +146,17 @@ def execute(self, session: Session, payload: dict[str, Any], *, emit: EmitFn) ->
                 total_skipped += skipped
                 buffer.clear()
 
-                emit("load_goa_annotations.page_done", None, {
-                    "page": pages,
-                    "total_lines": total_lines,
-                    "total_inserted": total_inserted,
-                    "total_skipped": total_skipped,
-                }, "info")
+                emit(
+                    "load_goa_annotations.page_done",
+                    None,
+                    {
+                        "page": pages,
+                        "total_lines": total_lines,
+                        "total_inserted": total_inserted,
+                        "total_skipped": total_skipped,
+                    },
+                    "info",
+                )
 
                 if p.commit_every_page:
                     session.commit()
@@ -164,8 +185,12 @@ def execute(self, session: Session, payload: dict[str, Any], *, emit: EmitFn) ->
     def _load_accessions(self, session: Session, emit: EmitFn) -> set[str]:
         emit("load_goa_annotations.load_accessions_start", None, {}, "info")
         accessions = set(session.scalars(select(distinct(Protein.canonical_accession))))
-        emit("load_goa_annotations.load_accessions_done", None,
-             {"canonical_accessions": len(accessions)}, "info")
+        emit(
+            "load_goa_annotations.load_accessions_done",
+            None,
+            {"canonical_accessions": len(accessions)},
+            "info",
+        )
         return accessions
 
     def _load_go_term_map(
@@ -181,9 +206,7 @@ def _load_go_term_map(
         emit("load_goa_annotations.load_go_terms_done", None, {"go_terms": len(mapping)}, "info")
         return mapping
 
-    def _stream_gaf(
-        self, p: LoadGOAAnnotationsPayload, emit: EmitFn
-    ) -> Iterator[dict[str, str]]:
+    def _stream_gaf(self, p: LoadGOAAnnotationsPayload, emit: EmitFn) -> Iterator[dict[str, str]]:
         emit("load_goa_annotations.download_start", None, {"gaf_url": p.gaf_url}, "info")
         resp = requests.get(p.gaf_url, stream=True, timeout=p.timeout_seconds)
         resp.raise_for_status()
@@ -249,27 +272,28 @@ def _store_buffer(
                 continue
             seen.add(dedup_key)
 
-            to_add.append({
-                "annotation_set_id": annotation_set_id,
-                "protein_accession": accession,
-                "go_term_id": go_term_id,
-                "qualifier": rec["qualifier"] or None,
-                "evidence_code": evidence_code,
-                "assigned_by": rec["assigned_by"] or None,
-                "db_reference": rec["db_reference"] or None,
-                "with_from": rec["with_from"] or None,
-                "annotation_date": rec["annotation_date"] or None,
-            })
+            to_add.append(
+                {
+                    "annotation_set_id": annotation_set_id,
+                    "protein_accession": accession,
+                    "go_term_id": go_term_id,
+                    "qualifier": rec["qualifier"] or None,
+                    "evidence_code": evidence_code,
+                    "assigned_by": rec["assigned_by"] or None,
+                    "db_reference": rec["db_reference"] or None,
+                    "with_from": rec["with_from"] or None,
+                    "annotation_date": rec["annotation_date"] or None,
+                }
+            )
 
         if to_add:
             from sqlalchemy.dialects.postgresql import insert as pg_insert
+
             chunk_size = 5000
             for i in range(0, len(to_add), chunk_size):
-                chunk = to_add[i: i + chunk_size]
+                chunk = to_add[i : i + chunk_size]
                 stmt = pg_insert(ProteinGOAnnotation.__table__).values(chunk)
-                stmt = stmt.on_conflict_do_nothing(
-                    constraint="uq_pga_set_protein_term_evidence"
-                )
+                stmt = stmt.on_conflict_do_nothing(constraint="uq_pga_set_protein_term_evidence")
                 session.execute(stmt)
 
         return len(to_add), skipped
diff --git a/protea/core/operations/load_ontology_snapshot.py b/protea/core/operations/load_ontology_snapshot.py
index a72abc3..86d6fa2 100644
--- a/protea/core/operations/load_ontology_snapshot.py
+++ b/protea/core/operations/load_ontology_snapshot.py
@@ -43,7 +43,9 @@ class LoadOntologySnapshotOperation:
 
     name = "load_ontology_snapshot"
 
-    def execute(self, session: Session, payload: dict[str, Any], *, emit: EmitFn) -> OperationResult:
+    def execute(
+        self, session: Session, payload: dict[str, Any], *, emit: EmitFn
+    ) -> OperationResult:
         p = LoadOntologySnapshotPayload.model_validate(payload)
 
         emit("load_ontology_snapshot.start", None, {"obo_url": p.obo_url}, "info")
@@ -58,9 +60,13 @@ def execute(self, session: Session, payload: dict[str, Any], *, emit: EmitFn) ->
         existing = session.query(OntologySnapshot).filter_by(obo_version=obo_version).first()
         if existing is not None:
             from sqlalchemy import func as _func
-            rel_count = session.query(_func.count(GOTermRelationship.id)).filter(
-                GOTermRelationship.ontology_snapshot_id == existing.id
-            ).scalar() or 0
+
+            rel_count = (
+                session.query(_func.count(GOTermRelationship.id))
+                .filter(GOTermRelationship.ontology_snapshot_id == existing.id)
+                .scalar()
+                or 0
+            )
 
             if rel_count > 0:
                 emit(
@@ -69,21 +75,27 @@ def execute(self, session: Session, payload: dict[str, Any], *, emit: EmitFn) ->
                     {"ontology_snapshot_id": str(existing.id), "obo_version": obo_version},
                     "info",
                 )
-                return OperationResult(result={
-                    "ontology_snapshot_id": str(existing.id),
-                    "obo_version": obo_version,
-                    "skipped": True,
-                })
+                return OperationResult(
+                    result={
+                        "ontology_snapshot_id": str(existing.id),
+                        "obo_version": obo_version,
+                        "skipped": True,
+                    }
+                )
 
             # Snapshot exists but has no relationships — backfill them
-            emit("load_ontology_snapshot.backfill_relationships", None,
-                 {"ontology_snapshot_id": str(existing.id)}, "info")
+            emit(
+                "load_ontology_snapshot.backfill_relationships",
+                None,
+                {"ontology_snapshot_id": str(existing.id)},
+                "info",
+            )
             terms = self._parse_terms(obo_text)
             go_id_to_db_id = {
                 go_id: db_id
-                for go_id, db_id in session.query(GOTerm.go_id, GOTerm.id).filter(
-                    GOTerm.ontology_snapshot_id == existing.id
-                ).all()
+                for go_id, db_id in session.query(GOTerm.go_id, GOTerm.id)
+                .filter(GOTerm.ontology_snapshot_id == existing.id)
+                .all()
             }
             relationships: list[GOTermRelationship] = []
             for t in terms:
@@ -94,22 +106,30 @@ def execute(self, session: Session, payload: dict[str, Any], *, emit: EmitFn) ->
                     parent_db_id = go_id_to_db_id.get(parent_go_id)
                     if parent_db_id is None:
                         continue
-                    relationships.append(GOTermRelationship(
-                        child_go_term_id=child_db_id,
-                        parent_go_term_id=parent_db_id,
-                        relation_type=rel_type,
-                        ontology_snapshot_id=existing.id,
-                    ))
+                    relationships.append(
+                        GOTermRelationship(
+                            child_go_term_id=child_db_id,
+                            parent_go_term_id=parent_db_id,
+                            relation_type=rel_type,
+                            ontology_snapshot_id=existing.id,
+                        )
+                    )
             session.add_all(relationships)
             session.flush()
-            emit("load_ontology_snapshot.backfill_done", None,
-                 {"relationships_inserted": len(relationships)}, "info")
-            return OperationResult(result={
-                "ontology_snapshot_id": str(existing.id),
-                "obo_version": obo_version,
-                "skipped": False,
-                "relationships_inserted": len(relationships),
-            })
+            emit(
+                "load_ontology_snapshot.backfill_done",
+                None,
+                {"relationships_inserted": len(relationships)},
+                "info",
+            )
+            return OperationResult(
+                result={
+                    "ontology_snapshot_id": str(existing.id),
+                    "obo_version": obo_version,
+                    "skipped": False,
+                    "relationships_inserted": len(relationships),
+                }
+            )
 
         snapshot = OntologySnapshot(obo_url=p.obo_url, obo_version=obo_version)
         session.add(snapshot)
@@ -144,12 +164,14 @@ def execute(self, session: Session, payload: dict[str, Any], *, emit: EmitFn) ->
                 parent_db_id = go_id_to_db_id.get(parent_go_id)
                 if parent_db_id is None:
                     continue
-                relationships.append(GOTermRelationship(
-                    child_go_term_id=child_db_id,
-                    parent_go_term_id=parent_db_id,
-                    relation_type=rel_type,
-                    ontology_snapshot_id=snapshot.id,
-                ))
+                relationships.append(
+                    GOTermRelationship(
+                        child_go_term_id=child_db_id,
+                        parent_go_term_id=parent_db_id,
+                        relation_type=rel_type,
+                        ontology_snapshot_id=snapshot.id,
+                    )
+                )
         session.add_all(relationships)
         session.flush()
 
@@ -166,13 +188,15 @@ def execute(self, session: Session, payload: dict[str, Any], *, emit: EmitFn) ->
             },
             "info",
         )
-        return OperationResult(result={
-            "ontology_snapshot_id": str(snapshot.id),
-            "obo_version": obo_version,
-            "terms_inserted": len(go_terms),
-            "relationships_inserted": len(relationships),
-            "elapsed_seconds": elapsed,
-        })
+        return OperationResult(
+            result={
+                "ontology_snapshot_id": str(snapshot.id),
+                "obo_version": obo_version,
+                "terms_inserted": len(go_terms),
+                "relationships_inserted": len(relationships),
+                "elapsed_seconds": elapsed,
+            }
+        )
 
     def _download(self, p: LoadOntologySnapshotPayload, emit: EmitFn) -> str:
         emit("load_ontology_snapshot.download_start", None, {"url": p.obo_url}, "info")
@@ -196,8 +220,13 @@ def _extract_version(self, obo_text: str) -> str:
 
     # Relationship types to capture from OBO `relationship:` lines
     _RELATION_TYPES = {
-        "part_of", "regulates", "negatively_regulates", "positively_regulates",
-        "occurs_in", "capable_of", "capable_of_part_of",
+        "part_of",
+        "regulates",
+        "negatively_regulates",
+        "positively_regulates",
+        "occurs_in",
+        "capable_of",
+        "capable_of_part_of",
     }
 
     def _parse_terms(self, obo_text: str) -> list[dict[str, Any]]:
@@ -206,14 +235,16 @@ def _parse_terms(self, obo_text: str) -> list[dict[str, Any]]:
 
         def flush() -> None:
             if "go_id" in current:
-                terms.append({
-                    "go_id": current["go_id"],
-                    "name": current.get("name"),
-                    "aspect": self._ASPECT_MAP.get(current.get("namespace", ""), None),
-                    "definition": current.get("definition"),
-                    "is_obsolete": current.get("is_obsolete", False),
-                    "relationships": current.get("relationships", []),
-                })
+                terms.append(
+                    {
+                        "go_id": current["go_id"],
+                        "name": current.get("name"),
+                        "aspect": self._ASPECT_MAP.get(current.get("namespace", ""), None),
+                        "definition": current.get("definition"),
+                        "is_obsolete": current.get("is_obsolete", False),
+                        "relationships": current.get("relationships", []),
+                    }
+                )
             current.clear()
 
         in_term = False
@@ -247,8 +278,12 @@ def flush() -> None:
                 current.setdefault("relationships", []).append(("is_a", parent_go_id))
             elif line.startswith("relationship:"):
                 # relationship: part_of GO:XXXXXXX ! label
-                parts = line[len("relationship:"):].strip().split()
-                if len(parts) >= 2 and parts[0] in self._RELATION_TYPES and parts[1].startswith("GO:"):
+                parts = line[len("relationship:") :].strip().split()
+                if (
+                    len(parts) >= 2
+                    and parts[0] in self._RELATION_TYPES
+                    and parts[1].startswith("GO:")
+                ):
                     current.setdefault("relationships", []).append((parts[0], parts[1]))
 
         flush()
diff --git a/protea/core/operations/load_quickgo_annotations.py b/protea/core/operations/load_quickgo_annotations.py
index 684c3d7..e90b4ce 100644
--- a/protea/core/operations/load_quickgo_annotations.py
+++ b/protea/core/operations/load_quickgo_annotations.py
@@ -35,9 +35,7 @@ class LoadQuickGOAnnotationsPayload(ProteaPayload, frozen=True):
 
     ontology_snapshot_id: str
     source_version: str
-    quickgo_base_url: str = (
-        "https://www.ebi.ac.uk/QuickGO/services/annotation/downloadSearch"
-    )
+    quickgo_base_url: str = "https://www.ebi.ac.uk/QuickGO/services/annotation/downloadSearch"
     gene_product_ids: list[str] | None = None
     use_db_accessions: bool = True
     eco_mapping_url: str | None = None
@@ -74,7 +72,9 @@ class LoadQuickGOAnnotationsOperation:
 
     name = "load_quickgo_annotations"
 
-    def execute(self, session: Session, payload: dict[str, Any], *, emit: EmitFn) -> OperationResult:
+    def execute(
+        self, session: Session, payload: dict[str, Any], *, emit: EmitFn
+    ) -> OperationResult:
         p = LoadQuickGOAnnotationsPayload.model_validate(payload)
 
         snapshot_id = uuid.UUID(p.ontology_snapshot_id)
@@ -82,10 +82,15 @@ def execute(self, session: Session, payload: dict[str, Any], *, emit: EmitFn) ->
             raise ValueError(f"OntologySnapshot {p.ontology_snapshot_id} not found")
 
         t0 = time.perf_counter()
-        emit("load_quickgo_annotations.start", None, {
-            "ontology_snapshot_id": p.ontology_snapshot_id,
-            "source_version": p.source_version,
-        }, "info")
+        emit(
+            "load_quickgo_annotations.start",
+            None,
+            {
+                "ontology_snapshot_id": p.ontology_snapshot_id,
+                "source_version": p.source_version,
+            },
+            "info",
+        )
 
         canonical_accessions, protein_accessions = self._load_accessions(session, emit)
         if not canonical_accessions:
@@ -108,8 +113,12 @@ def execute(self, session: Session, payload: dict[str, Any], *, emit: EmitFn) ->
         session.add(annotation_set)
         session.flush()
 
-        emit("load_quickgo_annotations.annotation_set_created", None,
-             {"annotation_set_id": str(annotation_set.id)}, "info")
+        emit(
+            "load_quickgo_annotations.annotation_set_created",
+            None,
+            {"annotation_set_id": str(annotation_set.id)},
+            "info",
+        )
 
         total_lines = 0
         total_inserted = 0
@@ -121,8 +130,12 @@ def execute(self, session: Session, payload: dict[str, Any], *, emit: EmitFn) ->
             total_lines += 1
 
             if p.total_limit is not None and total_inserted >= p.total_limit:
-                emit("load_quickgo_annotations.limit_reached", None,
-                     {"total_limit": p.total_limit}, "warning")
+                emit(
+                    "load_quickgo_annotations.limit_reached",
+                    None,
+                    {"total_limit": p.total_limit},
+                    "warning",
+                )
                 break
 
             buffer.append(record)
@@ -130,19 +143,28 @@ def execute(self, session: Session, payload: dict[str, Any], *, emit: EmitFn) ->
             if len(buffer) >= p.page_size:
                 pages += 1
                 inserted, skipped = self._store_buffer(
-                    session, buffer, annotation_set.id,
-                    protein_accessions, go_term_map, eco_map,
+                    session,
+                    buffer,
+                    annotation_set.id,
+                    protein_accessions,
+                    go_term_map,
+                    eco_map,
                 )
                 total_inserted += inserted
                 total_skipped += skipped
                 buffer.clear()
 
-                emit("load_quickgo_annotations.page_done", None, {
-                    "page": pages,
-                    "total_lines": total_lines,
-                    "total_inserted": total_inserted,
-                    "total_skipped": total_skipped,
-                }, "info")
+                emit(
+                    "load_quickgo_annotations.page_done",
+                    None,
+                    {
+                        "page": pages,
+                        "total_lines": total_lines,
+                        "total_inserted": total_inserted,
+                        "total_skipped": total_skipped,
+                    },
+                    "info",
+                )
 
                 if p.commit_every_page:
                     session.commit()
@@ -150,8 +172,12 @@ def execute(self, session: Session, payload: dict[str, Any], *, emit: EmitFn) ->
         if buffer:
             pages += 1
             inserted, skipped = self._store_buffer(
-                session, buffer, annotation_set.id,
-                protein_accessions, go_term_map, eco_map,
+                session,
+                buffer,
+                annotation_set.id,
+                protein_accessions,
+                go_term_map,
+                eco_map,
             )
             total_inserted += inserted
             total_skipped += skipped
@@ -180,9 +206,15 @@ def _load_accessions(self, session: Session, emit: EmitFn) -> tuple[set[str], se
         emit("load_quickgo_annotations.load_accessions_start", None, {}, "info")
         canonical_accessions = set(session.scalars(select(distinct(Protein.canonical_accession))))
         protein_accessions = set(session.scalars(select(distinct(Protein.accession))))
-        emit("load_quickgo_annotations.load_accessions_done", None,
-             {"canonical_accessions": len(canonical_accessions),
-              "protein_accessions": len(protein_accessions)}, "info")
+        emit(
+            "load_quickgo_annotations.load_accessions_done",
+            None,
+            {
+                "canonical_accessions": len(canonical_accessions),
+                "protein_accessions": len(protein_accessions),
+            },
+            "info",
+        )
         return canonical_accessions, protein_accessions
 
     def _load_go_term_map(
@@ -195,18 +227,16 @@ def _load_go_term_map(
             .all()
         )
         mapping = {go_id: term_id for go_id, term_id in rows}
-        emit("load_quickgo_annotations.load_go_terms_done", None,
-             {"go_terms": len(mapping)}, "info")
+        emit(
+            "load_quickgo_annotations.load_go_terms_done", None, {"go_terms": len(mapping)}, "info"
+        )
         return mapping
 
-    def _load_eco_mapping(
-        self, p: LoadQuickGOAnnotationsPayload, emit: EmitFn
-    ) -> dict[str, str]:
+    def _load_eco_mapping(self, p: LoadQuickGOAnnotationsPayload, emit: EmitFn) -> dict[str, str]:
         """Download and parse gaf-eco-mapping-derived.txt → {ECO:XXXXXXX: CODE}."""
         if not p.eco_mapping_url:
             return {}
-        emit("load_quickgo_annotations.eco_mapping_start", None,
-             {"url": p.eco_mapping_url}, "info")
+        emit("load_quickgo_annotations.eco_mapping_start", None, {"url": p.eco_mapping_url}, "info")
         resp = requests.get(p.eco_mapping_url, timeout=60)
         resp.raise_for_status()
         mapping: dict[str, str] = {}
@@ -214,34 +244,45 @@ def _load_eco_mapping(
             parts = line.strip().split()
             if len(parts) >= 2 and parts[0].startswith("ECO:"):
                 mapping[parts[0]] = parts[1]
-        emit("load_quickgo_annotations.eco_mapping_done", None,
-             {"entries": len(mapping)}, "info")
+        emit("load_quickgo_annotations.eco_mapping_done", None, {"entries": len(mapping)}, "info")
         return mapping
 
     def _stream_quickgo(
-        self, p: LoadQuickGOAnnotationsPayload, emit: EmitFn,
+        self,
+        p: LoadQuickGOAnnotationsPayload,
+        emit: EmitFn,
         gene_product_ids: list[str] | None = None,
     ) -> Iterator[dict[str, str]]:
         effective_ids = gene_product_ids or p.gene_product_ids
 
         # If no ID filter, do a single unbatched request
         if not effective_ids:
-            yield from self._fetch_quickgo_page(p, emit, gp_ids=None, batch_index=0, total_batches=1)
+            yield from self._fetch_quickgo_page(
+                p, emit, gp_ids=None, batch_index=0, total_batches=1
+            )
             return
 
         # Batch accessions to avoid URL length limits (QuickGO returns 400 for very long URLs)
         batches = [
-            effective_ids[i: i + p.gene_product_batch_size]
+            effective_ids[i : i + p.gene_product_batch_size]
             for i in range(0, len(effective_ids), p.gene_product_batch_size)
         ]
         total_batches = len(batches)
-        emit("load_quickgo_annotations.batching", None,
-             {"total_accessions": len(effective_ids), "total_batches": total_batches,
-              "batch_size": p.gene_product_batch_size}, "info")
+        emit(
+            "load_quickgo_annotations.batching",
+            None,
+            {
+                "total_accessions": len(effective_ids),
+                "total_batches": total_batches,
+                "batch_size": p.gene_product_batch_size,
+            },
+            "info",
+        )
 
         for batch_index, batch in enumerate(batches):
-            yield from self._fetch_quickgo_page(p, emit, gp_ids=batch,
-                                                batch_index=batch_index, total_batches=total_batches)
+            yield from self._fetch_quickgo_page(
+                p, emit, gp_ids=batch, batch_index=batch_index, total_batches=total_batches
+            )
 
     def _fetch_quickgo_page(
         self,
@@ -259,11 +300,18 @@ def _fetch_quickgo_page(
             "Accept": "text/tsv",
             "User-Agent": "PROTEA/load_quickgo_annotations",
         }
-        emit("load_quickgo_annotations.download_start", None,
-             {"batch": batch_index + 1, "of": total_batches,
-              "accessions_in_batch": len(gp_ids) if gp_ids else "all",
-              "_progress_current": batch_index + 1,
-              "_progress_total": total_batches}, "info")
+        emit(
+            "load_quickgo_annotations.download_start",
+            None,
+            {
+                "batch": batch_index + 1,
+                "of": total_batches,
+                "accessions_in_batch": len(gp_ids) if gp_ids else "all",
+                "_progress_current": batch_index + 1,
+                "_progress_total": total_batches,
+            },
+            "info",
+        )
 
         resp = requests.get(
             p.quickgo_base_url,
@@ -316,29 +364,30 @@ def _store_buffer(
                 continue
 
             eco_id = row.get("ECO ID", "").strip() or None
-            evidence_code = (eco_map.get(eco_id, eco_id) if eco_id else None)
-
-            to_add.append({
-                "annotation_set_id": annotation_set_id,
-                "protein_accession": accession,
-                "go_term_id": go_term_id,
-                "qualifier": row.get("QUALIFIER", "").strip() or None,
-                "evidence_code": evidence_code,
-                "assigned_by": row.get("ASSIGNED BY", "").strip() or None,
-                "db_reference": row.get("REFERENCE", "").strip() or None,
-                "with_from": row.get("WITH/FROM", "").strip() or None,
-                "annotation_date": row.get("DATE", "").strip() or None,
-            })
+            evidence_code = eco_map.get(eco_id, eco_id) if eco_id else None
+
+            to_add.append(
+                {
+                    "annotation_set_id": annotation_set_id,
+                    "protein_accession": accession,
+                    "go_term_id": go_term_id,
+                    "qualifier": row.get("QUALIFIER", "").strip() or None,
+                    "evidence_code": evidence_code,
+                    "assigned_by": row.get("ASSIGNED BY", "").strip() or None,
+                    "db_reference": row.get("REFERENCE", "").strip() or None,
+                    "with_from": row.get("WITH/FROM", "").strip() or None,
+                    "annotation_date": row.get("DATE", "").strip() or None,
+                }
+            )
 
         if to_add:
             from sqlalchemy.dialects.postgresql import insert as pg_insert
+
             chunk_size = 5000
             for i in range(0, len(to_add), chunk_size):
-                chunk = to_add[i: i + chunk_size]
+                chunk = to_add[i : i + chunk_size]
                 stmt = pg_insert(ProteinGOAnnotation.__table__).values(chunk)
-                stmt = stmt.on_conflict_do_nothing(
-                    constraint="uq_pga_set_protein_term_evidence"
-                )
+                stmt = stmt.on_conflict_do_nothing(constraint="uq_pga_set_protein_term_evidence")
                 session.execute(stmt)
 
         return len(to_add), skipped
diff --git a/protea/core/operations/ping.py b/protea/core/operations/ping.py
index 8215da9..560bbde 100644
--- a/protea/core/operations/ping.py
+++ b/protea/core/operations/ping.py
@@ -11,7 +11,9 @@
 class PingOperation(Operation):
     name = "ping"
 
-    def execute(self, session: Session, payload: dict[str, Any], *, emit: EmitFn) -> OperationResult:
+    def execute(
+        self, session: Session, payload: dict[str, Any], *, emit: EmitFn
+    ) -> OperationResult:
         emit("ping.start", "Ping received", {"payload_keys": list(payload.keys())}, "info")
         emit("ping.done", "Ping finished", {}, "info")
         return OperationResult(result={"ok": True})
diff --git a/protea/core/operations/predict_go_terms.py b/protea/core/operations/predict_go_terms.py
index 6914f51..9e01430 100644
--- a/protea/core/operations/predict_go_terms.py
+++ b/protea/core/operations/predict_go_terms.py
@@ -1,7 +1,9 @@
 from __future__ import annotations
 
+import os
 import time
 import uuid
+from pathlib import Path
 from typing import Annotated, Any
 from uuid import UUID
 
@@ -16,6 +18,7 @@
 from protea.core.knn_search import search_knn
 from protea.core.utils import utcnow
 from protea.infrastructure.orm.models.annotation.annotation_set import AnnotationSet
+from protea.infrastructure.orm.models.annotation.go_term import GOTerm
 from protea.infrastructure.orm.models.annotation.ontology_snapshot import OntologySnapshot
 from protea.infrastructure.orm.models.annotation.protein_go_annotation import ProteinGOAnnotation
 from protea.infrastructure.orm.models.embedding.embedding_config import EmbeddingConfig
@@ -31,7 +34,14 @@
 
 _ANNOTATION_CHUNK_SIZE = 10_000
 _BATCH_QUEUE = "protea.predictions.batch"
-_WRITE_QUEUE  = "protea.predictions.write"
+_WRITE_QUEUE = "protea.predictions.write"
+# Rows fetched per round-trip when streaming reference embeddings from PostgreSQL.
+# At 1280 dims × 2 bytes (float16) × 2000 rows = ~5 MB per chunk — keeps Python
+# object pressure negligible while amortising cursor round-trips.
+_STREAM_CHUNK_SIZE = 2_000
+
+# GO aspect single-character codes used in GOTerm.aspect
+_ASPECTS = ("P", "F", "C")  # biological_process, molecular_function, cellular_component
 
 # ---------------------------------------------------------------------------
 # Process-level reference cache
@@ -46,11 +56,184 @@
 _REF_CACHE: dict[tuple[str, str], dict[str, Any]] = {}
 _REF_CACHE_MAX = 1
 
+# ---------------------------------------------------------------------------
+# Disk cache for reference embeddings
+# Survives worker restarts — avoids re-fetching GB of vectors from PostgreSQL.
+# Files: {cache_dir}/{emb_config_id}__{ann_set_id}_embeddings.npy
+#         {cache_dir}/{emb_config_id}__{ann_set_id}_accessions.npy
+# Invalidation: annotation sets are immutable once loaded, so the cache is
+# valid as long as the file exists. Delete files manually to force a reload.
+# ---------------------------------------------------------------------------
+_DISK_CACHE_DIR = Path(os.environ.get("PROTEA_REF_CACHE_DIR", "data/ref_cache"))
+
+
+def _disk_cache_paths(
+    embedding_config_id: uuid.UUID,
+    annotation_set_id: uuid.UUID,
+) -> tuple[Path, Path]:
+    """Return (embeddings_path, accessions_path) for the unified reference cache."""
+    key = f"{embedding_config_id}__{annotation_set_id}"
+    return (
+        _DISK_CACHE_DIR / f"{key}_embeddings.npy",
+        _DISK_CACHE_DIR / f"{key}_accessions.npy",
+    )
+
+
+def _aspect_index_path(
+    embedding_config_id: uuid.UUID,
+    annotation_set_id: uuid.UUID,
+    aspect: str,
+) -> Path:
+    """Return the path for the per-aspect index array (int32 indices into the unified cache)."""
+    key = f"{embedding_config_id}__{annotation_set_id}"
+    return _DISK_CACHE_DIR / f"{key}__{aspect}_indices.npy"
+
+
+def _anno_disk_cache_paths(
+    embedding_config_id: uuid.UUID,
+    annotation_set_id: uuid.UUID,
+    aspect: str,
+) -> tuple[Path, Path, Path, Path]:
+    """Return (gtids, quals, ecodes, offsets) paths for the annotation CSR cache."""
+    key = f"{embedding_config_id}__{annotation_set_id}__{aspect}"
+    base = _DISK_CACHE_DIR
+    return (
+        base / f"{key}_anno_gtids.npy",
+        base / f"{key}_anno_quals.npy",
+        base / f"{key}_anno_ecodes.npy",
+        base / f"{key}_anno_offsets.npy",
+    )
+
+
+def _build_anno_csr(
+    accessions: list[str],
+    go_map: dict[str, list[dict[str, Any]]],
+) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
+    """Build a CSR-style annotation structure for the given accession list.
+
+    Returns (go_term_ids, qualifiers, evidence_codes, offsets) where
+    annotations for accessions[i] are at indices offsets[i]:offsets[i+1].
+    """
+    all_gtids: list[int] = []
+    all_quals: list[Any] = []
+    all_ecodes: list[Any] = []
+    offsets: list[int] = [0]
+    for acc in accessions:
+        for ann in go_map.get(acc, []):
+            all_gtids.append(ann["go_term_id"])
+            all_quals.append(ann.get("qualifier"))
+            all_ecodes.append(ann.get("evidence_code"))
+        offsets.append(len(all_gtids))
+    return (
+        np.array(all_gtids, dtype=np.int32),
+        np.array(all_quals, dtype=object),
+        np.array(all_ecodes, dtype=object),
+        np.array(offsets, dtype=np.int32),
+    )
+
+
+def _load_anno_csr_from_disk(
+    embedding_config_id: uuid.UUID,
+    annotation_set_id: uuid.UUID,
+    aspect: str,
+) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray] | None:
+    """Load annotation CSR arrays from disk. Returns None on miss or error."""
+    gtids_p, quals_p, ecodes_p, offsets_p = _anno_disk_cache_paths(
+        embedding_config_id, annotation_set_id, aspect
+    )
+    if not all(p.exists() for p in (gtids_p, quals_p, ecodes_p, offsets_p)):
+        return None
+    try:
+        return (
+            np.load(gtids_p),
+            np.load(quals_p, allow_pickle=True),
+            np.load(ecodes_p, allow_pickle=True),
+            np.load(offsets_p),
+        )
+    except Exception:
+        return None
+
+
+def _save_anno_csr_to_disk(
+    embedding_config_id: uuid.UUID,
+    annotation_set_id: uuid.UUID,
+    aspect: str,
+    gtids: np.ndarray,
+    quals: np.ndarray,
+    ecodes: np.ndarray,
+    offsets: np.ndarray,
+) -> None:
+    gtids_p, quals_p, ecodes_p, offsets_p = _anno_disk_cache_paths(
+        embedding_config_id, annotation_set_id, aspect
+    )
+    gtids_p.parent.mkdir(parents=True, exist_ok=True)
+    np.save(gtids_p, gtids)
+    np.save(quals_p, quals)
+    np.save(ecodes_p, ecodes)
+    np.save(offsets_p, offsets)
+
+
+def _csr_lookup(
+    query_accessions: set[str],
+    accessions: list[str],
+    acc_to_anno_idx: dict[str, int],
+    gtids: np.ndarray,
+    quals: np.ndarray,
+    ecodes: np.ndarray,
+    offsets: np.ndarray,
+) -> dict[str, list[dict[str, Any]]]:
+    """Return a go_map for query_accessions using the preloaded CSR annotation cache."""
+    go_map: dict[str, list[dict[str, Any]]] = {}
+    for acc in query_accessions:
+        idx = acc_to_anno_idx.get(acc)
+        if idx is None:
+            continue
+        start, end = int(offsets[idx]), int(offsets[idx + 1])
+        if start >= end:
+            continue
+        go_map[acc] = [
+            {
+                "go_term_id": int(gtids[j]),
+                "qualifier": quals[j] if quals[j] is not None else None,
+                "evidence_code": ecodes[j] if ecodes[j] is not None else None,
+            }
+            for j in range(start, end)
+        ]
+    return go_map
+
+
+def _load_from_disk_cache(
+    embedding_config_id: uuid.UUID,
+    annotation_set_id: uuid.UUID,
+) -> dict[str, Any] | None:
+    emb_path, acc_path = _disk_cache_paths(embedding_config_id, annotation_set_id)
+    if not emb_path.exists() or not acc_path.exists():
+        return None
+    try:
+        embeddings = np.load(emb_path)
+        accessions = list(np.load(acc_path))
+        return {"accessions": accessions, "embeddings": embeddings}
+    except Exception:
+        return None
+
+
+def _save_to_disk_cache(
+    embedding_config_id: uuid.UUID,
+    annotation_set_id: uuid.UUID,
+    accessions: list[str],
+    embeddings: np.ndarray,
+) -> None:
+    emb_path, acc_path = _disk_cache_paths(embedding_config_id, annotation_set_id)
+    emb_path.parent.mkdir(parents=True, exist_ok=True)
+    np.save(emb_path, embeddings)
+    np.save(acc_path, np.array(accessions))
+
 
 # ---------------------------------------------------------------------------
 # Payloads
 # ---------------------------------------------------------------------------
 
+
 class PredictGOTermsPayload(ProteaPayload, frozen=True):
     """Payload for the predict_go_terms coordinator job."""
 
@@ -76,7 +259,19 @@ class PredictGOTermsPayload(ProteaPayload, frozen=True):
     compute_alignments: bool = False
     compute_taxonomy: bool = False
 
-    @field_validator("embedding_config_id", "annotation_set_id", "ontology_snapshot_id", mode="before")
+    # Per-aspect KNN indices (opt-in)
+    # When True, three separate KNN indices are built — one per GO aspect (P/F/C).
+    # Each index contains only reference proteins annotated in that aspect, and only
+    # annotations of that aspect are transferred from matched neighbors.
+    # This guarantees that every query protein receives BPO, MFO, and CCO candidates
+    # even if its nearest neighbors in a unified index happen to be annotated only in
+    # one or two aspects (a common cause of BPO recall ceilings).
+    # Memory cost: 3× the reference embedding array; search time: 3 KNN calls per batch.
+    aspect_separated_knn: bool = True
+
+    @field_validator(
+        "embedding_config_id", "annotation_set_id", "ontology_snapshot_id", mode="before"
+    )
     @classmethod
     def must_be_non_empty(cls, v: str) -> str:
         if not isinstance(v, str) or not v.strip():
@@ -104,6 +299,7 @@ class PredictGOTermsBatchPayload(ProteaPayload, frozen=True):
     faiss_hnsw_ef_search: int = 64
     compute_alignments: bool = False
     compute_taxonomy: bool = False
+    aspect_separated_knn: bool = True
 
 
 class StorePredictionsPayload(ProteaPayload, frozen=True):
@@ -118,6 +314,7 @@ class StorePredictionsPayload(ProteaPayload, frozen=True):
 # Coordinator
 # ---------------------------------------------------------------------------
 
+
 class PredictGOTermsOperation:
     """Coordinator: validates, creates PredictionSet, dispatches N batch messages.
 
@@ -139,8 +336,8 @@ def execute(
         p = PredictGOTermsPayload.model_validate(payload)
         parent_job_id = UUID(payload["_job_id"])
 
-        embedding_config_id  = uuid.UUID(p.embedding_config_id)
-        annotation_set_id    = uuid.UUID(p.annotation_set_id)
+        embedding_config_id = uuid.UUID(p.embedding_config_id)
+        annotation_set_id = uuid.UUID(p.annotation_set_id)
         ontology_snapshot_id = uuid.UUID(p.ontology_snapshot_id)
 
         config = session.get(EmbeddingConfig, embedding_config_id)
@@ -151,17 +348,20 @@ def execute(
         if session.get(OntologySnapshot, ontology_snapshot_id) is None:
             raise ValueError(f"OntologySnapshot {p.ontology_snapshot_id} not found")
 
-        emit("predict_go_terms.start", None, {
-            "embedding_config_id": p.embedding_config_id,
-            "model_name": config.model_name,
-            "annotation_set_id": p.annotation_set_id,
-            "limit_per_entry": p.limit_per_entry,
-            "search_backend": p.search_backend,
-        }, "info")
-
-        query_accessions = self._load_query_accessions(
-            session, p, embedding_config_id, emit
+        emit(
+            "predict_go_terms.start",
+            None,
+            {
+                "embedding_config_id": p.embedding_config_id,
+                "model_name": config.model_name,
+                "annotation_set_id": p.annotation_set_id,
+                "limit_per_entry": p.limit_per_entry,
+                "search_backend": p.search_backend,
+            },
+            "info",
         )
+
+        query_accessions = self._load_query_accessions(session, p, embedding_config_id, emit)
         if not query_accessions:
             emit("predict_go_terms.no_queries", None, {}, "warning")
             return OperationResult(result={"batches": 0, "queries": 0})
@@ -179,42 +379,53 @@ def execute(
         session.flush()
 
         batches = [
-            query_accessions[i: i + p.batch_size]
+            query_accessions[i : i + p.batch_size]
             for i in range(0, len(query_accessions), p.batch_size)
         ]
         n_batches = len(batches)
 
-        emit("predict_go_terms.dispatching", None, {
-            "queries": len(query_accessions),
-            "batches": n_batches,
-            "prediction_set_id": str(prediction_set.id),
-        }, "info")
+        emit(
+            "predict_go_terms.dispatching",
+            None,
+            {
+                "queries": len(query_accessions),
+                "batches": n_batches,
+                "prediction_set_id": str(prediction_set.id),
+            },
+            "info",
+        )
 
         operations: list[tuple[str, dict[str, Any]]] = []
         for batch_accs in batches:
-            operations.append((_BATCH_QUEUE, {
-                "operation": "predict_go_terms_batch",
-                "job_id":    str(parent_job_id),
-                "payload": {
-                    "embedding_config_id":  p.embedding_config_id,
-                    "annotation_set_id":    p.annotation_set_id,
-                    "prediction_set_id":    str(prediction_set.id),
-                    "parent_job_id":        str(parent_job_id),
-                    "query_accessions":     batch_accs,
-                    "query_set_id":         p.query_set_id,
-                    "limit_per_entry":      p.limit_per_entry,
-                    "distance_threshold":   p.distance_threshold,
-                    "search_backend":       p.search_backend,
-                    "metric":               p.metric,
-                    "faiss_index_type":     p.faiss_index_type,
-                    "faiss_nlist":          p.faiss_nlist,
-                    "faiss_nprobe":         p.faiss_nprobe,
-                    "faiss_hnsw_m":         p.faiss_hnsw_m,
-                    "faiss_hnsw_ef_search": p.faiss_hnsw_ef_search,
-                    "compute_alignments":   p.compute_alignments,
-                    "compute_taxonomy":     p.compute_taxonomy,
-                },
-            }))
+            operations.append(
+                (
+                    _BATCH_QUEUE,
+                    {
+                        "operation": "predict_go_terms_batch",
+                        "job_id": str(parent_job_id),
+                        "payload": {
+                            "embedding_config_id": p.embedding_config_id,
+                            "annotation_set_id": p.annotation_set_id,
+                            "prediction_set_id": str(prediction_set.id),
+                            "parent_job_id": str(parent_job_id),
+                            "query_accessions": batch_accs,
+                            "query_set_id": p.query_set_id,
+                            "limit_per_entry": p.limit_per_entry,
+                            "distance_threshold": p.distance_threshold,
+                            "search_backend": p.search_backend,
+                            "metric": p.metric,
+                            "faiss_index_type": p.faiss_index_type,
+                            "faiss_nlist": p.faiss_nlist,
+                            "faiss_nprobe": p.faiss_nprobe,
+                            "faiss_hnsw_m": p.faiss_hnsw_m,
+                            "faiss_hnsw_ef_search": p.faiss_hnsw_ef_search,
+                            "compute_alignments": p.compute_alignments,
+                            "compute_taxonomy": p.compute_taxonomy,
+                            "aspect_separated_knn": p.aspect_separated_knn,
+                        },
+                    },
+                )
+            )
 
         return OperationResult(
             result={
@@ -267,8 +478,7 @@ def _load_query_accessions(
             rows = q.all()
 
         accessions = [r[0] for r in rows]
-        emit("predict_go_terms.load_queries_done", None,
-             {"queries": len(accessions)}, "info")
+        emit("predict_go_terms.load_queries_done", None, {"queries": len(accessions)}, "info")
         return accessions
 
 
@@ -276,6 +486,7 @@ def _load_query_accessions(
 # Batch worker
 # ---------------------------------------------------------------------------
 
+
 class PredictGOTermsBatchOperation:
     """CPU batch worker: KNN search + GO annotation transfer for one query chunk.
 
@@ -292,38 +503,49 @@ def execute(
         self, session: Session, payload: dict[str, Any], *, emit: EmitFn
     ) -> OperationResult:
         p = PredictGOTermsBatchPayload.model_validate(payload)
-        parent_job_id     = UUID(p.parent_job_id)
+        parent_job_id = UUID(p.parent_job_id)
         prediction_set_id = uuid.UUID(p.prediction_set_id)
         embedding_config_id = uuid.UUID(p.embedding_config_id)
-        annotation_set_id   = uuid.UUID(p.annotation_set_id)
+        annotation_set_id = uuid.UUID(p.annotation_set_id)
 
         # Skip if parent was cancelled/failed
         parent = session.get(Job, parent_job_id)
         if parent is not None and parent.status in (JobStatus.CANCELLED, JobStatus.FAILED):
-            emit("predict_go_terms_batch.skipped", None,
-                 {"parent_job_id": str(parent_job_id)}, "warning")
+            emit(
+                "predict_go_terms_batch.skipped",
+                None,
+                {"parent_job_id": str(parent_job_id)},
+                "warning",
+            )
             return OperationResult(result={"skipped": True})
 
-        # --- reference cache (load once per process per config+annotation_set) ---
-        cache_key = (p.embedding_config_id, p.annotation_set_id)
+        # --- reference cache (load once per process per config+annotation_set+mode) ---
+        # The cache key includes aspect_separated_knn so that switching modes on the
+        # same worker process does not serve stale data from a previous run.
+        cache_key = (p.embedding_config_id, p.annotation_set_id, p.aspect_separated_knn)
         if cache_key not in _REF_CACHE:
             # Evict oldest entry when cache is full to free numpy arrays from memory.
             if len(_REF_CACHE) >= _REF_CACHE_MAX:
                 evict_key = next(iter(_REF_CACHE))
                 del _REF_CACHE[evict_key]
-            emit("predict_go_terms_batch.loading_reference", None, {
-                "embedding_config_id": p.embedding_config_id,
-                "annotation_set_id": p.annotation_set_id,
-            }, "info")
-            _REF_CACHE[cache_key] = self._load_reference_data(
-                session, embedding_config_id, annotation_set_id, emit
+            emit(
+                "predict_go_terms_batch.loading_reference",
+                None,
+                {
+                    "embedding_config_id": p.embedding_config_id,
+                    "annotation_set_id": p.annotation_set_id,
+                    "aspect_separated_knn": p.aspect_separated_knn,
+                },
+                "info",
             )
-
-        ref_data = _REF_CACHE[cache_key]
-
-        if not ref_data["embeddings"].size:
-            emit("predict_go_terms_batch.no_references", None, {}, "warning")
-            return OperationResult(result={"predictions": 0})
+            if p.aspect_separated_knn:
+                _REF_CACHE[cache_key] = self._load_reference_data_per_aspect(
+                    session, embedding_config_id, annotation_set_id, emit
+                )
+            else:
+                _REF_CACHE[cache_key] = self._load_reference_data(
+                    session, embedding_config_id, annotation_set_id, emit
+                )
 
         # --- query embeddings for this batch ---
         query_embeddings, valid_accessions = self._load_query_embeddings(
@@ -332,78 +554,109 @@ def execute(
         if not query_embeddings.size:
             return OperationResult(result={"predictions": 0})
 
-        # --- KNN: convert float16 cache → float32 for search ---
         t0 = time.perf_counter()
-        ref_embeddings_f32 = ref_data["embeddings"].astype(np.float32)
-        neighbors = search_knn(
-            query_embeddings,
-            ref_embeddings_f32,
-            ref_data["accessions"],
-            k=p.limit_per_entry,
-            distance_threshold=p.distance_threshold,
-            backend=p.search_backend,
-            metric=p.metric,
-            faiss_index_type=p.faiss_index_type,
-            faiss_nlist=p.faiss_nlist,
-            faiss_nprobe=p.faiss_nprobe,
-            faiss_hnsw_m=p.faiss_hnsw_m,
-            faiss_hnsw_ef_search=p.faiss_hnsw_ef_search,
-        )
 
-        # --- lazy GO annotation load: only for neighbors actually found ---
-        unique_neighbors: set[str] = set()
-        for top_refs in neighbors:
-            for ref_acc, _ in top_refs:
-                unique_neighbors.add(ref_acc)
-        go_map = self._load_annotations_for(session, annotation_set_id, unique_neighbors)
-
-        # --- feature engineering sequences / taxonomy (opt-in) ---
-        ref_sequences: dict[str, str] = {}
-        query_sequences: dict[str, str] = {}
-        ref_tax_ids: dict[str, int | None] = {}
-        query_tax_ids: dict[str, int | None] = {}
+        if p.aspect_separated_knn:
+            prediction_dicts = self._run_aspect_separated_knn(
+                session,
+                valid_accessions,
+                query_embeddings,
+                _REF_CACHE[cache_key],
+                annotation_set_id,
+                prediction_set_id,
+                p,
+            )
+        else:
+            ref_data = _REF_CACHE[cache_key]
+            if not ref_data["embeddings"].size:
+                emit("predict_go_terms_batch.no_references", None, {}, "warning")
+                return OperationResult(result={"predictions": 0})
 
-        if p.compute_alignments:
-            ref_sequences   = self._load_sequences_for_proteins(session, unique_neighbors)
-            query_sequences = self._load_sequences_for_queries(session, p, valid_accessions)
+            # --- KNN: convert float16 cache → float32 for search ---
+            ref_embeddings_f32 = ref_data["embeddings"].astype(np.float32)
+            neighbors = search_knn(
+                query_embeddings,
+                ref_embeddings_f32,
+                ref_data["accessions"],
+                k=p.limit_per_entry,
+                distance_threshold=p.distance_threshold,
+                backend=p.search_backend,
+                metric=p.metric,
+                faiss_index_type=p.faiss_index_type,
+                faiss_nlist=p.faiss_nlist,
+                faiss_nprobe=p.faiss_nprobe,
+                faiss_hnsw_m=p.faiss_hnsw_m,
+                faiss_hnsw_ef_search=p.faiss_hnsw_ef_search,
+            )
 
-        if p.compute_taxonomy:
-            ref_tax_ids   = self._load_taxonomy_ids_for_proteins(session, unique_neighbors)
-            query_tax_ids = self._load_taxonomy_ids_for_queries(session, p, valid_accessions)
+            # --- lazy GO annotation load: only for neighbors actually found ---
+            unique_neighbors: set[str] = set()
+            for top_refs in neighbors:
+                for ref_acc, _ in top_refs:
+                    unique_neighbors.add(ref_acc)
+            go_map = self._load_annotations_for(session, annotation_set_id, unique_neighbors)
+
+            # --- feature engineering sequences / taxonomy (opt-in) ---
+            ref_sequences: dict[str, str] = {}
+            query_sequences: dict[str, str] = {}
+            ref_tax_ids: dict[str, int | None] = {}
+            query_tax_ids: dict[str, int | None] = {}
+
+            if p.compute_alignments:
+                ref_sequences = self._load_sequences_for_proteins(session, unique_neighbors)
+                query_sequences = self._load_sequences_for_queries(session, p, valid_accessions)
+
+            if p.compute_taxonomy:
+                ref_tax_ids = self._load_taxonomy_ids_for_proteins(session, unique_neighbors)
+                query_tax_ids = self._load_taxonomy_ids_for_queries(session, p, valid_accessions)
+
+            ref_data_with_annotations = {
+                "accessions": ref_data["accessions"],
+                "embeddings": ref_embeddings_f32,
+                "go_map": go_map,
+            }
+            prediction_dicts = self._predict_batch(
+                valid_accessions,
+                query_embeddings,
+                ref_data_with_annotations,
+                prediction_set_id,
+                p,
+                neighbors=neighbors,
+                ref_sequences=ref_sequences,
+                query_sequences=query_sequences,
+                ref_tax_ids=ref_tax_ids,
+                query_tax_ids=query_tax_ids,
+            )
 
-        # --- assemble ref_data with lazily-loaded annotations for _predict_batch ---
-        ref_data_with_annotations = {
-            "accessions": ref_data["accessions"],
-            "embeddings": ref_embeddings_f32,
-            "go_map":     go_map,
-        }
-        prediction_dicts = self._predict_batch(
-            valid_accessions, query_embeddings, ref_data_with_annotations, prediction_set_id, p,
-            neighbors=neighbors,
-            ref_sequences=ref_sequences,
-            query_sequences=query_sequences,
-            ref_tax_ids=ref_tax_ids,
-            query_tax_ids=query_tax_ids,
-        )
         elapsed = time.perf_counter() - t0
 
-        emit("predict_go_terms_batch.done", None, {
-            "queries": len(valid_accessions),
-            "predictions": len(prediction_dicts),
-            "elapsed_seconds": elapsed,
-        }, "info")
+        emit(
+            "predict_go_terms_batch.done",
+            None,
+            {
+                "queries": len(valid_accessions),
+                "predictions": len(prediction_dicts),
+                "elapsed_seconds": elapsed,
+            },
+            "info",
+        )
 
         return OperationResult(
             result={"predictions": len(prediction_dicts)},
-            publish_operations=[(_WRITE_QUEUE, {
-                "operation": "store_predictions",
-                "job_id":    str(parent_job_id),
-                "payload": {
-                    "parent_job_id":     str(parent_job_id),
-                    "prediction_set_id": str(prediction_set_id),
-                    "predictions":       prediction_dicts,
-                },
-            })],
+            publish_operations=[
+                (
+                    _WRITE_QUEUE,
+                    {
+                        "operation": "store_predictions",
+                        "job_id": str(parent_job_id),
+                        "payload": {
+                            "parent_job_id": str(parent_job_id),
+                            "prediction_set_id": str(prediction_set_id),
+                            "predictions": prediction_dicts,
+                        },
+                    },
+                )
+            ],
         )
 
     # ── helpers ──────────────────────────────────────────────────────────────
@@ -417,6 +670,9 @@ def _load_reference_data(
     ) -> dict[str, Any]:
         """Load reference accessions and embeddings (float16) into the process cache.
 
+        Checks the disk cache first (survives worker restarts). On miss, fetches
+        from PostgreSQL and writes the result to disk for future restarts.
+
         GO annotations are NOT loaded here — they are fetched lazily per batch
         for only the unique neighbors found by KNN, saving several GB of RAM.
         Embeddings are stored as float16 (half the memory of float32); they are
@@ -424,72 +680,463 @@ def _load_reference_data(
         """
         emit("predict_go_terms_batch.load_references_start", None, {}, "info")
 
-        rows = (
-            session.query(
-                Protein.accession,
-                SequenceEmbedding.embedding,
+        cached = _load_from_disk_cache(embedding_config_id, annotation_set_id)
+        if cached is not None:
+            emit(
+                "predict_go_terms_batch.load_references_done",
+                None,
+                {
+                    "references": len(cached["accessions"]),
+                    "embeddings_mb": round(cached["embeddings"].nbytes / 1024 / 1024),
+                    "source": "disk_cache",
+                },
+                "info",
             )
-            .join(Protein.sequence)
+            return cached
+
+        annotated_accessions_sq = (
+            session.query(ProteinGOAnnotation.protein_accession)
+            .filter(ProteinGOAnnotation.annotation_set_id == annotation_set_id)
+            .distinct()
+            .subquery()
+        )
+        base_q = (
+            session.query(Protein.accession, SequenceEmbedding.embedding)
             .join(
                 SequenceEmbedding,
                 (SequenceEmbedding.sequence_id == Protein.sequence_id)
                 & (SequenceEmbedding.embedding_config_id == embedding_config_id),
             )
-            .filter(
-                Protein.accession.in_(
-                    session.query(ProteinGOAnnotation.protein_accession)
-                    .filter(ProteinGOAnnotation.annotation_set_id == annotation_set_id)
-                    .distinct()
-                )
+            .join(
+                annotated_accessions_sq,
+                Protein.accession == annotated_accessions_sq.c.protein_accession,
             )
-            .all()
         )
 
-        if not rows:
+        # Count first so we can pre-allocate the numpy array and never build a
+        # list-of-lists in Python.  Without pre-allocation, .all() on 400k rows
+        # materialises ~14 GB of Python float objects and hits swap.
+        total = base_q.count()
+        if total == 0:
             return {"accessions": [], "embeddings": np.empty((0,), dtype=np.float16)}
 
-        accessions = [r[0] for r in rows]
-        # float16: half the memory of float32, sufficient precision for cosine KNN
-        embeddings = np.array([list(r[1]) for r in rows], dtype=np.float16)
-
-        emit("predict_go_terms_batch.load_references_done", None, {
-            "references": len(accessions),
-            "embeddings_mb": round(embeddings.nbytes / 1024 / 1024),
-        }, "info")
+        # Determine embedding dimension from a single row.
+        first_emb = base_q.limit(1).one()[1]
+        dim = len(first_emb)
+
+        # Pre-allocate float16 array; fill row-by-row via yield_per so the
+        # cursor fetches _STREAM_CHUNK_SIZE rows at a time — peak Python-object
+        # memory stays at ~chunk_size × dim × 28 bytes ≈ tens of MB, not 14 GB.
+        embeddings = np.empty((total, dim), dtype=np.float16)
+        accessions: list[str] = []
+        for i, (acc, emb) in enumerate(base_q.yield_per(_STREAM_CHUNK_SIZE)):
+            embeddings[i] = emb
+            accessions.append(acc)
+
+        _save_to_disk_cache(embedding_config_id, annotation_set_id, accessions, embeddings)
+
+        emit(
+            "predict_go_terms_batch.load_references_done",
+            None,
+            {
+                "references": len(accessions),
+                "embeddings_mb": round(embeddings.nbytes / 1024 / 1024),
+                "source": "database",
+            },
+            "info",
+        )
 
         return {"accessions": accessions, "embeddings": embeddings}
 
-    def _load_annotations_for(
+    def _load_reference_data_per_aspect(
         self,
         session: Session,
+        embedding_config_id: uuid.UUID,
         annotation_set_id: uuid.UUID,
-        accessions: set[str],
-    ) -> dict[str, list[dict[str, Any]]]:
-        """Load GO annotations for the given accessions, chunked to avoid param limits."""
-        go_map: dict[str, list[dict[str, Any]]] = {}
-        accessions_list = list(accessions)
+        emit: EmitFn,
+    ) -> dict[str, dict[str, Any]]:
+        """Build per-aspect views over the single unified reference cache.
+
+        Strategy — one array, three index slices:
+
+        1. Load (or build) the **unified** reference embeddings exactly as
+           :meth:`_load_reference_data` does — a single 1 GB float16 array shared
+           across all three aspects.  No embeddings are duplicated on disk or in RAM.
+        2. For each aspect (P / F / C) load (or build) a tiny **index array** — a
+           1-D int32 array of row positions inside the unified array that correspond
+           to proteins annotated in that aspect.  Index arrays are ~2 MB each and
+           are built with a lightweight accession-only query (no embedding data fetched).
+        3. Return per-aspect sub-arrays as numpy fancy-index results (a copy in
+           float16, ~300 MB per aspect at most).
+
+        Disk layout::
+
+            {key}_embeddings.npy            ← unified, ~1 GB float16  (shared)
+            {key}_accessions.npy            ← unified accession list   (shared)
+            {key}__P_indices.npy            ← int32 row indices, ~2 MB
+            {key}__F_indices.npy
+            {key}__C_indices.npy
+            {key}__P_anno_gtids.npy         ← CSR annotation cache per aspect
+            {key}__P_anno_quals.npy
+            {key}__P_anno_ecodes.npy
+            {key}__P_anno_offsets.npy
+            {key}__F_anno_*.npy
+            {key}__C_anno_*.npy
+        """
+        emit(
+            "predict_go_terms_batch.load_references_per_aspect_start",
+            None,
+            {
+                "embedding_config_id": str(embedding_config_id),
+                "annotation_set_id": str(annotation_set_id),
+            },
+            "info",
+        )
 
-        for i in range(0, len(accessions_list), _ANNOTATION_CHUNK_SIZE):
-            chunk = accessions_list[i: i + _ANNOTATION_CHUNK_SIZE]
+        # ── step 1: unified embeddings (reuses existing disk cache or builds it once) ──
+        unified = self._load_reference_data(session, embedding_config_id, annotation_set_id, emit)
+        if not unified["accessions"]:
+            return {
+                asp: {"accessions": [], "embeddings": np.empty((0,), dtype=np.float16)}
+                for asp in _ASPECTS
+            }
+
+        acc_to_idx: dict[str, int] = {acc: i for i, acc in enumerate(unified["accessions"])}
+
+        # ── step 2: per-aspect index arrays ──────────────────────────────────────────
+        result: dict[str, dict[str, Any]] = {}
+        total_refs = 0
+
+        # Determine which aspects still need DB queries (index or annotation cache missing)
+        missing_aspects = [
+            asp
+            for asp in _ASPECTS
+            if not _aspect_index_path(embedding_config_id, annotation_set_id, asp).exists()
+            or _load_anno_csr_from_disk(embedding_config_id, annotation_set_id, asp) is None
+        ]
+
+        # Single-pass query for ALL missing aspects: fetch full annotation rows
+        # (accession, aspect, go_term_id, qualifier, evidence_code) in one table scan.
+        # This replaces both the old index-only query and all per-batch IN queries.
+        aspect_to_accset: dict[str, set[str]] = {asp: set() for asp in missing_aspects}
+        aspect_to_go_map: dict[str, dict[str, list[dict[str, Any]]]] = {
+            asp: {} for asp in missing_aspects
+        }
+        if missing_aspects:
             rows = (
                 session.query(
                     ProteinGOAnnotation.protein_accession,
+                    GOTerm.aspect,
                     ProteinGOAnnotation.go_term_id,
                     ProteinGOAnnotation.qualifier,
                     ProteinGOAnnotation.evidence_code,
                 )
+                .join(ProteinGOAnnotation.go_term)
                 .filter(
                     ProteinGOAnnotation.annotation_set_id == annotation_set_id,
-                    ProteinGOAnnotation.protein_accession.in_(chunk),
+                    GOTerm.aspect.in_(missing_aspects),
+                    (
+                        ProteinGOAnnotation.qualifier.is_(None)
+                        | ~ProteinGOAnnotation.qualifier.like("%NOT%")
+                    ),
                 )
-                .all()
+                .yield_per(50_000)
             )
+            for acc, asp, go_term_id, qualifier, evidence_code in rows:
+                if asp in aspect_to_accset:
+                    aspect_to_accset[asp].add(acc)
+                    aspect_to_go_map[asp].setdefault(acc, []).append({
+                        "go_term_id": go_term_id,
+                        "qualifier": qualifier,
+                        "evidence_code": evidence_code,
+                    })
+
+            for asp in missing_aspects:
+                # Save embedding index array
+                idx_path = _aspect_index_path(embedding_config_id, annotation_set_id, asp)
+                indices = np.array(
+                    [acc_to_idx[acc] for acc in aspect_to_accset[asp] if acc in acc_to_idx],
+                    dtype=np.int32,
+                )
+                idx_path.parent.mkdir(parents=True, exist_ok=True)
+                np.save(idx_path, indices)
+
+                # Save annotation CSR cache — zero DB queries during batch processing
+                asp_accessions = [unified["accessions"][i] for i in indices]
+                gtids, quals, ecodes, offsets = _build_anno_csr(
+                    asp_accessions, aspect_to_go_map[asp]
+                )
+                _save_anno_csr_to_disk(
+                    embedding_config_id, annotation_set_id, asp, gtids, quals, ecodes, offsets
+                )
+
+        for aspect in _ASPECTS:
+            idx_path = _aspect_index_path(embedding_config_id, annotation_set_id, aspect)
+            indices = np.load(idx_path)
+            source = "disk_cache" if aspect not in missing_aspects else "database"
+
+            aspect_accessions = [unified["accessions"][i] for i in indices]
+            aspect_embeddings = unified["embeddings"][indices]  # float16 copy, ~300 MB max
+
+            anno_csr = _load_anno_csr_from_disk(embedding_config_id, annotation_set_id, aspect)
+            anno_data: dict[str, Any] = {}
+            if anno_csr is not None:
+                gtids, quals, ecodes, offsets = anno_csr
+                anno_data = {
+                    "anno_gtids": gtids,
+                    "anno_quals": quals,
+                    "anno_ecodes": ecodes,
+                    "anno_offsets": offsets,
+                    "acc_to_anno_idx": {acc: i for i, acc in enumerate(aspect_accessions)},
+                }
+
+            result[aspect] = {
+                "accessions": aspect_accessions,
+                "embeddings": aspect_embeddings,
+                **anno_data,
+            }
+            total_refs += len(indices)
+            emit(
+                "predict_go_terms_batch.load_references_per_aspect_done",
+                None,
+                {
+                    "aspect": aspect,
+                    "references": len(indices),
+                    "source": source,
+                },
+                "info",
+            )
+
+        emit(
+            "predict_go_terms_batch.load_references_per_aspect_all_done",
+            None,
+            {
+                "total_references": total_refs,
+            },
+            "info",
+        )
+        return result
+
+    def _run_aspect_separated_knn(
+        self,
+        session: Session,
+        valid_accessions: list[str],
+        query_embeddings: np.ndarray,
+        ref_data_by_aspect: dict[str, dict[str, Any]],
+        annotation_set_id: uuid.UUID,
+        prediction_set_id: uuid.UUID,
+        p: PredictGOTermsBatchPayload,
+    ) -> list[dict[str, Any]]:
+        """Run three independent KNN searches (one per GO aspect) and merge results.
+
+        For each aspect ``a`` in (P, F, C):
+        1. Build a KNN index from the aspect-filtered reference embeddings.
+        2. Find the ``limit_per_entry`` nearest neighbors for every query.
+        3. Load only aspect-``a`` GO annotations for those neighbors.
+        4. Transfer those annotations as predictions.
+
+        This guarantees that every query protein can receive BPO, MFO, and CCO
+        candidates even if its globally nearest neighbors happen to carry
+        annotations in only one or two aspects — the dominant cause of the BPO
+        recall ceiling observed with a unified index.
+
+        Feature engineering (alignments / taxonomy) is computed for the union of
+        neighbors across all aspects to avoid redundant work on shared neighbors.
+        """
+        # Collect all unique neighbors across aspects so feature engineering
+        # is computed once per pair regardless of how many aspects reference it.
+        neighbors_by_aspect: dict[str, list[list[tuple[str, float]]]] = {}
+        all_unique_neighbors: set[str] = set()
+
+        for aspect in _ASPECTS:
+            aspect_refs = ref_data_by_aspect[aspect]
+            if not aspect_refs["accessions"]:
+                neighbors_by_aspect[aspect] = [[] for _ in valid_accessions]
+                continue
+
+            ref_f32 = aspect_refs["embeddings"].astype(np.float32)
+            aspect_neighbors = search_knn(
+                query_embeddings,
+                ref_f32,
+                aspect_refs["accessions"],
+                k=p.limit_per_entry,
+                distance_threshold=p.distance_threshold,
+                backend=p.search_backend,
+                metric=p.metric,
+                faiss_index_type=p.faiss_index_type,
+                faiss_nlist=p.faiss_nlist,
+                faiss_nprobe=p.faiss_nprobe,
+                faiss_hnsw_m=p.faiss_hnsw_m,
+                faiss_hnsw_ef_search=p.faiss_hnsw_ef_search,
+            )
+            neighbors_by_aspect[aspect] = aspect_neighbors
+            for top_refs in aspect_neighbors:
+                for ref_acc, _ in top_refs:
+                    all_unique_neighbors.add(ref_acc)
+
+        # Feature engineering — computed over the union of all neighbors
+        ref_sequences: dict[str, str] = {}
+        query_sequences: dict[str, str] = {}
+        ref_tax_ids: dict[str, int | None] = {}
+        query_tax_ids: dict[str, int | None] = {}
+
+        if p.compute_alignments:
+            ref_sequences = self._load_sequences_for_proteins(session, all_unique_neighbors)
+            query_sequences = self._load_sequences_for_queries(session, p, valid_accessions)
+
+        if p.compute_taxonomy:
+            ref_tax_ids = self._load_taxonomy_ids_for_proteins(session, all_unique_neighbors)
+            query_tax_ids = self._load_taxonomy_ids_for_queries(session, p, valid_accessions)
+
+        # Build predictions per aspect, merging into a single list.
+        # seen_terms is keyed per query protein to deduplicate across aspects.
+        predictions: list[dict[str, Any]] = []
+        seen_per_query: dict[str, set[int]] = {acc: set() for acc in valid_accessions}
+        pair_features: dict[tuple[str, str], dict[str, Any]] = {}
+
+        for aspect in _ASPECTS:
+            unique_neighbors_aspect: set[str] = set()
+            for top_refs in neighbors_by_aspect[aspect]:
+                for ref_acc, _ in top_refs:
+                    unique_neighbors_aspect.add(ref_acc)
+
+            aspect_ref = ref_data_by_aspect[aspect]
+            if "anno_gtids" in aspect_ref:
+                go_map = _csr_lookup(
+                    unique_neighbors_aspect,
+                    aspect_ref["accessions"],
+                    aspect_ref["acc_to_anno_idx"],
+                    aspect_ref["anno_gtids"],
+                    aspect_ref["anno_quals"],
+                    aspect_ref["anno_ecodes"],
+                    aspect_ref["anno_offsets"],
+                )
+            else:
+                go_map = self._load_annotations_for(
+                    session, annotation_set_id, unique_neighbors_aspect, aspect=aspect
+                )
+
+            for q_acc, top_refs in zip(valid_accessions, neighbors_by_aspect[aspect], strict=False):
+                seen_terms = seen_per_query[q_acc]
+
+                for ref_acc, distance in top_refs:
+                    pair_key = (q_acc, ref_acc)
+                    if pair_key not in pair_features:
+                        feats: dict[str, Any] = {}
+                        if p.compute_alignments:
+                            q_seq = query_sequences.get(q_acc, "")
+                            r_seq = ref_sequences.get(ref_acc, "")
+                            if q_seq and r_seq:
+                                feats.update(compute_alignment(q_seq, r_seq))
+                        if p.compute_taxonomy:
+                            q_tid = query_tax_ids.get(q_acc)
+                            r_tid = ref_tax_ids.get(ref_acc)
+                            feats.update(compute_taxonomy(q_tid, r_tid))
+                            feats["query_taxonomy_id"] = q_tid
+                            feats["ref_taxonomy_id"] = r_tid
+                        pair_features[pair_key] = feats
+
+                    feats = pair_features[pair_key]
+
+                    for ann in go_map.get(ref_acc, []):
+                        go_term_id = ann["go_term_id"]
+                        if go_term_id in seen_terms:
+                            continue
+                        seen_terms.add(go_term_id)
+                        pred: dict[str, Any] = {
+                            "prediction_set_id": str(prediction_set_id),
+                            "protein_accession": q_acc,
+                            "go_term_id": go_term_id,
+                            "ref_protein_accession": ref_acc,
+                            "distance": distance,
+                        }
+                        if ann.get("qualifier"):
+                            pred["qualifier"] = ann["qualifier"]
+                        if ann.get("evidence_code"):
+                            pred["evidence_code"] = ann["evidence_code"]
+                        for key in (
+                            "identity_nw",
+                            "similarity_nw",
+                            "alignment_score_nw",
+                            "gaps_pct_nw",
+                            "alignment_length_nw",
+                            "identity_sw",
+                            "similarity_sw",
+                            "alignment_score_sw",
+                            "gaps_pct_sw",
+                            "alignment_length_sw",
+                            "length_query",
+                            "length_ref",
+                            "query_taxonomy_id",
+                            "ref_taxonomy_id",
+                            "taxonomic_lca",
+                            "taxonomic_distance",
+                            "taxonomic_common_ancestors",
+                            "taxonomic_relation",
+                        ):
+                            val = feats.get(key)
+                            if val is not None:
+                                pred[key] = val
+                        predictions.append(pred)
+
+        return predictions
+
+    def _load_annotations_for(
+        self,
+        session: Session,
+        annotation_set_id: uuid.UUID,
+        accessions: set[str],
+        aspect: str | None = None,
+    ) -> dict[str, list[dict[str, Any]]]:
+        """Load GO annotations for the given accessions, chunked to avoid param limits.
+
+        Only non-negated annotations are loaded: rows with a NOT qualifier (e.g.
+        ``'NOT'``, ``'NOT|involved_in'``) assert that the protein does *not* have
+        the annotated function and must never be transferred as positive predictions.
+        Although NOT annotations are rare in GOA/QuickGO (~0.1 % of rows), including
+        them would introduce false positives that are silently penalised by cafaeval
+        without any obvious trace in the prediction artefacts.
+
+        When ``aspect`` is given (``'P'``, ``'F'``, or ``'C'``), only annotations
+        whose GO term belongs to that aspect are returned.  This is used by the
+        per-aspect KNN mode so that BPO-index neighbors transfer only BPO terms,
+        MFO-index neighbors transfer only MFO terms, etc.  The join to ``go_term``
+        is added only when needed to keep the no-aspect path as fast as before.
+        """
+        go_map: dict[str, list[dict[str, Any]]] = {}
+        accessions_list = list(accessions)
+
+        for i in range(0, len(accessions_list), _ANNOTATION_CHUNK_SIZE):
+            chunk = accessions_list[i : i + _ANNOTATION_CHUNK_SIZE]
+            q = session.query(
+                ProteinGOAnnotation.protein_accession,
+                ProteinGOAnnotation.go_term_id,
+                ProteinGOAnnotation.qualifier,
+                ProteinGOAnnotation.evidence_code,
+            ).filter(
+                ProteinGOAnnotation.annotation_set_id == annotation_set_id,
+                ProteinGOAnnotation.protein_accession.in_(chunk),
+                # Exclude NOT-qualified annotations (e.g. 'NOT', 'NOT|involved_in').
+                # qualifier IS NULL must be preserved explicitly because SQL LIKE
+                # returns NULL for NULL inputs, which would silently drop those rows.
+                (
+                    ProteinGOAnnotation.qualifier.is_(None)
+                    | ~ProteinGOAnnotation.qualifier.like("%NOT%")
+                ),
+            )
+            if aspect is not None:
+                # Join go_term only when aspect filtering is requested to avoid
+                # an unnecessary join on the common (non-aspect-separated) path.
+                q = q.join(ProteinGOAnnotation.go_term).filter(GOTerm.aspect == aspect)
+            rows = q.all()
             for acc, go_term_id, qualifier, evidence_code in rows:
-                go_map.setdefault(acc, []).append({
-                    "go_term_id": go_term_id,
-                    "qualifier": qualifier,
-                    "evidence_code": evidence_code,
-                })
+                go_map.setdefault(acc, []).append(
+                    {
+                        "go_term_id": go_term_id,
+                        "qualifier": qualifier,
+                        "evidence_code": evidence_code,
+                    }
+                )
 
         return go_map
 
@@ -562,10 +1209,10 @@ def _predict_batch(
         Returns compact dicts — None-valued optional fields are omitted to reduce
         message size.
         """
-        ref_sequences   = ref_sequences   or {}
+        ref_sequences = ref_sequences or {}
         query_sequences = query_sequences or {}
-        ref_tax_ids     = ref_tax_ids     or {}
-        query_tax_ids   = query_tax_ids   or {}
+        ref_tax_ids = ref_tax_ids or {}
+        query_tax_ids = query_tax_ids or {}
 
         if neighbors is None:
             ref_emb = ref_data["embeddings"]
@@ -622,25 +1269,35 @@ def _predict_batch(
                     seen_terms.add(go_term_id)
                     # Only include non-None optional fields to keep message compact
                     pred: dict[str, Any] = {
-                        "prediction_set_id":     str(prediction_set_id),
-                        "protein_accession":     q_acc,
-                        "go_term_id":            go_term_id,
+                        "prediction_set_id": str(prediction_set_id),
+                        "protein_accession": q_acc,
+                        "go_term_id": go_term_id,
                         "ref_protein_accession": ref_acc,
-                        "distance":              distance,
+                        "distance": distance,
                     }
                     if ann.get("qualifier"):
                         pred["qualifier"] = ann["qualifier"]
                     if ann.get("evidence_code"):
                         pred["evidence_code"] = ann["evidence_code"]
                     for key in (
-                        "identity_nw", "similarity_nw", "alignment_score_nw",
-                        "gaps_pct_nw", "alignment_length_nw",
-                        "identity_sw", "similarity_sw", "alignment_score_sw",
-                        "gaps_pct_sw", "alignment_length_sw",
-                        "length_query", "length_ref",
-                        "query_taxonomy_id", "ref_taxonomy_id",
-                        "taxonomic_lca", "taxonomic_distance",
-                        "taxonomic_common_ancestors", "taxonomic_relation",
+                        "identity_nw",
+                        "similarity_nw",
+                        "alignment_score_nw",
+                        "gaps_pct_nw",
+                        "alignment_length_nw",
+                        "identity_sw",
+                        "similarity_sw",
+                        "alignment_score_sw",
+                        "gaps_pct_sw",
+                        "alignment_length_sw",
+                        "length_query",
+                        "length_ref",
+                        "query_taxonomy_id",
+                        "ref_taxonomy_id",
+                        "taxonomic_lca",
+                        "taxonomic_distance",
+                        "taxonomic_common_ancestors",
+                        "taxonomic_relation",
                     ):
                         val = features.get(key)
                         if val is not None:
@@ -657,7 +1314,7 @@ def _load_sequences_for_proteins(
         result: dict[str, str] = {}
         acc_list = list(accessions)
         for i in range(0, len(acc_list), _ANNOTATION_CHUNK_SIZE):
-            chunk = acc_list[i: i + _ANNOTATION_CHUNK_SIZE]
+            chunk = acc_list[i : i + _ANNOTATION_CHUNK_SIZE]
             rows = (
                 session.query(Protein.accession, Sequence.sequence)
                 .join(Protein.sequence)
@@ -691,7 +1348,7 @@ def _load_taxonomy_ids_for_proteins(
         result: dict[str, int | None] = {}
         acc_list = list(accessions)
         for i in range(0, len(acc_list), _ANNOTATION_CHUNK_SIZE):
-            chunk = acc_list[i: i + _ANNOTATION_CHUNK_SIZE]
+            chunk = acc_list[i : i + _ANNOTATION_CHUNK_SIZE]
             rows = (
                 session.query(Protein.accession, Protein.taxonomy_id)
                 .filter(Protein.accession.in_(chunk))
@@ -711,7 +1368,7 @@ def _load_taxonomy_ids_for_queries(
         result: dict[str, int | None] = {acc: None for acc in acc_set}
         acc_list = list(acc_set)
         for i in range(0, len(acc_list), _ANNOTATION_CHUNK_SIZE):
-            chunk = acc_list[i: i + _ANNOTATION_CHUNK_SIZE]
+            chunk = acc_list[i : i + _ANNOTATION_CHUNK_SIZE]
             rows = (
                 session.query(Protein.accession, Protein.taxonomy_id)
                 .filter(Protein.accession.in_(chunk))
@@ -726,6 +1383,7 @@ def _load_taxonomy_ids_for_queries(
 # Write worker
 # ---------------------------------------------------------------------------
 
+
 class StorePredictionsOperation:
     """Write worker: bulk-inserts GOPrediction rows and updates parent job progress.
 
@@ -741,13 +1399,14 @@ def execute(
         self, session: Session, payload: dict[str, Any], *, emit: EmitFn
     ) -> OperationResult:
         p = StorePredictionsPayload.model_validate(payload)
-        parent_job_id     = UUID(p.parent_job_id)
+        parent_job_id = UUID(p.parent_job_id)
         prediction_set_id = uuid.UUID(p.prediction_set_id)
 
         parent = session.get(Job, parent_job_id)
         if parent is not None and parent.status in (JobStatus.CANCELLED, JobStatus.FAILED):
-            emit("store_predictions.skipped", None,
-                 {"parent_job_id": str(parent_job_id)}, "warning")
+            emit(
+                "store_predictions.skipped", None, {"parent_job_id": str(parent_job_id)}, "warning"
+            )
             return OperationResult(result={"skipped": True})
 
         if p.predictions:
@@ -755,48 +1414,51 @@ def execute(
                 pg_insert(GOPrediction).on_conflict_do_nothing(),
                 [
                     {
-                        "prediction_set_id":     prediction_set_id,
-                        "protein_accession":     pred["protein_accession"],
-                        "go_term_id":            pred["go_term_id"],
+                        "prediction_set_id": prediction_set_id,
+                        "protein_accession": pred["protein_accession"],
+                        "go_term_id": pred["go_term_id"],
                         "ref_protein_accession": pred["ref_protein_accession"],
-                        "distance":              pred["distance"],
-                        "qualifier":             pred.get("qualifier"),
-                        "evidence_code":         pred.get("evidence_code"),
-                        "identity_nw":           pred.get("identity_nw"),
-                        "similarity_nw":         pred.get("similarity_nw"),
-                        "alignment_score_nw":    pred.get("alignment_score_nw"),
-                        "gaps_pct_nw":           pred.get("gaps_pct_nw"),
-                        "alignment_length_nw":   pred.get("alignment_length_nw"),
-                        "identity_sw":           pred.get("identity_sw"),
-                        "similarity_sw":         pred.get("similarity_sw"),
-                        "alignment_score_sw":    pred.get("alignment_score_sw"),
-                        "gaps_pct_sw":           pred.get("gaps_pct_sw"),
-                        "alignment_length_sw":   pred.get("alignment_length_sw"),
-                        "length_query":          pred.get("length_query"),
-                        "length_ref":            pred.get("length_ref"),
-                        "query_taxonomy_id":     pred.get("query_taxonomy_id"),
-                        "ref_taxonomy_id":       pred.get("ref_taxonomy_id"),
-                        "taxonomic_lca":         pred.get("taxonomic_lca"),
-                        "taxonomic_distance":    pred.get("taxonomic_distance"),
+                        "distance": pred["distance"],
+                        "qualifier": pred.get("qualifier"),
+                        "evidence_code": pred.get("evidence_code"),
+                        "identity_nw": pred.get("identity_nw"),
+                        "similarity_nw": pred.get("similarity_nw"),
+                        "alignment_score_nw": pred.get("alignment_score_nw"),
+                        "gaps_pct_nw": pred.get("gaps_pct_nw"),
+                        "alignment_length_nw": pred.get("alignment_length_nw"),
+                        "identity_sw": pred.get("identity_sw"),
+                        "similarity_sw": pred.get("similarity_sw"),
+                        "alignment_score_sw": pred.get("alignment_score_sw"),
+                        "gaps_pct_sw": pred.get("gaps_pct_sw"),
+                        "alignment_length_sw": pred.get("alignment_length_sw"),
+                        "length_query": pred.get("length_query"),
+                        "length_ref": pred.get("length_ref"),
+                        "query_taxonomy_id": pred.get("query_taxonomy_id"),
+                        "ref_taxonomy_id": pred.get("ref_taxonomy_id"),
+                        "taxonomic_lca": pred.get("taxonomic_lca"),
+                        "taxonomic_distance": pred.get("taxonomic_distance"),
                         "taxonomic_common_ancestors": pred.get("taxonomic_common_ancestors"),
-                        "taxonomic_relation":    pred.get("taxonomic_relation"),
+                        "taxonomic_relation": pred.get("taxonomic_relation"),
                     }
                     for pred in p.predictions
                 ],
             )
 
-        emit("store_predictions.done", None, {
-            "predictions_inserted": len(p.predictions),
-            "parent_job_id": str(parent_job_id),
-        }, "info")
+        emit(
+            "store_predictions.done",
+            None,
+            {
+                "predictions_inserted": len(p.predictions),
+                "parent_job_id": str(parent_job_id),
+            },
+            "info",
+        )
 
         self._update_parent_progress(session, parent_job_id, emit)
 
         return OperationResult(result={"predictions_inserted": len(p.predictions)})
 
-    def _update_parent_progress(
-        self, session: Session, parent_job_id: UUID, emit: EmitFn
-    ) -> None:
+    def _update_parent_progress(self, session: Session, parent_job_id: UUID, emit: EmitFn) -> None:
         row = session.execute(
             sa_update(Job)
             .where(Job.id == parent_job_id, Job.status == JobStatus.RUNNING)
@@ -815,11 +1477,17 @@ def _update_parent_progress(
         ).fetchone()
 
         if closed:
-            session.add(JobEvent(
-                job_id=parent_job_id,
-                event="job.succeeded",
-                fields={"via": "last_batch_stored"},
-                level="info",
-            ))
-            emit("store_predictions.parent_succeeded", None,
-                 {"parent_job_id": str(parent_job_id)}, "info")
+            session.add(
+                JobEvent(
+                    job_id=parent_job_id,
+                    event="job.succeeded",
+                    fields={"via": "last_batch_stored"},
+                    level="info",
+                )
+            )
+            emit(
+                "store_predictions.parent_succeeded",
+                None,
+                {"parent_job_id": str(parent_job_id)},
+                "info",
+            )
diff --git a/protea/core/operations/run_cafa_evaluation.py b/protea/core/operations/run_cafa_evaluation.py
index b6c8866..ddfd1b6 100644
--- a/protea/core/operations/run_cafa_evaluation.py
+++ b/protea/core/operations/run_cafa_evaluation.py
@@ -13,6 +13,7 @@
 
 from protea.core.contracts.operation import EmitFn, OperationResult, ProteaPayload
 from protea.core.evaluation import compute_evaluation_data
+from protea.core.scoring import compute_score
 from protea.infrastructure.orm.models.annotation.annotation_set import AnnotationSet
 from protea.infrastructure.orm.models.annotation.evaluation_result import EvaluationResult
 from protea.infrastructure.orm.models.annotation.evaluation_set import EvaluationSet
@@ -20,6 +21,7 @@
 from protea.infrastructure.orm.models.annotation.ontology_snapshot import OntologySnapshot
 from protea.infrastructure.orm.models.embedding.go_prediction import GOPrediction
 from protea.infrastructure.orm.models.embedding.prediction_set import PredictionSet
+from protea.infrastructure.orm.models.embedding.scoring_config import ScoringConfig
 
 # Namespace labels used by cafaeval OBO parser
 _NS_LABELS = {
@@ -35,6 +37,18 @@ class RunCafaEvaluationPayload(ProteaPayload, frozen=True):
     prediction_set_id: str
     max_distance: float | None = Field(default=None, ge=0.0, le=2.0)
     artifacts_dir: str | None = Field(default=None)
+    scoring_config_id: str | None = Field(default=None)
+    ia_file: str | None = Field(
+        default=None,
+        description=(
+            "Path to an Information Accretion (IA) TSV file (two columns: go_id, ia_value). "
+            "When provided, cafaeval weights each GO term by its IC so that rare, specific "
+            "terms contribute more to the score than common, easy-to-predict terms. "
+            "Without this file cafaeval assigns uniform weight (IC=1) to every term, which "
+            "inflates Fmax because high-frequency terms dominate the metric. "
+            "For CAFA6 evaluations use the IA_cafa6.tsv file supplied with the benchmark."
+        ),
+    )
 
     @field_validator("evaluation_set_id", "prediction_set_id", mode="before")
     @classmethod
@@ -51,10 +65,20 @@ class RunCafaEvaluationOperation:
       1. Load EvaluationSet and PredictionSet from DB.
       2. Compute evaluation data (delta NK/LK + known-terms) with full NOT propagation.
       3. Download the OBO file from the ontology snapshot URL.
-      4. Write temp files: ground-truth NK/LK, known-terms, predictions (CAFA format).
-      5. Call ``cafa_eval`` for each setting (NK, LK, PK).
-      6. Parse per-namespace Fmax / precision / recall / coverage from results.
-      7. Persist an EvaluationResult row with all metrics.
+      4. Resolve the Information Accretion (IA) file:
+           - If ``ia_file`` is set in the payload, use that path directly.
+           - Otherwise, if the OntologySnapshot has an ``ia_url``, download it to
+             a temporary file and pass it to cafaeval.
+           - If neither is set, cafaeval runs with uniform IC=1 for all terms.
+         IA weights make rare, specific GO terms count more than common ones and
+         are strongly recommended for publishable evaluations.  Each CAFA benchmark
+         ships its own IA file (e.g. ``IA_cafa6.tsv``); store its URL in the
+         corresponding OntologySnapshot so future evaluations pick it up
+         automatically without touching the job payload.
+      5. Write temp files: ground-truth NK/LK, known-terms, predictions (CAFA format).
+      6. Call ``cafa_eval`` for each setting (NK, LK, PK).
+      7. Parse per-namespace Fmax / precision / recall / coverage from results.
+      8. Persist an EvaluationResult row with all metrics.
     """
 
     name = "run_cafa_evaluation"
@@ -80,11 +104,16 @@ def execute(
         ann_old = session.get(AnnotationSet, eval_set.old_annotation_set_id)
         snapshot = session.get(OntologySnapshot, ann_old.ontology_snapshot_id)
 
-        emit("run_cafa_evaluation.start", None, {
-            "evaluation_set_id": str(eval_set_id),
-            "prediction_set_id": str(pred_set_id),
-            "obo_url": snapshot.obo_url,
-        }, "info")
+        emit(
+            "run_cafa_evaluation.start",
+            None,
+            {
+                "evaluation_set_id": str(eval_set_id),
+                "prediction_set_id": str(pred_set_id),
+                "obo_url": snapshot.obo_url,
+            },
+            "info",
+        )
 
         # ── 1. Compute evaluation data ────────────────────────────────────────
         emit("run_cafa_evaluation.computing_delta", None, {}, "info")
@@ -94,53 +123,105 @@ def execute(
             eval_set.new_annotation_set_id,
             ann_old.ontology_snapshot_id,
         )
-        emit("run_cafa_evaluation.delta_done", None, {
-            "nk_proteins": data.nk_proteins,
-            "lk_proteins": data.lk_proteins,
-        }, "info")
+        emit(
+            "run_cafa_evaluation.delta_done",
+            None,
+            {
+                "nk_proteins": data.nk_proteins,
+                "lk_proteins": data.lk_proteins,
+                "pk_proteins": data.pk_proteins,
+            },
+            "info",
+        )
 
         if data.delta_proteins == 0:
             raise ValueError("No delta proteins found — cannot evaluate")
 
+        # Load and snapshot ScoringConfig before the no-op commit below
+        scoring_config_snapshot: ScoringConfig | None = None
+        if p.scoring_config_id:
+            sc = session.get(ScoringConfig, uuid.UUID(p.scoring_config_id))
+            if sc is None:
+                raise ValueError(f"ScoringConfig {p.scoring_config_id} not found")
+            scoring_config_snapshot = ScoringConfig(
+                formula=sc.formula,
+                weights=dict(sc.weights),
+            )
+
         # Pre-generate result_id so the artifact directory name matches the DB row.
         result_id = uuid.uuid4()
 
         # ── 2. Prepare artifact directory (persistent) + temp dir for OBO ─────
-        artifacts_root = (
-            Path(p.artifacts_dir) / str(result_id)
-            if p.artifacts_dir
-            else None
-        )
+        artifacts_root = Path(p.artifacts_dir) / str(result_id) if p.artifacts_dir else None
         if artifacts_root is not None:
             artifacts_root.mkdir(parents=True, exist_ok=True)
 
         results: dict[str, Any] = {}
         with tempfile.TemporaryDirectory(prefix="protea_cafa_") as tmpdir:
-
             # Download OBO into temp dir (large file, not persisted)
             emit("run_cafa_evaluation.downloading_obo", None, {"url": snapshot.obo_url}, "info")
             obo_path = os.path.join(tmpdir, "go.obo")
             self._download_obo(snapshot.obo_url, obo_path)
 
+            # Resolve IA file: explicit payload path > snapshot ia_url > None (uniform IC).
+            # Priority: an explicit ia_file in the payload overrides the snapshot URL so
+            # that one-off experiments can use a custom IA without touching the snapshot.
+            # When ia_file is absent but the snapshot carries an ia_url, the file is
+            # downloaded once into tmpdir and used for all three settings (NK/LK/PK).
+            ia_path: str | None = p.ia_file
+            if ia_path is None and snapshot.ia_url:
+                ia_path = os.path.join(tmpdir, "ia.tsv")
+                emit("run_cafa_evaluation.downloading_ia", None, {"url": snapshot.ia_url}, "info")
+                self._download_tsv(snapshot.ia_url, ia_path)
+            if ia_path:
+                emit("run_cafa_evaluation.ia_resolved", None, {"ia_path": ia_path}, "info")
+            else:
+                emit(
+                    "run_cafa_evaluation.ia_missing",
+                    None,
+                    {
+                        "warning": "No IA file available; cafaeval will use uniform IC=1 for all "
+                        "GO terms. Set ia_url on the OntologySnapshot or pass ia_file "
+                        "in the payload for information-content-weighted metrics.",
+                    },
+                    "warning",
+                )
+
             # Write ground truth files
             gt_dir = str(artifacts_root) if artifacts_root else tmpdir
             nk_path = os.path.join(gt_dir, "gt_NK.tsv")
             lk_path = os.path.join(gt_dir, "gt_LK.tsv")
+            pk_path = os.path.join(gt_dir, "gt_PK.tsv")
             known_path = os.path.join(gt_dir, "known_terms.tsv")
+            pk_known_path = os.path.join(gt_dir, "pk_known_terms.tsv")
 
             self._write_gt(data.nk, nk_path)
             self._write_gt(data.lk, lk_path)
+            self._write_gt(data.pk, pk_path)
             self._write_gt(data.known, known_path)
+            self._write_gt(data.pk_known, pk_known_path)
 
             # Write predictions (CAFA format) filtered to delta proteins
             pred_dir = os.path.join(gt_dir, "predictions")
             os.makedirs(pred_dir, exist_ok=True)
             pred_path = os.path.join(pred_dir, "predictions.tsv")
-            delta_proteins = set(data.nk) | set(data.lk)
-            emit("run_cafa_evaluation.writing_predictions", None, {
-                "delta_proteins": len(delta_proteins),
-            }, "info")
-            self._write_predictions(session, pred_set_id, delta_proteins, p.max_distance, pred_path)
+            delta_proteins = set(data.nk) | set(data.lk) | set(data.pk)
+            emit(
+                "run_cafa_evaluation.writing_predictions",
+                None,
+                {
+                    "delta_proteins": len(delta_proteins),
+                },
+                "info",
+            )
+            self._write_predictions(
+                session,
+                pred_set_id,
+                delta_proteins,
+                p.max_distance,
+                pred_path,
+                scoring_config_snapshot,
+            )
 
             # No-op commit: releases the DB connection back to the pool before
             # cafaeval forks worker processes via multiprocessing.Pool.  Forked
@@ -154,7 +235,7 @@ def execute(
             for setting, gt_file, known_file in [
                 ("NK", nk_path, None),
                 ("LK", lk_path, None),
-                ("PK", lk_path, known_path),
+                ("PK", pk_path, pk_known_path),
             ]:
                 emit("run_cafa_evaluation.evaluating", None, {"setting": setting}, "info")
                 try:
@@ -166,7 +247,10 @@ def execute(
                     _old_sigint = signal.signal(signal.SIGINT, signal.SIG_DFL)
                     try:
                         df, dfs_best = cafa_eval(
-                            obo_path, pred_dir, gt_file,
+                            obo_path,
+                            pred_dir,
+                            gt_file,
+                            ia=ia_path,
                             exclude=known_file,
                             prop="max",
                             norm="cafa",
@@ -181,19 +265,30 @@ def execute(
                     # Persist full cafaeval output (PR curves + best metrics per metric type)
                     if artifacts_root is not None and df is not None:
                         from cafaeval.evaluation import write_results as _write_results
+
                         setting_dir = artifacts_root / setting
                         setting_dir.mkdir(exist_ok=True)
                         _write_results(df, dfs_best, str(setting_dir))
 
-                    emit("run_cafa_evaluation.setting_done", None, {
-                        "setting": setting,
-                        "namespaces": list(results[setting].keys()),
-                    }, "info")
+                    emit(
+                        "run_cafa_evaluation.setting_done",
+                        None,
+                        {
+                            "setting": setting,
+                            "namespaces": list(results[setting].keys()),
+                        },
+                        "info",
+                    )
                 except Exception as exc:
-                    emit("run_cafa_evaluation.setting_failed", None, {
-                        "setting": setting,
-                        "error": str(exc),
-                    }, "warning")
+                    emit(
+                        "run_cafa_evaluation.setting_failed",
+                        None,
+                        {
+                            "setting": setting,
+                            "error": str(exc),
+                        },
+                        "warning",
+                    )
                     results[setting] = {}
 
         # ── 3. Persist EvaluationResult ───────────────────────────────────────
@@ -201,26 +296,35 @@ def execute(
             id=result_id,
             evaluation_set_id=eval_set_id,
             prediction_set_id=pred_set_id,
+            scoring_config_id=uuid.UUID(p.scoring_config_id) if p.scoring_config_id else None,
             results=results,
         )
         session.add(eval_result)
         session.flush()
 
-        emit("run_cafa_evaluation.done", None, {
-            "evaluation_result_id": str(result_id),
-            "settings_evaluated": list(results.keys()),
-            "artifacts_dir": str(artifacts_root) if artifacts_root else None,
-        }, "info")
-        return OperationResult(result={
-            "evaluation_result_id": str(result_id),
-            "results": results,
-        })
+        emit(
+            "run_cafa_evaluation.done",
+            None,
+            {
+                "evaluation_result_id": str(result_id),
+                "settings_evaluated": list(results.keys()),
+                "artifacts_dir": str(artifacts_root) if artifacts_root else None,
+            },
+            "info",
+        )
+        return OperationResult(
+            result={
+                "evaluation_result_id": str(result_id),
+                "results": results,
+            }
+        )
 
     # ── Helpers ───────────────────────────────────────────────────────────────
 
     def _download_obo(self, url: str, dest: str) -> None:
         """Download OBO file to dest, decompressing gzip if needed."""
         import gzip
+
         resp = requests.get(url, stream=True, timeout=300)
         resp.raise_for_status()
         if url.endswith(".gz"):
@@ -230,6 +334,44 @@ def _download_obo(self, url: str, dest: str) -> None:
             with open(dest, "w", encoding="utf-8") as f:
                 f.write(resp.text)
 
+    def _download_tsv(self, url: str, dest: str) -> None:
+        """Copy or download a plain-text TSV file (gzip-transparent) to dest.
+
+        Accepts both HTTP(S) URLs and local filesystem paths (absolute or
+        ``file://`` scheme).  Local paths are resolved without any network
+        request, which is useful during development when the IA file lives
+        inside the repository (``data/benchmarks/IA_cafa6.tsv``) and
+        ``ia_url`` is set to its absolute path.  Once the file is pushed to
+        GitHub the URL can be switched to the raw.githubusercontent.com
+        address and the same code path handles it transparently.
+        """
+        import gzip as _gzip
+        import shutil
+
+        # Resolve local paths (absolute or file:// scheme) without HTTP.
+        local_path: str | None = None
+        if url.startswith("file://"):
+            local_path = url[len("file://") :]
+        elif url.startswith("/"):
+            local_path = url
+
+        if local_path is not None:
+            if url.endswith(".gz"):
+                with _gzip.open(local_path, "rb") as src, open(dest, "wb") as f:
+                    shutil.copyfileobj(src, f)
+            else:
+                shutil.copy2(local_path, dest)
+            return
+
+        resp = requests.get(url, stream=True, timeout=300)
+        resp.raise_for_status()
+        if url.endswith(".gz"):
+            with open(dest, "wb") as f:
+                f.write(_gzip.decompress(resp.content))
+        else:
+            with open(dest, "w", encoding="utf-8") as f:
+                f.write(resp.text)
+
     def _write_gt(self, annotations: dict[str, set[str]], path: str) -> None:
         """Write {protein: {go_id}} to a 2-column TSV (no header)."""
         with open(path, "w") as f:
@@ -244,8 +386,14 @@ def _write_predictions(
         delta_proteins: set[str],
         max_distance: float | None,
         path: str,
+        scoring_config: ScoringConfig | None = None,
     ) -> None:
-        """Write CAFA-format predictions (protein\\tgo_id\\tscore) for delta proteins."""
+        """Write CAFA-format predictions (protein\\tgo_id\\tscore) for delta proteins.
+
+        If a ScoringConfig is provided, scores are computed via compute_score()
+        using all available signals (embedding similarity, evidence, alignment,
+        taxonomy).  Otherwise falls back to ``1 - cosine_distance / 2``.
+        """
         q = (
             session.query(GOPrediction, GOTerm)
             .join(GOTerm, GOPrediction.go_term_id == GOTerm.id)
@@ -263,7 +411,17 @@ def _write_predictions(
                 if key in seen:
                     continue
                 seen.add(key)
-                score = max(0.0, 1.0 - pred.distance)
+                if scoring_config is not None:
+                    pred_dict = {
+                        "distance": pred.distance,
+                        "identity_nw": pred.identity_nw,
+                        "identity_sw": pred.identity_sw,
+                        "evidence_code": pred.evidence_code,
+                        "taxonomic_distance": pred.taxonomic_distance,
+                    }
+                    score = compute_score(pred_dict, scoring_config)
+                else:
+                    score = max(0.0, 1.0 - (pred.distance or 0.0) / 2.0)
                 f.write(f"{pred.protein_accession}\t{gt.go_id}\t{score:.4f}\n")
 
     def _parse_results(self, dfs_best: dict) -> dict[str, Any]:
diff --git a/protea/core/scoring.py b/protea/core/scoring.py
new file mode 100644
index 0000000..183c01a
--- /dev/null
+++ b/protea/core/scoring.py
@@ -0,0 +1,193 @@
+"""Scoring engine for GOPrediction rows.
+
+Applies a :class:`~protea.infrastructure.orm.models.embedding.scoring_config.ScoringConfig`
+formula to raw prediction signals and returns a normalised [0, 1] confidence score.
+
+The engine is intentionally *stateless*: every call to :func:`compute_score`
+is self-contained, which means any ``ScoringConfig`` can be applied to an
+existing ``PredictionSet`` at any time without re-running the KNN search.
+
+Evidence-code weights
+---------------------
+Evidence code quality is resolved through a two-level lookup:
+
+1. If ``config.evidence_weights`` is not ``None``, that dict is checked first.
+2. For codes absent from the override (or when no override exists), the module-
+   level :data:`DEFAULT_EVIDENCE_WEIGHTS` table is used.
+3. Codes unknown to both tables fall back to
+   :data:`DEFAULT_EVIDENCE_WEIGHT_FALLBACK` (0.5).
+
+This means a ``ScoringConfig`` may carry a *partial* override — e.g. only
+changing the IEA weight from 0.3 to 0.0 — without having to redeclare every
+other code.  The resolution order ensures backwards compatibility: configs
+stored without ``evidence_weights`` behave identically to older configs.
+"""
+
+from __future__ import annotations
+
+from typing import Any
+
+from protea.core.evidence_codes import ECO_TO_CODE
+from protea.infrastructure.orm.models.embedding.scoring_config import (
+    DEFAULT_EVIDENCE_WEIGHT_FALLBACK,
+    DEFAULT_EVIDENCE_WEIGHTS,
+    FORMULA_EVIDENCE_WEIGHTED,
+    ScoringConfig,
+)
+
+# ---------------------------------------------------------------------------
+# Evidence-code weight resolution
+# ---------------------------------------------------------------------------
+
+
+def evidence_weight(
+    code: str | None,
+    *,
+    overrides: dict[str, float] | None = None,
+) -> float:
+    """Resolve the [0, 1] quality weight for a GO evidence code or ECO ID.
+
+    Resolution order
+    ----------------
+    1. Normalise *code* from ECO ID to GO code via :data:`ECO_TO_CODE` if needed.
+    2. Look up the normalised code in *overrides* (if provided).
+    3. Fall back to :data:`DEFAULT_EVIDENCE_WEIGHTS`.
+    4. If still not found, return :data:`DEFAULT_EVIDENCE_WEIGHT_FALLBACK`.
+
+    Parameters
+    ----------
+    code:
+        A GO evidence code (e.g. ``"IEA"``) or an ECO URI
+        (e.g. ``"ECO:0000501"``).  ``None`` returns the fallback weight.
+    overrides:
+        Optional per-config evidence weight table.  May be a partial dict;
+        codes not present here are resolved via :data:`DEFAULT_EVIDENCE_WEIGHTS`.
+
+    Returns
+    -------
+    float in [0, 1].
+    """
+    if not code:
+        return DEFAULT_EVIDENCE_WEIGHT_FALLBACK
+
+    # Normalise ECO IDs to canonical GO evidence codes.
+    normalized = ECO_TO_CODE.get(code, code)
+
+    # Config-level override takes precedence over the system default.
+    if overrides and normalized in overrides:
+        return float(overrides[normalized])
+
+    return DEFAULT_EVIDENCE_WEIGHTS.get(normalized, DEFAULT_EVIDENCE_WEIGHT_FALLBACK)
+
+
+# ---------------------------------------------------------------------------
+# Score computation
+# ---------------------------------------------------------------------------
+
+
+def compute_score(pred: dict[str, Any], config: ScoringConfig) -> float:
+    """Compute a [0, 1] confidence score for a single GOPrediction dict.
+
+    All signals are normalised to [0, 1] before weighting.  Signals whose
+    value is ``None`` (because the corresponding feature-engineering flag was
+    not enabled at prediction time) are *silently excluded* from both the
+    numerator and the denominator, so the remaining signals still produce a
+    valid normalised score.
+
+    Parameters
+    ----------
+    pred:
+        Dict with raw prediction fields.  Recognised keys:
+
+        - ``distance`` (float): cosine distance in [0, 2].
+        - ``identity_nw`` (float | None): NW global identity in [0, 1].
+        - ``identity_sw`` (float | None): SW local identity in [0, 1].
+        - ``evidence_code`` (str | None): GO or ECO evidence code.
+        - ``taxonomic_distance`` (float | None): raw taxonomic distance.
+
+    config:
+        A :class:`ScoringConfig` instance defining the formula, signal
+        weights, and optional per-code evidence weight overrides.
+
+    Returns
+    -------
+    float in [0, 1].  Higher values indicate higher predicted confidence.
+    The result is rounded to 6 decimal places.
+    """
+    signal_weights = config.weights
+    ev_overrides: dict[str, float] | None = config.evidence_weights or None
+
+    total_w = 0.0
+    weighted_sum = 0.0
+
+    def _add(key: str, value: float | None) -> None:
+        """Add one signal's contribution to the running weighted average."""
+        nonlocal total_w, weighted_sum
+        w = float(signal_weights.get(key, 0.0))
+        if w == 0.0 or value is None:
+            return
+        total_w += w
+        weighted_sum += w * max(0.0, min(1.0, value))
+
+    # 1. Embedding similarity: cosine distance [0, 2] → similarity [0, 1].
+    distance = pred.get("distance")
+    if distance is not None:
+        _add("embedding_similarity", 1.0 - distance / 2.0)
+
+    # 2. Global sequence identity (Needleman-Wunsch).
+    _add("identity_nw", pred.get("identity_nw"))
+
+    # 3. Local sequence identity (Smith-Waterman).
+    _add("identity_sw", pred.get("identity_sw"))
+
+    # 4. Evidence code quality — resolved with per-config overrides.
+    ev_w = evidence_weight(pred.get("evidence_code"), overrides=ev_overrides)
+    _add("evidence_weight", ev_w)
+
+    # 5. Taxonomic proximity: 1 / (1 + d) maps [0, ∞) → (0, 1].
+    tax_dist = pred.get("taxonomic_distance")
+    if tax_dist is not None:
+        _add("taxonomic_proximity", 1.0 / (1.0 + float(tax_dist)))
+
+    if total_w == 0.0:
+        return 0.0
+
+    base_score = weighted_sum / total_w
+
+    # evidence_weighted formula: multiply the final score by the resolved
+    # evidence quality so that low-confidence annotations (IEA, ND) are
+    # down-ranked even when other signals are strong — and regardless of
+    # whether the evidence_weight signal is active (its signal weight may be 0).
+    if config.formula == FORMULA_EVIDENCE_WEIGHTED:
+        base_score *= ev_w
+
+    return round(base_score, 6)
+
+
+# ---------------------------------------------------------------------------
+# Batch helper
+# ---------------------------------------------------------------------------
+
+
+def score_predictions(
+    predictions: list[dict[str, Any]],
+    config: ScoringConfig,
+) -> list[dict[str, Any]]:
+    """Add a ``score`` key to each prediction dict and return them sorted descending.
+
+    Parameters
+    ----------
+    predictions:
+        List of raw prediction dicts (same format as accepted by
+        :func:`compute_score`).
+    config:
+        The :class:`ScoringConfig` to apply.
+
+    Returns
+    -------
+    A new list with a ``score`` key added to each item, sorted by score in
+    descending order.  The original list is not modified.
+    """
+    scored = [{**p, "score": compute_score(p, config)} for p in predictions]
+    scored.sort(key=lambda x: x["score"], reverse=True)
+    return scored
diff --git a/protea/core/utils.py b/protea/core/utils.py
index 041336c..4811c1f 100644
--- a/protea/core/utils.py
+++ b/protea/core/utils.py
@@ -60,7 +60,9 @@ def _get_with_retries(self, url: str, p: _HttpPayload, emit: EmitFn) -> Response
                 if attempt > p.max_retries:
                     raise
                 self._http_retries += 1
-                self._sleep_backoff(p, attempt, emit, reason=f"request_exception:{e.__class__.__name__}")
+                self._sleep_backoff(
+                    p, attempt, emit, reason=f"request_exception:{e.__class__.__name__}"
+                )
                 continue
 
             if 200 <= resp.status_code < 300:
@@ -73,8 +75,12 @@ def _get_with_retries(self, url: str, p: _HttpPayload, emit: EmitFn) -> Response
                 retry_after = resp.headers.get("Retry-After")
                 if retry_after and retry_after.isdigit():
                     wait_s = min(float(retry_after), p.backoff_max_seconds)
-                    emit("http.retry", None,
-                         {"attempt": attempt, "wait_seconds": wait_s, "reason": "retry_after"}, "warning")
+                    emit(
+                        "http.retry",
+                        None,
+                        {"attempt": attempt, "wait_seconds": wait_s, "reason": "retry_after"},
+                        "warning",
+                    )
                     time.sleep(wait_s)
                 else:
                     self._sleep_backoff(p, attempt, emit, reason=f"status_{resp.status_code}")
@@ -85,7 +91,12 @@ def _get_with_retries(self, url: str, p: _HttpPayload, emit: EmitFn) -> Response
     def _sleep_backoff(self, p: _HttpPayload, attempt: int, emit: EmitFn, reason: str) -> None:
         base = p.backoff_base_seconds * (2 ** (attempt - 1))
         wait_s = min(base, p.backoff_max_seconds) + random.uniform(0.0, p.jitter_seconds)
-        emit("http.retry", None, {"attempt": attempt, "wait_seconds": wait_s, "reason": reason}, "warning")
+        emit(
+            "http.retry",
+            None,
+            {"attempt": attempt, "wait_seconds": wait_s, "reason": reason},
+            "warning",
+        )
         time.sleep(wait_s)
 
     def _extract_next_cursor(self, link_header: str) -> str | None:
diff --git a/protea/infrastructure/orm/models/__init__.py b/protea/infrastructure/orm/models/__init__.py
index 62d114e..2dd358e 100644
--- a/protea/infrastructure/orm/models/__init__.py
+++ b/protea/infrastructure/orm/models/__init__.py
@@ -8,9 +8,11 @@
 from .embedding.embedding_config import EmbeddingConfig  # noqa: F401
 from .embedding.go_prediction import GOPrediction  # noqa: F401
 from .embedding.prediction_set import PredictionSet  # noqa: F401
+from .embedding.scoring_config import ScoringConfig  # noqa: F401
 from .embedding.sequence_embedding import SequenceEmbedding  # noqa: F401
 from .job import Job, JobEvent  # noqa: F401
 from .protein.protein import Protein  # noqa: F401
 from .protein.protein_metadata import ProteinUniProtMetadata  # noqa: F401
 from .query.query_set import QuerySet, QuerySetEntry  # noqa: F401
 from .sequence.sequence import Sequence  # noqa: F401
+from .support_entry import SupportEntry  # noqa: F401
diff --git a/protea/infrastructure/orm/models/annotation/annotation_set.py b/protea/infrastructure/orm/models/annotation/annotation_set.py
index 65614fb..701f62b 100644
--- a/protea/infrastructure/orm/models/annotation/annotation_set.py
+++ b/protea/infrastructure/orm/models/annotation/annotation_set.py
@@ -32,9 +32,7 @@ class AnnotationSet(Base):
 
     __tablename__ = "annotation_set"
 
-    id: Mapped[uuid.UUID] = mapped_column(
-        UUID(as_uuid=True), primary_key=True, default=uuid.uuid4
-    )
+    id: Mapped[uuid.UUID] = mapped_column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
     source: Mapped[str] = mapped_column(String, nullable=False, index=True)
     source_version: Mapped[str | None] = mapped_column(String, nullable=True)
     ontology_snapshot_id: Mapped[uuid.UUID] = mapped_column(
diff --git a/protea/infrastructure/orm/models/annotation/evaluation_result.py b/protea/infrastructure/orm/models/annotation/evaluation_result.py
index e254875..187eb5d 100644
--- a/protea/infrastructure/orm/models/annotation/evaluation_result.py
+++ b/protea/infrastructure/orm/models/annotation/evaluation_result.py
@@ -13,6 +13,7 @@
 if TYPE_CHECKING:
     from protea.infrastructure.orm.models.annotation.evaluation_set import EvaluationSet
     from protea.infrastructure.orm.models.embedding.prediction_set import PredictionSet
+    from protea.infrastructure.orm.models.embedding.scoring_config import ScoringConfig
     from protea.infrastructure.orm.models.job import Job
 
 
@@ -39,9 +40,7 @@ class EvaluationResult(Base):
 
     __tablename__ = "evaluation_result"
 
-    id: Mapped[uuid.UUID] = mapped_column(
-        UUID(as_uuid=True), primary_key=True, default=uuid.uuid4
-    )
+    id: Mapped[uuid.UUID] = mapped_column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
     evaluation_set_id: Mapped[uuid.UUID] = mapped_column(
         UUID(as_uuid=True),
         ForeignKey("evaluation_set.id", ondelete="CASCADE"),
@@ -54,6 +53,12 @@ class EvaluationResult(Base):
         nullable=False,
         index=True,
     )
+    scoring_config_id: Mapped[uuid.UUID | None] = mapped_column(
+        UUID(as_uuid=True),
+        ForeignKey("scoring_config.id", ondelete="SET NULL"),
+        nullable=True,
+        index=True,
+    )
     job_id: Mapped[uuid.UUID | None] = mapped_column(
         UUID(as_uuid=True),
         ForeignKey("job.id", ondelete="SET NULL"),
@@ -67,4 +72,5 @@ class EvaluationResult(Base):
 
     evaluation_set: Mapped[EvaluationSet] = relationship("EvaluationSet")
     prediction_set: Mapped[PredictionSet] = relationship("PredictionSet")
+    scoring_config: Mapped[ScoringConfig | None] = relationship("ScoringConfig")
     job: Mapped[Job | None] = relationship("Job")
diff --git a/protea/infrastructure/orm/models/annotation/evaluation_set.py b/protea/infrastructure/orm/models/annotation/evaluation_set.py
index 8d0054d..a19ecd7 100644
--- a/protea/infrastructure/orm/models/annotation/evaluation_set.py
+++ b/protea/infrastructure/orm/models/annotation/evaluation_set.py
@@ -31,9 +31,7 @@ class EvaluationSet(Base):
 
     __tablename__ = "evaluation_set"
 
-    id: Mapped[uuid.UUID] = mapped_column(
-        UUID(as_uuid=True), primary_key=True, default=uuid.uuid4
-    )
+    id: Mapped[uuid.UUID] = mapped_column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
     old_annotation_set_id: Mapped[uuid.UUID] = mapped_column(
         UUID(as_uuid=True),
         ForeignKey("annotation_set.id", ondelete="RESTRICT"),
diff --git a/protea/infrastructure/orm/models/annotation/go_term_relationship.py b/protea/infrastructure/orm/models/annotation/go_term_relationship.py
index 35745a5..c567304 100644
--- a/protea/infrastructure/orm/models/annotation/go_term_relationship.py
+++ b/protea/infrastructure/orm/models/annotation/go_term_relationship.py
@@ -25,7 +25,9 @@ class GOTermRelationship(Base):
     __tablename__ = "go_term_relationship"
     __table_args__ = (
         UniqueConstraint(
-            "child_go_term_id", "parent_go_term_id", "relation_type",
+            "child_go_term_id",
+            "parent_go_term_id",
+            "relation_type",
             name="uq_go_term_relationship",
         ),
     )
diff --git a/protea/infrastructure/orm/models/annotation/ontology_snapshot.py b/protea/infrastructure/orm/models/annotation/ontology_snapshot.py
index dfea4c3..5115552 100644
--- a/protea/infrastructure/orm/models/annotation/ontology_snapshot.py
+++ b/protea/infrastructure/orm/models/annotation/ontology_snapshot.py
@@ -23,15 +23,32 @@ class OntologySnapshot(Base):
     file was downloaded, providing full provenance.  Multiple ``AnnotationSet``
     rows can reference the same snapshot when they were built against the same
     ontology release.
+
+    ``ia_url`` optionally points to the Information Accretion (IA) TSV file
+    associated with this ontology release.  IA files are published alongside
+    each CAFA benchmark (e.g. ``IA_cafa6.tsv``) and contain per-term
+    information-content weights that make cafaeval penalise predictions of
+    common, easy-to-predict terms less than rare, specific ones.  When present,
+    ``run_cafa_evaluation`` downloads and passes this file to cafaeval
+    automatically — no manual path is required in the job payload.  Set a new
+    ``ia_url`` on each future snapshot (CAFA7, etc.) to keep evaluations
+    comparable across benchmark generations.
     """
 
     __tablename__ = "ontology_snapshot"
 
-    id: Mapped[uuid.UUID] = mapped_column(
-        UUID(as_uuid=True), primary_key=True, default=uuid.uuid4
-    )
+    id: Mapped[uuid.UUID] = mapped_column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
     obo_url: Mapped[str] = mapped_column(String, nullable=False)
     obo_version: Mapped[str] = mapped_column(String, nullable=False, unique=True, index=True)
+    ia_url: Mapped[str | None] = mapped_column(
+        String,
+        nullable=True,
+        comment=(
+            "URL of the Information Accretion TSV for this ontology release "
+            "(two columns: go_id, ia_value). Used by run_cafa_evaluation to "
+            "weight GO terms by information content. NULL means uniform IC=1."
+        ),
+    )
     loaded_at: Mapped[datetime] = mapped_column(
         DateTime(timezone=True), nullable=False, server_default=func.now()
     )
diff --git a/protea/infrastructure/orm/models/embedding/embedding_config.py b/protea/infrastructure/orm/models/embedding/embedding_config.py
index 76b6913..5e59d0d 100644
--- a/protea/infrastructure/orm/models/embedding/embedding_config.py
+++ b/protea/infrastructure/orm/models/embedding/embedding_config.py
@@ -65,17 +65,13 @@ class EmbeddingConfig(Base):
 
     __tablename__ = "embedding_config"
 
-    id: Mapped[uuid.UUID] = mapped_column(
-        UUID(as_uuid=True), primary_key=True, default=uuid.uuid4
-    )
+    id: Mapped[uuid.UUID] = mapped_column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
     model_name: Mapped[str] = mapped_column(String, nullable=False, index=True)
     model_backend: Mapped[str] = mapped_column(String, nullable=False, default="esm")
     layer_indices: Mapped[list[Any]] = mapped_column(JSONB, nullable=False)
     layer_agg: Mapped[str] = mapped_column(String, nullable=False, default="mean")
     pooling: Mapped[str] = mapped_column(String, nullable=False, default="mean")
-    normalize_residues: Mapped[bool] = mapped_column(
-        Boolean, nullable=False, default=False
-    )
+    normalize_residues: Mapped[bool] = mapped_column(Boolean, nullable=False, default=False)
     normalize: Mapped[bool] = mapped_column(Boolean, nullable=False, default=True)
     max_length: Mapped[int] = mapped_column(Integer, nullable=False, default=1022)
     use_chunking: Mapped[bool] = mapped_column(Boolean, nullable=False, default=False)
diff --git a/protea/infrastructure/orm/models/embedding/prediction_set.py b/protea/infrastructure/orm/models/embedding/prediction_set.py
index e26e94b..4ba165e 100644
--- a/protea/infrastructure/orm/models/embedding/prediction_set.py
+++ b/protea/infrastructure/orm/models/embedding/prediction_set.py
@@ -28,9 +28,7 @@ class PredictionSet(Base):
 
     __tablename__ = "prediction_set"
 
-    id: Mapped[uuid.UUID] = mapped_column(
-        UUID(as_uuid=True), primary_key=True, default=uuid.uuid4
-    )
+    id: Mapped[uuid.UUID] = mapped_column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
     embedding_config_id: Mapped[uuid.UUID] = mapped_column(
         UUID(as_uuid=True),
         ForeignKey("embedding_config.id", ondelete="RESTRICT"),
diff --git a/protea/infrastructure/orm/models/embedding/scoring_config.py b/protea/infrastructure/orm/models/embedding/scoring_config.py
new file mode 100644
index 0000000..259a228
--- /dev/null
+++ b/protea/infrastructure/orm/models/embedding/scoring_config.py
@@ -0,0 +1,211 @@
+"""ORM model for ScoringConfig — reproducible scoring formulas for GOPrediction rows.
+
+A ScoringConfig stores two complementary layers of configuration:
+
+1. **Signal weights** (``weights`` field): a dict mapping each composite signal
+   (e.g. ``embedding_similarity``, ``identity_nw``) to a relative weight [0, 1].
+   Missing signals — because the corresponding feature-engineering flag was not
+   enabled at prediction time — are automatically excluded from the denominator,
+   so the remaining active signals always produce a normalised [0, 1] score.
+
+2. **Evidence-code weights** (``evidence_weights`` field, optional JSONB):
+   a per-GO-evidence-code quality multiplier, also in [0, 1].  When ``None``,
+   :data:`DEFAULT_EVIDENCE_WEIGHTS` is used as the fallback.  Supplying a
+   partial dict overrides only the codes present; codes absent from the dict
+   fall back to the system default, making partial overrides safe.
+
+This two-layer design separates *how much a signal matters* (signal weights)
+from *how trustworthy the underlying annotation is* (evidence weights), which
+are independent research decisions.
+
+Formulas
+--------
+linear
+    score = Σ(w_i · s_i) / Σ(w_i)  for all active (w_i > 0, s_i available) signals.
+evidence_weighted
+    Same as linear but the resolved evidence weight is always applied as a
+    final multiplier on top of the weighted sum — even when its signal weight is
+    0.  This allows down-ranking IEA-sourced predictions regardless of how
+    strong the embedding or alignment signals are.
+"""
+
+from __future__ import annotations
+
+import uuid
+from datetime import datetime
+from typing import Any
+
+from sqlalchemy import DateTime, String, Text
+from sqlalchemy.dialects.postgresql import JSONB, UUID
+from sqlalchemy.orm import Mapped, mapped_column
+from sqlalchemy.sql import func
+
+from protea.infrastructure.orm.base import Base
+
+# ---------------------------------------------------------------------------
+# Formula identifiers
+# ---------------------------------------------------------------------------
+
+FORMULA_LINEAR = "linear"
+FORMULA_EVIDENCE_WEIGHTED = "evidence_weighted"
+VALID_FORMULAS = (FORMULA_LINEAR, FORMULA_EVIDENCE_WEIGHTED)
+
+# ---------------------------------------------------------------------------
+# Signal weight defaults
+# ---------------------------------------------------------------------------
+# Pure embedding similarity by default; all other signals must be opted in.
+
+DEFAULT_WEIGHTS: dict[str, float] = {
+    "embedding_similarity": 1.0,
+    "identity_nw": 0.0,
+    "identity_sw": 0.0,
+    "evidence_weight": 0.0,
+    "taxonomic_proximity": 0.0,
+}
+
+# ---------------------------------------------------------------------------
+# Evidence-code quality weights (default table)
+# ---------------------------------------------------------------------------
+# These defaults reflect the GO Annotation quality hierarchy.  A ScoringConfig
+# may store a full or partial override in its ``evidence_weights`` column.
+#
+# Sources:
+#   GO evidence code definitions — https://geneontology.org/docs/guide-go-evidence-codes/
+#   CAFA community consensus: experimental codes provide the highest confidence.
+#
+# Default tier mapping:
+#   Experimental (EXP, IDA, IPI, IMP, IGI, IEP, HTP, HDA, HMP, HGI, HEP,
+#                 IC, TAS)                                          → 1.0
+#   Computational / Phylogenetic (ISS, ISO, ISA, ISM, IGC, IBA,
+#                                 IBD, IKR, IRD, RCA)              → 0.7
+#   Non-traceable author statement (NAS)                           → 0.5
+#   Electronic annotation (IEA)                                    → 0.3
+#   No biological data (ND)                                        → 0.1
+
+DEFAULT_EVIDENCE_WEIGHTS: dict[str, float] = {
+    # Experimental — direct biological evidence
+    "EXP": 1.0,  # Inferred from Experiment
+    "IDA": 1.0,  # Inferred from Direct Assay
+    "IPI": 1.0,  # Inferred from Physical Interaction
+    "IMP": 1.0,  # Inferred from Mutant Phenotype
+    "IGI": 1.0,  # Inferred from Genetic Interaction
+    "IEP": 1.0,  # Inferred from Expression Pattern
+    "HTP": 1.0,  # High-Throughput experiment (umbrella)
+    "HDA": 1.0,  # High-Throughput Direct Assay
+    "HMP": 1.0,  # High-Throughput Mutant Phenotype
+    "HGI": 1.0,  # High-Throughput Genetic Interaction
+    "HEP": 1.0,  # High-Throughput Expression Pattern
+    "IC": 1.0,  # Inferred by Curator
+    "TAS": 1.0,  # Traceable Author Statement
+    # Computational / Phylogenetic — derived from sequence or phylogeny
+    "ISS": 0.7,  # Inferred from Sequence or Structural Similarity
+    "ISO": 0.7,  # Inferred from Sequence Orthology
+    "ISA": 0.7,  # Inferred from Sequence Alignment
+    "ISM": 0.7,  # Inferred from Sequence Model
+    "IGC": 0.7,  # Inferred from Genomic Context
+    "IBA": 0.7,  # Inferred from Biological aspect of Ancestor
+    "IBD": 0.7,  # Inferred from Biological aspect of Descendant
+    "IKR": 0.7,  # Inferred from Key Residues
+    "IRD": 0.7,  # Inferred from Rapid Divergence
+    "RCA": 0.7,  # Inferred from Reviewed Computational Analysis
+    # Electronic / author statement — lowest-effort annotation
+    "NAS": 0.5,  # Non-traceable Author Statement
+    "IEA": 0.3,  # Inferred from Electronic Annotation (automated, bulk)
+    # No biological data — used only as a placeholder
+    "ND": 0.1,  # No biological Data available
+}
+
+#: Ordered grouping of evidence codes used for UI rendering and documentation.
+#: Preserves the biological meaning of each tier.
+EVIDENCE_CODE_GROUPS: dict[str, list[str]] = {
+    "Experimental": [
+        "EXP",
+        "IDA",
+        "IPI",
+        "IMP",
+        "IGI",
+        "IEP",
+        "HTP",
+        "HDA",
+        "HMP",
+        "HGI",
+        "HEP",
+        "IC",
+        "TAS",
+    ],
+    "Computational / Phylogenetic": [
+        "ISS",
+        "ISO",
+        "ISA",
+        "ISM",
+        "IGC",
+        "IBA",
+        "IBD",
+        "IKR",
+        "IRD",
+        "RCA",
+    ],
+    "Electronic": ["NAS", "IEA"],
+    "No data": ["ND"],
+}
+
+#: Fallback weight applied when a code is not found in any lookup table.
+DEFAULT_EVIDENCE_WEIGHT_FALLBACK: float = 0.5
+
+
+# ---------------------------------------------------------------------------
+# ORM model
+# ---------------------------------------------------------------------------
+
+
+class ScoringConfig(Base):
+    """Persistent scoring formula definition.
+
+    Instances are stored in the ``scoring_config`` table and referenced by
+    evaluation endpoints and the UI scoring selector.  Every field that
+    influences score computation is serialised, making any result fully
+    reproducible by re-applying the same ``ScoringConfig`` to the raw
+    ``GOPrediction`` rows.
+
+    Attributes
+    ----------
+    id:
+        UUID primary key.
+    name:
+        Human-readable label shown in the UI dropdown.
+    formula:
+        One of :data:`VALID_FORMULAS` — controls how the weighted average is
+        combined with the evidence multiplier.
+    weights:
+        JSONB dict mapping signal keys to their relative weights.  Valid keys
+        are the ones in :data:`DEFAULT_WEIGHTS`.  Weights of 0 deactivate a
+        signal; absent keys are treated as 0.
+    evidence_weights:
+        Optional JSONB dict mapping GO evidence codes (e.g. ``"IEA"``) to
+        per-code quality multipliers in [0, 1].  When ``None`` the system falls
+        back to :data:`DEFAULT_EVIDENCE_WEIGHTS`.  Partial dicts are allowed:
+        codes absent from the override still resolve via the default table.
+    description:
+        Free-text description shown as a tooltip in the UI.
+    created_at:
+        UTC timestamp set by the database at insert time.
+    """
+
+    __tablename__ = "scoring_config"
+
+    id: Mapped[uuid.UUID] = mapped_column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    name: Mapped[str] = mapped_column(String(255), nullable=False)
+    formula: Mapped[str] = mapped_column(String(50), nullable=False, default=FORMULA_LINEAR)
+    weights: Mapped[dict[str, Any]] = mapped_column(JSONB, nullable=False)
+    evidence_weights: Mapped[dict[str, Any] | None] = mapped_column(JSONB, nullable=True)
+    description: Mapped[str | None] = mapped_column(Text, nullable=True)
+    created_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=True), server_default=func.now(), nullable=False
+    )
+
+    def __repr__(self) -> str:
+        return (
+            f"<ScoringConfig id={self.id} name={self.name!r}"
+            f" formula={self.formula!r}"
+            f" evidence_weights={'custom' if self.evidence_weights else 'default'}>"
+        )
diff --git a/protea/infrastructure/orm/models/job.py b/protea/infrastructure/orm/models/job.py
index 102e6c5..9b5be20 100644
--- a/protea/infrastructure/orm/models/job.py
+++ b/protea/infrastructure/orm/models/job.py
@@ -42,7 +42,9 @@ class Job(Base):
     payload: Mapped[dict[str, Any]] = mapped_column(JSONB, nullable=False, default=dict)
     meta: Mapped[dict[str, Any]] = mapped_column(JSONB, nullable=False, default=dict)
 
-    created_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), nullable=False, default=utcnow)
+    created_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=True), nullable=False, default=utcnow
+    )
     started_at: Mapped[datetime | None] = mapped_column(DateTime(timezone=True), nullable=True)
     finished_at: Mapped[datetime | None] = mapped_column(DateTime(timezone=True), nullable=True)
 
diff --git a/protea/infrastructure/orm/models/protein/protein.py b/protea/infrastructure/orm/models/protein/protein.py
index 3a3ce0d..93d8186 100644
--- a/protea/infrastructure/orm/models/protein/protein.py
+++ b/protea/infrastructure/orm/models/protein/protein.py
@@ -40,9 +40,11 @@ class Protein(Base):
 
     # Core FASTA-header derived fields
     taxonomy_id: Mapped[str | None] = mapped_column(String, nullable=True, index=True)  # OX=
-    organism: Mapped[str | None] = mapped_column(String, nullable=True)                 # OS=
-    gene_name: Mapped[str | None] = mapped_column(String, nullable=True)                # GN=
-    length: Mapped[int | None] = mapped_column(Integer, nullable=True)                  # len(sequence) or UniProt length
+    organism: Mapped[str | None] = mapped_column(String, nullable=True)  # OS=
+    gene_name: Mapped[str | None] = mapped_column(String, nullable=True)  # GN=
+    length: Mapped[int | None] = mapped_column(
+        Integer, nullable=True
+    )  # len(sequence) or UniProt length
 
     # MANY proteins can share one Sequence
     sequence_id: Mapped[int | None] = mapped_column(
@@ -52,11 +54,17 @@ class Protein(Base):
         index=True,
     )
 
-    created_at: Mapped[datetime] = mapped_column(DateTime, nullable=False, server_default=func.now())
-    updated_at: Mapped[datetime] = mapped_column(DateTime, nullable=False, server_default=func.now(), onupdate=func.now())
+    created_at: Mapped[datetime] = mapped_column(
+        DateTime, nullable=False, server_default=func.now()
+    )
+    updated_at: Mapped[datetime] = mapped_column(
+        DateTime, nullable=False, server_default=func.now(), onupdate=func.now()
+    )
 
     # Relationships
-    sequence: Mapped[Sequence | None] = relationship("Sequence", back_populates="proteins", uselist=False)
+    sequence: Mapped[Sequence | None] = relationship(
+        "Sequence", back_populates="proteins", uselist=False
+    )
 
     # Optional UniProt raw metadata (defined in another module). View-only join by canonical_accession.
     uniprot_metadata: Mapped[ProteinUniProtMetadata | None] = relationship(
diff --git a/protea/infrastructure/orm/models/protein/protein_metadata.py b/protea/infrastructure/orm/models/protein/protein_metadata.py
index 5b682e0..ea9413d 100644
--- a/protea/infrastructure/orm/models/protein/protein_metadata.py
+++ b/protea/infrastructure/orm/models/protein/protein_metadata.py
@@ -44,8 +44,12 @@ class ProteinUniProtMetadata(Base):
     keywords: Mapped[str | None] = mapped_column(Text, nullable=True)
     features: Mapped[str | None] = mapped_column(Text, nullable=True)
 
-    created_at: Mapped[datetime] = mapped_column(DateTime, nullable=False, server_default=func.now())
-    updated_at: Mapped[datetime] = mapped_column(DateTime, nullable=False, server_default=func.now(), onupdate=func.now())
+    created_at: Mapped[datetime] = mapped_column(
+        DateTime, nullable=False, server_default=func.now()
+    )
+    updated_at: Mapped[datetime] = mapped_column(
+        DateTime, nullable=False, server_default=func.now(), onupdate=func.now()
+    )
 
     proteins: Mapped[list[Protein]] = relationship(
         "Protein",
diff --git a/protea/infrastructure/orm/models/query/query_set.py b/protea/infrastructure/orm/models/query/query_set.py
index a22c32c..36f6b31 100644
--- a/protea/infrastructure/orm/models/query/query_set.py
+++ b/protea/infrastructure/orm/models/query/query_set.py
@@ -34,9 +34,7 @@ class QuerySet(Base):
 
     __tablename__ = "query_set"
 
-    id: Mapped[uuid.UUID] = mapped_column(
-        UUID(as_uuid=True), primary_key=True, default=uuid.uuid4
-    )
+    id: Mapped[uuid.UUID] = mapped_column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
     name: Mapped[str] = mapped_column(String, nullable=False)
     description: Mapped[str | None] = mapped_column(Text, nullable=True)
     created_at: Mapped[datetime] = mapped_column(
diff --git a/protea/infrastructure/orm/models/sequence/sequence.py b/protea/infrastructure/orm/models/sequence/sequence.py
index a2b6400..24f026c 100644
--- a/protea/infrastructure/orm/models/sequence/sequence.py
+++ b/protea/infrastructure/orm/models/sequence/sequence.py
@@ -29,8 +29,12 @@ class Sequence(Base):
     sequence: Mapped[str] = mapped_column(Text, nullable=False)
     sequence_hash: Mapped[str] = mapped_column(String(32), unique=True, index=True, nullable=False)
 
-    created_at: Mapped[datetime] = mapped_column(DateTime, nullable=False, server_default=func.now())
-    updated_at: Mapped[datetime] = mapped_column(DateTime, nullable=False, server_default=func.now(), onupdate=func.now())
+    created_at: Mapped[datetime] = mapped_column(
+        DateTime, nullable=False, server_default=func.now()
+    )
+    updated_at: Mapped[datetime] = mapped_column(
+        DateTime, nullable=False, server_default=func.now(), onupdate=func.now()
+    )
 
     # Relationships (1-N)
     proteins: Mapped[list[Protein]] = relationship("Protein", back_populates="sequence")
diff --git a/protea/infrastructure/orm/models/support_entry.py b/protea/infrastructure/orm/models/support_entry.py
new file mode 100644
index 0000000..29fa334
--- /dev/null
+++ b/protea/infrastructure/orm/models/support_entry.py
@@ -0,0 +1,23 @@
+from __future__ import annotations
+
+import uuid
+from datetime import datetime
+
+from sqlalchemy import DateTime, Text
+from sqlalchemy.dialects.postgresql import UUID
+from sqlalchemy.orm import Mapped, mapped_column
+from sqlalchemy.sql import func
+
+from protea.infrastructure.orm.base import Base
+
+
+class SupportEntry(Base):
+    """A thumbs-up + optional comment submitted by a visitor."""
+
+    __tablename__ = "support_entry"
+
+    id: Mapped[uuid.UUID] = mapped_column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    comment: Mapped[str | None] = mapped_column(Text, nullable=True)
+    created_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=True), nullable=False, server_default=func.now()
+    )
diff --git a/protea/infrastructure/queue/consumer.py b/protea/infrastructure/queue/consumer.py
index 6a299a3..8c3310a 100644
--- a/protea/infrastructure/queue/consumer.py
+++ b/protea/infrastructure/queue/consumer.py
@@ -230,19 +230,26 @@ def _on_message(
             channel.basic_nack(delivery_tag=method.delivery_tag, requeue=False)
             return
 
-        logger.info("Dispatching operation. operation=%s queue=%s", operation_name, self._queue_name)
+        logger.info(
+            "Dispatching operation. operation=%s queue=%s", operation_name, self._queue_name
+        )
 
         op = self._registry.get(operation_name)
         session = self._factory()
         try:
-            def emit(event: str, message: str | None = None,
-                     fields: dict[str, Any] | None = None, level: str = "info") -> None:
+
+            def emit(
+                event: str,
+                message: str | None = None,
+                fields: dict[str, Any] | None = None,
+                level: str = "info",
+            ) -> None:
                 logger.info("operation.%s fields=%s", event, fields or {})
 
             result = op.execute(session, payload, emit=emit)
             session.commit()
             # Forward any downstream operation messages (e.g. GPU→write worker).
-            for queue_name, op_payload in (result.publish_operations or []):
+            for queue_name, op_payload in result.publish_operations or []:
                 publish_operation(self._amqp_url, queue_name, op_payload)
             channel.basic_ack(delivery_tag=method.delivery_tag)
             logger.info("Operation acked. operation=%s", operation_name)
@@ -253,11 +260,14 @@ def emit(event: str, message: str | None = None,
             if "CUDA out of memory" in str(exc):
                 try:
                     import torch
+
                     torch.cuda.empty_cache()
                 except Exception:
                     pass
                 requeue = True
-                logger.warning("CUDA OOM — cache cleared, message requeued. operation=%s", operation_name)
+                logger.warning(
+                    "CUDA OOM — cache cleared, message requeued. operation=%s", operation_name
+                )
             else:
                 logger.error("Operation failed. operation=%s error=%s", operation_name, exc)
             try:
diff --git a/protea/infrastructure/queue/publisher.py b/protea/infrastructure/queue/publisher.py
index a609afd..29a3985 100644
--- a/protea/infrastructure/queue/publisher.py
+++ b/protea/infrastructure/queue/publisher.py
@@ -40,13 +40,18 @@ def _publish(amqp_url: str, queue_name: str, body: bytes) -> None:
             if delay is not None:
                 logger.warning(
                     "publish failed (attempt %d), retrying in %ds. queue=%s error=%s",
-                    attempt, delay, queue_name, exc,
+                    attempt,
+                    delay,
+                    queue_name,
+                    exc,
                 )
                 time.sleep(delay)
             else:
                 logger.error(
                     "publish failed after %d attempts. queue=%s error=%s",
-                    attempt, queue_name, exc,
+                    attempt,
+                    queue_name,
+                    exc,
                 )
 
     raise RuntimeError(
diff --git a/protea/infrastructure/settings.py b/protea/infrastructure/settings.py
index 4c9687d..2c4ce9a 100644
--- a/protea/infrastructure/settings.py
+++ b/protea/infrastructure/settings.py
@@ -43,7 +43,9 @@ def load_settings(project_root: Path, *, env_prefix: str = "PROTEA_") -> Setting
         or file_db_url
         or "postgresql+psycopg://usuario:clave@localhost:5432/BioData"
     )
-    amqp_url = os.getenv(f"{env_prefix}AMQP_URL") or file_amqp_url or "amqp://guest:guest@localhost:5672/"
+    amqp_url = (
+        os.getenv(f"{env_prefix}AMQP_URL") or file_amqp_url or "amqp://guest:guest@localhost:5672/"
+    )
 
     raw_artifacts = (
         os.getenv(f"{env_prefix}ARTIFACTS_DIR")
diff --git a/protea/workers/base_worker.py b/protea/workers/base_worker.py
index 80f2ccf..b5d7323 100644
--- a/protea/workers/base_worker.py
+++ b/protea/workers/base_worker.py
@@ -14,7 +14,7 @@
 from protea.core.contracts.registry import OperationRegistry
 from protea.core.utils import utcnow
 from protea.infrastructure.orm.models.job import Job, JobEvent, JobStatus
-from protea.infrastructure.queue.publisher import publish_job
+from protea.infrastructure.queue.publisher import publish_job, publish_operation
 
 logger = logging.getLogger(__name__)
 
@@ -68,7 +68,14 @@ def handle_job(self, job_id: UUID) -> None:
 
             job.status = JobStatus.RUNNING
             job.started_at = utcnow()
-            self._emit(session, job_id, "job.started", None, {"worker": self._config.worker_name}, level="info")
+            self._emit(
+                session,
+                job_id,
+                "job.started",
+                None,
+                {"worker": self._config.worker_name},
+                level="info",
+            )
             session.commit()
         finally:
             session.close()
@@ -87,16 +94,24 @@ def handle_job(self, job_id: UUID) -> None:
                 if parent is not None and parent.status == JobStatus.CANCELLED:
                     job.status = JobStatus.CANCELLED
                     job.finished_at = utcnow()
-                    self._emit(session, job_id, "job.cancelled", None,
-                               {"reason": "parent_cancelled"}, level="info")
+                    self._emit(
+                        session,
+                        job_id,
+                        "job.cancelled",
+                        None,
+                        {"reason": "parent_cancelled"},
+                        level="info",
+                    )
                     session.commit()
                     return
 
             op = self._registry.get(job.operation)
 
             def emit(
-                event: str, message: str | None = None,
-                fields: dict[str, Any] | None = None, level: str = "info"
+                event: str,
+                message: str | None = None,
+                fields: dict[str, Any] | None = None,
+                level: str = "info",
             ) -> None:
                 # Dedicated short-lived session that commits immediately so
                 # events are visible in real time, not just at job completion.
@@ -128,11 +143,25 @@ def emit(
 
                 if result.deferred:
                     # Coordinator job: children will mark it SUCCEEDED when done.
-                    self._emit(session, job_id, "job.dispatched", None, {"result": result.result}, level="info")
+                    self._emit(
+                        session,
+                        job_id,
+                        "job.dispatched",
+                        None,
+                        {"result": result.result},
+                        level="info",
+                    )
                 else:
                     job.status = JobStatus.SUCCEEDED
                     job.finished_at = utcnow()
-                    self._emit(session, job_id, "job.succeeded", None, {"result": result.result}, level="info")
+                    self._emit(
+                        session,
+                        job_id,
+                        "job.succeeded",
+                        None,
+                        {"result": result.result},
+                        level="info",
+                    )
 
                 session.commit()
 
@@ -141,12 +170,23 @@ def emit(
                     for queue_name, child_job_id in result.publish_after_commit:
                         publish_job(self._amqp_url, queue_name, child_job_id)
 
+                # Publish ephemeral operation messages (e.g. embedding batches).
+                if result.publish_operations and self._amqp_url:
+                    for queue_name, op_payload in result.publish_operations:
+                        publish_operation(self._amqp_url, queue_name, op_payload)
+
             except RetryLaterError as e:
                 # Resource busy — reset to QUEUED so the consumer can re-publish.
                 job.status = JobStatus.QUEUED
                 job.started_at = None
-                self._emit(session, job_id, "job.retry_later", str(e),
-                           {"delay_seconds": e.delay_seconds}, level="info")
+                self._emit(
+                    session,
+                    job_id,
+                    "job.retry_later",
+                    str(e),
+                    {"delay_seconds": e.delay_seconds},
+                    level="info",
+                )
                 session.commit()
                 raise  # consumer handles re-publish
 
@@ -155,7 +195,14 @@ def emit(
                 job.finished_at = utcnow()
                 job.error_code = e.__class__.__name__
                 job.error_message = str(e)
-                self._emit(session, job_id, "job.failed", str(e), {"error_code": job.error_code}, level="error")
+                self._emit(
+                    session,
+                    job_id,
+                    "job.failed",
+                    str(e),
+                    {"error_code": job.error_code},
+                    level="error",
+                )
                 if job.parent_job_id is not None:
                     self._maybe_fail_parent(session, job.parent_job_id)
                 try:
@@ -166,7 +213,8 @@ def emit(
                     # left permanently stuck in RUNNING.
                     logger.error(
                         "Execute session commit failed; using fallback session. job_id=%s error=%s",
-                        job_id, commit_exc,
+                        job_id,
+                        commit_exc,
                     )
                     self._force_fail_job(job_id, e)
                 raise
@@ -196,7 +244,8 @@ def _force_fail_job(self, job_id: UUID, original_exc: Exception) -> None:
         except Exception as exc:
             logger.error(
                 "Fallback session also failed; job may remain RUNNING. job_id=%s error=%s",
-                job_id, exc,
+                job_id,
+                exc,
             )
         finally:
             fallback.close()
@@ -231,9 +280,14 @@ def _maybe_fail_parent(self, session: Session, parent_job_id: UUID) -> None:
                 error_message="All child jobs failed or were cancelled",
             )
         )
-        self._emit(session, parent_job_id, "job.failed",
-                   "All child jobs failed or were cancelled",
-                   {"reason": "all_children_failed"}, level="error")
+        self._emit(
+            session,
+            parent_job_id,
+            "job.failed",
+            "All child jobs failed or were cancelled",
+            {"reason": "all_children_failed"},
+            level="error",
+        )
 
     @staticmethod
     def _emit(
@@ -245,4 +299,6 @@ def _emit(
         *,
         level: str,
     ) -> None:
-        session.add(JobEvent(job_id=job_id, event=event, message=message, fields=fields, level=level))
+        session.add(
+            JobEvent(job_id=job_id, event=event, message=message, fields=fields, level=level)
+        )
diff --git a/pyproject.toml b/pyproject.toml
index 1ccb793..e7438be 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "protea"
-version = "0.1.0"
+version = "0.2.0"
 description = ""
 authors = [
     {name = "frapercan",email = "frapercan1@alum.us.es"}
diff --git a/scripts/manage.sh b/scripts/manage.sh
index 0974502..020a8e3 100755
--- a/scripts/manage.sh
+++ b/scripts/manage.sh
@@ -22,7 +22,7 @@ CYAN="\033[36m"; BOLD="\033[1m"; RESET="\033[0m"
 _start_bg() {
     local name="$1"; shift
     mkdir -p "$LOG_DIR" "$PID_DIR"
-    "$@" >> "$LOG_DIR/${name}.log" 2>&1 &
+    setsid "$@" >> "$LOG_DIR/${name}.log" 2>&1 &
     local pid=$!
     echo "$pid" > "$PID_DIR/${name}.pid"
     printf "  ${GREEN}✓${RESET} %-35s PID %s\n" "$name" "$pid"
@@ -33,7 +33,8 @@ _stop_pid() {
     if [[ -f "$pidfile" ]]; then
         local pid; pid=$(cat "$pidfile")
         if kill -0 "$pid" 2>/dev/null; then
-            kill -15 "$pid" 2>/dev/null   # SIGTERM — lets current job finish
+            # Kill the whole process group (setsid guarantees PID == PGID)
+            kill -15 -- -"$pid" 2>/dev/null || kill -15 "$pid" 2>/dev/null
             printf "  ${RED}✗${RESET} %-35s stopping (PID %s) — SIGTERM sent\n" "$name" "$pid"
         fi
         rm -f "$pidfile"
@@ -137,7 +138,8 @@ cmd_stop() {
 
     # Also catch any untracked worker.py processes (manual launches etc.)
     while IFS= read -r pid; do
-        kill -15 "$pid" 2>/dev/null && worker_pids+=("$pid")
+        kill -15 -- -"$pid" 2>/dev/null || kill -15 "$pid" 2>/dev/null
+        worker_pids+=("$pid")
     done < <(pgrep -f "scripts/worker.py" 2>/dev/null || true)
 
     # Force-kill API and frontend immediately (no long-running state)
@@ -146,14 +148,14 @@ cmd_stop() {
 
     # Wait up to 60 s for workers to finish current job, then force-kill
     if [[ ${#worker_pids[@]} -gt 0 ]]; then
-        printf "  Waiting up to 60s for workers to finish current jobs...\n"
-        local deadline=$(( $(date +%s) + 60 ))
+        printf "  Waiting up to 5s for workers to finish current jobs...\n"
+        local deadline=$(( $(date +%s) + 5 ))
         for pid in "${worker_pids[@]}"; do
             while kill -0 "$pid" 2>/dev/null && (( $(date +%s) < deadline )); do
                 sleep 2
             done
             if kill -0 "$pid" 2>/dev/null; then
-                kill -9 "$pid" 2>/dev/null
+                kill -9 -- -"$pid" 2>/dev/null || kill -9 "$pid" 2>/dev/null
                 printf "  ${YELLOW}⚠${RESET}  PID %s force-killed (job still running)\n" "$pid"
             fi
         done
diff --git a/tests/test_core.py b/tests/test_core.py
index 95f82ee..b995518 100644
--- a/tests/test_core.py
+++ b/tests/test_core.py
@@ -182,3 +182,70 @@ def test_extract_next_cursor_absent(self) -> None:
     def test_extract_next_cursor_no_cursor_param(self) -> None:
         obj = self._obj()
         assert obj._extract_next_cursor('<http://x?page=2>; rel="next"') is None
+
+
+# ---------------------------------------------------------------------------
+# evidence_codes — normalize and is_experimental
+# ---------------------------------------------------------------------------
+
+from protea.core.evidence_codes import normalize, is_experimental, ECO_TO_CODE, EXPERIMENTAL
+
+
+class TestNormalize:
+    def test_go_code_passthrough(self):
+        assert normalize("IDA") == "IDA"
+
+    def test_eco_id_to_go_code(self):
+        assert normalize("ECO:0000314") == "IDA"
+        assert normalize("ECO:0000501") == "IEA"
+
+    def test_unknown_code_passthrough(self):
+        assert normalize("UNKNOWN_CODE") == "UNKNOWN_CODE"
+
+    def test_all_eco_ids_resolve(self):
+        for eco, expected in ECO_TO_CODE.items():
+            assert normalize(eco) == expected
+
+
+class TestIsExperimental:
+    def test_experimental_go_code(self):
+        for code in EXPERIMENTAL:
+            assert is_experimental(code) is True
+
+    def test_non_experimental_code(self):
+        assert is_experimental("IEA") is False
+        assert is_experimental("ISS") is False
+        assert is_experimental("ND") is False
+
+    def test_eco_experimental(self):
+        # ECO:0000314 → IDA → experimental
+        assert is_experimental("ECO:0000314") is True
+
+    def test_eco_non_experimental(self):
+        # ECO:0000501 → IEA → not experimental
+        assert is_experimental("ECO:0000501") is False
+
+    def test_unknown_code_not_experimental(self):
+        assert is_experimental("BADCODE") is False
+
+
+# ---------------------------------------------------------------------------
+# RetryLaterError
+# ---------------------------------------------------------------------------
+
+from protea.core.contracts.operation import RetryLaterError
+
+
+class TestRetryLaterError:
+    def test_default_delay(self):
+        err = RetryLaterError("GPU busy")
+        assert err.delay_seconds == 60
+        assert str(err) == "GPU busy"
+
+    def test_custom_delay(self):
+        err = RetryLaterError("busy", delay_seconds=120)
+        assert err.delay_seconds == 120
+
+    def test_is_exception(self):
+        with pytest.raises(RetryLaterError):
+            raise RetryLaterError("test")
diff --git a/tests/test_evaluation.py b/tests/test_evaluation.py
new file mode 100644
index 0000000..f88a759
--- /dev/null
+++ b/tests/test_evaluation.py
@@ -0,0 +1,143 @@
+"""Tests for protea.core.evaluation — pure-Python components."""
+import pytest
+
+from protea.core.evaluation import EvaluationData, _get_descendants
+
+
+# ---------------------------------------------------------------------------
+# EvaluationData — dataclass properties
+# ---------------------------------------------------------------------------
+
+class TestEvaluationDataProperties:
+    def _make(self, nk=None, lk=None, pk=None, known=None, pk_known=None):
+        return EvaluationData(
+            nk=nk or {},
+            lk=lk or {},
+            pk=pk or {},
+            known=known or {},
+            pk_known=pk_known or {},
+        )
+
+    def test_nk_proteins_count(self):
+        ed = self._make(nk={"P1": {"GO:0001"}, "P2": {"GO:0002"}})
+        assert ed.nk_proteins == 2
+
+    def test_lk_proteins_count(self):
+        ed = self._make(lk={"P3": {"GO:0003"}})
+        assert ed.lk_proteins == 1
+
+    def test_pk_proteins_count(self):
+        ed = self._make(pk={"P4": {"GO:0004"}, "P5": {"GO:0005"}})
+        assert ed.pk_proteins == 2
+
+    def test_nk_annotations_count(self):
+        ed = self._make(nk={"P1": {"GO:0001", "GO:0002"}, "P2": {"GO:0003"}})
+        assert ed.nk_annotations == 3
+
+    def test_lk_annotations_count(self):
+        ed = self._make(lk={"P1": {"GO:0001"}, "P2": {"GO:0002", "GO:0003"}})
+        assert ed.lk_annotations == 3
+
+    def test_pk_annotations_count(self):
+        ed = self._make(pk={"P1": {"GO:0001", "GO:0002"}})
+        assert ed.pk_annotations == 2
+
+    def test_known_terms_count(self):
+        ed = self._make(known={"P1": {"GO:0001"}, "P2": {"GO:0002", "GO:0003"}})
+        assert ed.known_terms_count == 3
+
+    def test_delta_proteins_union(self):
+        ed = self._make(
+            nk={"P1": {"GO:0001"}},
+            lk={"P2": {"GO:0002"}},
+            pk={"P3": {"GO:0003"}},
+        )
+        assert ed.delta_proteins == 3
+
+    def test_delta_proteins_overlapping(self):
+        # Same protein can appear in lk and pk for different namespaces
+        ed = self._make(
+            nk={},
+            lk={"P1": {"GO:0001"}},
+            pk={"P1": {"GO:0002"}},
+        )
+        assert ed.delta_proteins == 1
+
+    def test_empty_data(self):
+        ed = self._make()
+        assert ed.nk_proteins == 0
+        assert ed.lk_proteins == 0
+        assert ed.pk_proteins == 0
+        assert ed.nk_annotations == 0
+        assert ed.lk_annotations == 0
+        assert ed.pk_annotations == 0
+        assert ed.known_terms_count == 0
+        assert ed.delta_proteins == 0
+
+    def test_stats_dict_keys(self):
+        ed = self._make()
+        s = ed.stats()
+        expected = {
+            "delta_proteins", "nk_proteins", "lk_proteins", "pk_proteins",
+            "nk_annotations", "lk_annotations", "pk_annotations", "known_terms_count",
+        }
+        assert set(s.keys()) == expected
+
+    def test_stats_dict_values(self):
+        ed = self._make(
+            nk={"P1": {"GO:0001", "GO:0002"}},
+            lk={"P2": {"GO:0003"}},
+            pk={"P3": {"GO:0004"}},
+            known={"P1": {"GO:0010"}},
+        )
+        s = ed.stats()
+        assert s["nk_proteins"] == 1
+        assert s["lk_proteins"] == 1
+        assert s["pk_proteins"] == 1
+        assert s["nk_annotations"] == 2
+        assert s["lk_annotations"] == 1
+        assert s["pk_annotations"] == 1
+        assert s["known_terms_count"] == 1
+        assert s["delta_proteins"] == 3
+
+
+# ---------------------------------------------------------------------------
+# _get_descendants — BFS over GO DAG
+# ---------------------------------------------------------------------------
+
+class TestGetDescendants:
+    def test_no_children(self):
+        result = _get_descendants(1, {})
+        assert result == set()
+
+    def test_direct_children(self):
+        children_map = {1: {2, 3}}
+        result = _get_descendants(1, children_map)
+        assert result == {2, 3}
+
+    def test_transitive_descendants(self):
+        children_map = {1: {2}, 2: {3}, 3: {4}}
+        result = _get_descendants(1, children_map)
+        assert result == {2, 3, 4}
+
+    def test_diamond_dag_no_duplicate(self):
+        # 1 → 2, 3; 2 → 4; 3 → 4
+        children_map = {1: {2, 3}, 2: {4}, 3: {4}}
+        result = _get_descendants(1, children_map)
+        assert result == {2, 3, 4}
+
+    def test_cycle_safe(self):
+        # Shouldn't happen in GO but must not infinite-loop
+        children_map = {1: {2}, 2: {3}, 3: {1}}
+        result = _get_descendants(1, children_map)
+        assert 2 in result and 3 in result
+
+    def test_start_term_not_in_result(self):
+        children_map = {1: {2}}
+        result = _get_descendants(1, children_map)
+        assert 1 not in result
+
+    def test_leaf_node(self):
+        children_map = {1: {2}, 2: set()}
+        result = _get_descendants(1, children_map)
+        assert result == {2}
diff --git a/tests/test_generate_evaluation_set.py b/tests/test_generate_evaluation_set.py
new file mode 100644
index 0000000..8b54033
--- /dev/null
+++ b/tests/test_generate_evaluation_set.py
@@ -0,0 +1,148 @@
+"""Unit tests for GenerateEvaluationSetOperation — DB mocked."""
+from __future__ import annotations
+
+import uuid
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+from protea.core.operations.generate_evaluation_set import (
+    GenerateEvaluationSetOperation,
+    GenerateEvaluationSetPayload,
+)
+from protea.core.evaluation import EvaluationData
+
+
+# ---------------------------------------------------------------------------
+# Payload validator
+# ---------------------------------------------------------------------------
+
+class TestGenerateEvaluationSetPayload:
+    def test_valid_uuids(self):
+        old = str(uuid.uuid4())
+        new = str(uuid.uuid4())
+        p = GenerateEvaluationSetPayload(old_annotation_set_id=old, new_annotation_set_id=new)
+        assert p.old_annotation_set_id == old
+        assert p.new_annotation_set_id == new
+
+    def test_empty_old_raises(self):
+        with pytest.raises(Exception):
+            GenerateEvaluationSetPayload(old_annotation_set_id="  ", new_annotation_set_id=str(uuid.uuid4()))
+
+    def test_empty_new_raises(self):
+        with pytest.raises(Exception):
+            GenerateEvaluationSetPayload(old_annotation_set_id=str(uuid.uuid4()), new_annotation_set_id="")
+
+    def test_strips_whitespace(self):
+        uid = str(uuid.uuid4())
+        p = GenerateEvaluationSetPayload(
+            old_annotation_set_id=f"  {uid}  ",
+            new_annotation_set_id=uid,
+        )
+        assert p.old_annotation_set_id == uid
+
+
+# ---------------------------------------------------------------------------
+# Operation execute — mocked session
+# ---------------------------------------------------------------------------
+
+def _make_annotation_set(snapshot_id: uuid.UUID) -> MagicMock:
+    s = MagicMock()
+    s.ontology_snapshot_id = snapshot_id
+    return s
+
+
+def _make_eval_data() -> EvaluationData:
+    return EvaluationData(
+        nk={"P1": {"GO:0001"}},
+        lk={"P2": {"GO:0002"}},
+        pk={},
+    )
+
+
+class TestGenerateEvaluationSetExecute:
+    def setup_method(self):
+        self.op = GenerateEvaluationSetOperation()
+        self.emit = MagicMock()
+
+    def _payload(self, old_id=None, new_id=None):
+        return {
+            "old_annotation_set_id": str(old_id or uuid.uuid4()),
+            "new_annotation_set_id": str(new_id or uuid.uuid4()),
+        }
+
+    def test_old_set_not_found_raises(self):
+        session = MagicMock()
+        session.get.return_value = None
+        with pytest.raises(ValueError, match="not found"):
+            self.op.execute(session, self._payload(), emit=self.emit)
+
+    def test_new_set_not_found_raises(self):
+        session = MagicMock()
+        snap_id = uuid.uuid4()
+        old_set = _make_annotation_set(snap_id)
+        session.get.side_effect = [old_set, None]
+        with pytest.raises(ValueError, match="not found"):
+            self.op.execute(session, self._payload(), emit=self.emit)
+
+    def test_different_snapshot_raises(self):
+        session = MagicMock()
+        old_set = _make_annotation_set(uuid.uuid4())
+        new_set = _make_annotation_set(uuid.uuid4())  # different snapshot
+        session.get.side_effect = [old_set, new_set]
+        with pytest.raises(ValueError, match="same ontology snapshot"):
+            self.op.execute(session, self._payload(), emit=self.emit)
+
+    def test_successful_execution(self):
+        session = MagicMock()
+        snap_id = uuid.uuid4()
+        old_set = _make_annotation_set(snap_id)
+        new_set = _make_annotation_set(snap_id)
+        session.get.side_effect = [old_set, new_set]
+
+        eval_set = MagicMock()
+        eval_set.id = uuid.uuid4()
+
+        def add_side(obj):
+            obj.id = eval_set.id
+
+        session.add.side_effect = add_side
+        session.flush = MagicMock()
+
+        eval_data = _make_eval_data()
+
+        with patch(
+            "protea.core.operations.generate_evaluation_set.compute_evaluation_data",
+            return_value=eval_data,
+        ):
+            result = self.op.execute(session, self._payload(), emit=self.emit)
+
+        assert "evaluation_set_id" in result.result
+        assert result.result["nk_proteins"] == 1
+        assert result.result["lk_proteins"] == 1
+        assert self.emit.call_count >= 3  # start, computing, done
+
+    def test_emits_start_event(self):
+        session = MagicMock()
+        snap_id = uuid.uuid4()
+        old_set = _make_annotation_set(snap_id)
+        new_set = _make_annotation_set(snap_id)
+        session.get.side_effect = [old_set, new_set]
+
+        eval_set = MagicMock()
+
+        def add_side(obj):
+            obj.id = uuid.uuid4()
+
+        session.add.side_effect = add_side
+        session.flush = MagicMock()
+
+        with patch(
+            "protea.core.operations.generate_evaluation_set.compute_evaluation_data",
+            return_value=_make_eval_data(),
+        ):
+            self.op.execute(session, self._payload(), emit=self.emit)
+
+        events = [call.args[0] for call in self.emit.call_args_list]
+        assert "generate_evaluation_set.start" in events
+        assert "generate_evaluation_set.done" in events
diff --git a/tests/test_metrics.py b/tests/test_metrics.py
new file mode 100644
index 0000000..83abe54
--- /dev/null
+++ b/tests/test_metrics.py
@@ -0,0 +1,153 @@
+"""Unit tests for protea.core.metrics — pure-Python, no DB."""
+from __future__ import annotations
+
+import pytest
+
+from protea.core.evaluation import EvaluationData
+from protea.core.metrics import CAFAMetrics, PRPoint, compute_cafa_metrics
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+def _make_eval(nk=None, lk=None):
+    return EvaluationData(
+        nk=nk or {},
+        lk=lk or {},
+        pk={},
+    )
+
+
+def _pred(acc, go_id, score):
+    return {"protein_accession": acc, "go_id": go_id, "score": score}
+
+
+# ---------------------------------------------------------------------------
+# PRPoint / CAFAMetrics dataclasses
+# ---------------------------------------------------------------------------
+
+class TestDataclasses:
+    def test_prpoint_fields(self):
+        p = PRPoint(threshold=0.5, precision=0.8, recall=0.6, f1=0.686)
+        assert p.threshold == 0.5
+        assert p.precision == 0.8
+        assert p.recall == 0.6
+        assert p.f1 == 0.686
+
+    def test_cafa_metrics_summary_keys(self):
+        m = CAFAMetrics(
+            category="nk",
+            fmax=0.75,
+            threshold_at_fmax=0.3,
+            auc_pr=0.5,
+            n_ground_truth_proteins=10,
+            n_predicted_proteins=8,
+            n_predictions=50,
+        )
+        s = m.summary()
+        assert set(s.keys()) == {
+            "category", "fmax", "threshold_at_fmax", "auc_pr",
+            "n_ground_truth_proteins", "n_predicted_proteins", "n_predictions",
+        }
+        assert s["fmax"] == 0.75
+
+
+# ---------------------------------------------------------------------------
+# compute_cafa_metrics — validation
+# ---------------------------------------------------------------------------
+
+class TestComputeCafaMetricsValidation:
+    def test_invalid_category_raises(self):
+        with pytest.raises(ValueError, match="category"):
+            compute_cafa_metrics([], _make_eval(), category="pk")
+
+    def test_valid_nk_category(self):
+        result = compute_cafa_metrics([], _make_eval(nk={"P1": {"GO:0001"}}), category="nk")
+        assert result.category == "nk"
+
+    def test_valid_lk_category(self):
+        result = compute_cafa_metrics([], _make_eval(lk={"P1": {"GO:0001"}}), category="lk")
+        assert result.category == "lk"
+
+
+# ---------------------------------------------------------------------------
+# compute_cafa_metrics — empty / no predictions
+# ---------------------------------------------------------------------------
+
+class TestComputeCafaMetricsEmpty:
+    def test_empty_ground_truth_and_preds(self):
+        result = compute_cafa_metrics([], _make_eval())
+        assert result.fmax == 0.0
+        assert result.auc_pr == 0.0
+        assert result.n_ground_truth_proteins == 0
+        assert result.n_predictions == 0
+
+    def test_no_predictions_fmax_zero(self):
+        eval_data = _make_eval(nk={"P1": {"GO:0001"}, "P2": {"GO:0002"}})
+        result = compute_cafa_metrics([], eval_data)
+        assert result.fmax == 0.0
+        assert result.n_predicted_proteins == 0
+        assert result.n_ground_truth_proteins == 2
+
+    def test_curve_has_101_points(self):
+        result = compute_cafa_metrics([], _make_eval(nk={"P1": {"GO:0001"}}))
+        assert len(result.curve) == 101
+
+
+# ---------------------------------------------------------------------------
+# compute_cafa_metrics — correctness
+# ---------------------------------------------------------------------------
+
+class TestComputeCafaMetricsCorrectness:
+    def test_perfect_prediction_fmax_one(self):
+        """Protein with one GO term predicted at score 1.0 → Fmax == 1.0."""
+        eval_data = _make_eval(nk={"P1": {"GO:0001"}})
+        preds = [_pred("P1", "GO:0001", 1.0)]
+        result = compute_cafa_metrics(preds, eval_data)
+        assert result.fmax == 1.0
+
+    def test_wrong_term_fmax_zero(self):
+        eval_data = _make_eval(nk={"P1": {"GO:0001"}})
+        preds = [_pred("P1", "GO:9999", 1.0)]
+        result = compute_cafa_metrics(preds, eval_data)
+        assert result.fmax == 0.0
+
+    def test_protein_outside_ground_truth_ignored(self):
+        eval_data = _make_eval(nk={"P1": {"GO:0001"}})
+        preds = [_pred("OTHER", "GO:0001", 1.0)]
+        result = compute_cafa_metrics(preds, eval_data)
+        # OTHER is not in ground truth — should be ignored
+        assert result.n_predicted_proteins == 0
+
+    def test_partial_prediction(self):
+        """P1 has 2 true terms; predict only one → F1 < 1."""
+        eval_data = _make_eval(nk={"P1": {"GO:0001", "GO:0002"}})
+        preds = [_pred("P1", "GO:0001", 0.9)]
+        result = compute_cafa_metrics(preds, eval_data)
+        assert 0 < result.fmax < 1.0
+
+    def test_multiple_proteins(self):
+        eval_data = _make_eval(nk={
+            "P1": {"GO:0001"},
+            "P2": {"GO:0002"},
+        })
+        preds = [_pred("P1", "GO:0001", 0.9), _pred("P2", "GO:0002", 0.9)]
+        result = compute_cafa_metrics(preds, eval_data)
+        assert result.fmax > 0.5
+
+    def test_n_predictions_counts_all(self):
+        eval_data = _make_eval(nk={"P1": {"GO:0001"}})
+        preds = [_pred("P1", "GO:0001", 0.9), _pred("OTHER", "GO:0002", 0.5)]
+        result = compute_cafa_metrics(preds, eval_data)
+        assert result.n_predictions == 2
+
+    def test_lk_uses_lk_ground_truth(self):
+        eval_data = _make_eval(
+            nk={"P1": {"GO:0001"}},
+            lk={"P2": {"GO:0002"}},
+        )
+        preds = [_pred("P2", "GO:0002", 1.0)]
+        result = compute_cafa_metrics(preds, eval_data, category="lk")
+        assert result.fmax == 1.0
+        assert result.category == "lk"
diff --git a/tests/test_scoring.py b/tests/test_scoring.py
new file mode 100644
index 0000000..347afeb
--- /dev/null
+++ b/tests/test_scoring.py
@@ -0,0 +1,218 @@
+"""Tests for protea.core.scoring and related evidence weight resolution."""
+import pytest
+from unittest.mock import MagicMock
+
+from protea.core.scoring import compute_score, evidence_weight, score_predictions
+from protea.infrastructure.orm.models.embedding.scoring_config import (
+    DEFAULT_EVIDENCE_WEIGHT_FALLBACK,
+    DEFAULT_EVIDENCE_WEIGHTS,
+    FORMULA_EVIDENCE_WEIGHTED,
+    FORMULA_LINEAR,
+    ScoringConfig,
+)
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+def _config(
+    weights: dict,
+    formula: str = FORMULA_LINEAR,
+    evidence_weights: dict | None = None,
+) -> ScoringConfig:
+    cfg = MagicMock(spec=ScoringConfig)
+    cfg.weights = weights
+    cfg.formula = formula
+    cfg.evidence_weights = evidence_weights
+    return cfg
+
+
+# ---------------------------------------------------------------------------
+# evidence_weight
+# ---------------------------------------------------------------------------
+
+class TestEvidenceWeight:
+    def test_none_code_returns_fallback(self):
+        assert evidence_weight(None) == DEFAULT_EVIDENCE_WEIGHT_FALLBACK
+
+    def test_empty_string_returns_fallback(self):
+        assert evidence_weight("") == DEFAULT_EVIDENCE_WEIGHT_FALLBACK
+
+    def test_known_experimental_code(self):
+        assert evidence_weight("IDA") == 1.0
+
+    def test_known_electronic_code(self):
+        assert evidence_weight("IEA") == 0.3
+
+    def test_known_computational_code(self):
+        assert evidence_weight("IBA") == 0.7
+
+    def test_unknown_code_returns_fallback(self):
+        assert evidence_weight("UNKNOWN") == DEFAULT_EVIDENCE_WEIGHT_FALLBACK
+
+    def test_override_takes_precedence(self):
+        assert evidence_weight("IEA", overrides={"IEA": 0.0}) == 0.0
+
+    def test_partial_override_fallback_to_default(self):
+        # IDA not in overrides → falls back to DEFAULT_EVIDENCE_WEIGHTS
+        assert evidence_weight("IDA", overrides={"IEA": 0.0}) == 1.0
+
+    def test_eco_id_normalized(self):
+        # ECO:0000501 maps to IEA
+        from protea.core.evidence_codes import ECO_TO_CODE
+        eco_ids = [eco for eco, go in ECO_TO_CODE.items() if go == "IEA"]
+        if eco_ids:
+            assert evidence_weight(eco_ids[0]) == pytest.approx(0.3)
+
+    def test_override_with_none_overrides_arg(self):
+        # overrides=None should not crash
+        assert evidence_weight("IDA", overrides=None) == 1.0
+
+
+# ---------------------------------------------------------------------------
+# compute_score — pure embedding
+# ---------------------------------------------------------------------------
+
+class TestComputeScoreEmbeddingOnly:
+    def setup_method(self):
+        self.cfg = _config({"embedding_similarity": 1.0})
+
+    def test_zero_distance_gives_one(self):
+        score = compute_score({"distance": 0.0}, self.cfg)
+        assert score == pytest.approx(1.0)
+
+    def test_max_distance_gives_zero(self):
+        score = compute_score({"distance": 2.0}, self.cfg)
+        assert score == pytest.approx(0.0)
+
+    def test_mid_distance(self):
+        score = compute_score({"distance": 1.0}, self.cfg)
+        assert score == pytest.approx(0.5)
+
+    def test_no_signals_returns_zero(self):
+        cfg = _config({})
+        score = compute_score({"distance": 0.5}, cfg)
+        assert score == 0.0
+
+    def test_missing_distance_ignored(self):
+        score = compute_score({}, self.cfg)
+        assert score == 0.0
+
+    def test_score_rounded_to_6_decimals(self):
+        score = compute_score({"distance": 0.3333333}, self.cfg)
+        assert len(str(score).split(".")[-1]) <= 6
+
+
+# ---------------------------------------------------------------------------
+# compute_score — multi-signal
+# ---------------------------------------------------------------------------
+
+class TestComputeScoreMultiSignal:
+    def test_nw_identity_contributes(self):
+        cfg = _config({"embedding_similarity": 0.5, "identity_nw": 0.5})
+        pred = {"distance": 0.0, "identity_nw": 0.5}
+        score = compute_score(pred, cfg)
+        # embedding=1.0*0.5, nw=0.5*0.5 → (0.5+0.25)/1.0 = 0.75
+        assert score == pytest.approx(0.75)
+
+    def test_sw_identity_contributes(self):
+        cfg = _config({"embedding_similarity": 0.5, "identity_sw": 0.5})
+        pred = {"distance": 0.0, "identity_sw": 1.0}
+        score = compute_score(pred, cfg)
+        # embedding=1.0*0.5, sw=1.0*0.5 → (0.5+0.5)/1.0 = 1.0
+        assert score == pytest.approx(1.0)
+
+    def test_none_signal_excluded_from_denominator(self):
+        cfg = _config({"embedding_similarity": 1.0, "identity_nw": 1.0})
+        # identity_nw is None → only embedding_similarity contributes
+        pred = {"distance": 0.0, "identity_nw": None}
+        score = compute_score(pred, cfg)
+        assert score == pytest.approx(1.0)
+
+    def test_taxonomic_proximity_zero_distance(self):
+        cfg = _config({"taxonomic_proximity": 1.0})
+        pred = {"taxonomic_distance": 0.0}
+        score = compute_score(pred, cfg)
+        assert score == pytest.approx(1.0)
+
+    def test_taxonomic_proximity_large_distance(self):
+        cfg = _config({"taxonomic_proximity": 1.0})
+        pred = {"taxonomic_distance": 999.0}
+        score = compute_score(pred, cfg)
+        assert 0.0 < score < 0.01
+
+    def test_evidence_weight_signal(self):
+        cfg = _config({"evidence_weight": 1.0})
+        score_exp = compute_score({"evidence_code": "IDA"}, cfg)
+        score_iea = compute_score({"evidence_code": "IEA"}, cfg)
+        assert score_exp > score_iea
+
+    def test_signal_clamped_to_zero(self):
+        cfg = _config({"identity_nw": 1.0})
+        # negative value should be clamped to 0
+        score = compute_score({"identity_nw": -0.5}, cfg)
+        assert score == pytest.approx(0.0)
+
+    def test_signal_clamped_to_one(self):
+        cfg = _config({"identity_nw": 1.0})
+        score = compute_score({"identity_nw": 1.5}, cfg)
+        assert score == pytest.approx(1.0)
+
+
+# ---------------------------------------------------------------------------
+# compute_score — evidence_weighted formula
+# ---------------------------------------------------------------------------
+
+class TestComputeScoreEvidenceWeighted:
+    def test_iea_downgrades_score(self):
+        cfg_linear = _config({"embedding_similarity": 1.0}, formula=FORMULA_LINEAR)
+        cfg_evw = _config({"embedding_similarity": 1.0}, formula=FORMULA_EVIDENCE_WEIGHTED)
+        pred = {"distance": 0.0, "evidence_code": "IEA"}
+        score_linear = compute_score(pred, cfg_linear)
+        score_evw = compute_score(pred, cfg_evw)
+        assert score_evw < score_linear
+
+    def test_experimental_code_not_penalized(self):
+        cfg = _config({"embedding_similarity": 1.0}, formula=FORMULA_EVIDENCE_WEIGHTED)
+        pred = {"distance": 0.0, "evidence_code": "IDA"}
+        score = compute_score(pred, cfg)
+        assert score == pytest.approx(1.0)
+
+    def test_custom_evidence_override_applied(self):
+        cfg = _config(
+            {"embedding_similarity": 1.0},
+            formula=FORMULA_EVIDENCE_WEIGHTED,
+            evidence_weights={"IEA": 0.0},
+        )
+        pred = {"distance": 0.0, "evidence_code": "IEA"}
+        score = compute_score(pred, cfg)
+        assert score == pytest.approx(0.0)
+
+
+# ---------------------------------------------------------------------------
+# score_predictions
+# ---------------------------------------------------------------------------
+
+class TestScorePredictions:
+    def setup_method(self):
+        self.cfg = _config({"embedding_similarity": 1.0})
+
+    def test_returns_list_with_score_key(self):
+        preds = [{"distance": 0.5}, {"distance": 0.2}]
+        result = score_predictions(preds, self.cfg)
+        assert all("score" in r for r in result)
+
+    def test_sorted_descending(self):
+        preds = [{"distance": 1.0}, {"distance": 0.1}, {"distance": 0.5}]
+        result = score_predictions(preds, self.cfg)
+        scores = [r["score"] for r in result]
+        assert scores == sorted(scores, reverse=True)
+
+    def test_original_list_not_modified(self):
+        preds = [{"distance": 0.5}]
+        score_predictions(preds, self.cfg)
+        assert "score" not in preds[0]
+
+    def test_empty_list(self):
+        assert score_predictions([], self.cfg) == []
diff --git a/tests/test_scoring_router.py b/tests/test_scoring_router.py
new file mode 100644
index 0000000..eb037e6
--- /dev/null
+++ b/tests/test_scoring_router.py
@@ -0,0 +1,262 @@
+"""Unit tests for the scoring API router — no real DB required."""
+from __future__ import annotations
+
+from contextlib import contextmanager
+from datetime import datetime, timezone
+from unittest.mock import MagicMock, patch
+from uuid import uuid4
+
+import pytest
+from fastapi import FastAPI
+from fastapi.testclient import TestClient
+
+from protea.api.routers.scoring import router
+from protea.infrastructure.orm.models.embedding.scoring_config import (
+    FORMULA_LINEAR,
+    ScoringConfig,
+)
+
+
+# ---------------------------------------------------------------------------
+# Fixtures
+# ---------------------------------------------------------------------------
+
+def _make_config(name="test", formula=FORMULA_LINEAR, weights=None, ev_weights=None):
+    cfg = MagicMock(spec=ScoringConfig)
+    cfg.id = uuid4()
+    cfg.name = name
+    cfg.formula = formula
+    cfg.weights = weights or {"embedding_similarity": 1.0}
+    cfg.evidence_weights = ev_weights
+    cfg.description = None
+    cfg.created_at = datetime(2026, 1, 1, tzinfo=timezone.utc)
+    return cfg
+
+
+@contextmanager
+def _mock_scope(session):
+    yield session
+
+
+def _make_app(session):
+    app = FastAPI()
+    factory = MagicMock()
+    app.state.session_factory = factory
+    app.include_router(router)
+    return app, factory, session
+
+
+@pytest.fixture()
+def session():
+    return MagicMock()
+
+
+@pytest.fixture()
+def client(session):
+    app, factory, _ = _make_app(session)
+    with patch("protea.api.routers.scoring.session_scope", side_effect=lambda _: _mock_scope(session)):
+        yield TestClient(app, raise_server_exceptions=True)
+
+
+# ---------------------------------------------------------------------------
+# GET /configs
+# ---------------------------------------------------------------------------
+
+class TestListScoringConfigs:
+    def test_empty_list(self, client, session):
+        session.query.return_value.order_by.return_value.all.return_value = []
+        resp = client.get("/scoring/configs")
+        assert resp.status_code == 200
+        assert resp.json() == []
+
+    def test_returns_configs(self, client, session):
+        cfg = _make_config("my-config")
+        session.query.return_value.order_by.return_value.all.return_value = [cfg]
+        resp = client.get("/scoring/configs")
+        assert resp.status_code == 200
+        data = resp.json()
+        assert len(data) == 1
+        assert data[0]["name"] == "my-config"
+
+
+# ---------------------------------------------------------------------------
+# POST /configs
+# ---------------------------------------------------------------------------
+
+class TestCreateScoringConfig:
+    def test_create_valid(self, client, session):
+        cfg = _make_config("new-config")
+        session.flush = MagicMock()
+
+        def add_side_effect(obj):
+            obj.id = cfg.id
+            obj.created_at = cfg.created_at
+
+        session.add.side_effect = add_side_effect
+
+        resp = client.post("/scoring/configs", json={
+            "name": "new-config",
+            "formula": "linear",
+            "weights": {"embedding_similarity": 1.0},
+        })
+        assert resp.status_code == 201
+        assert resp.json()["name"] == "new-config"
+
+    def test_invalid_formula_returns_422(self, client, session):
+        resp = client.post("/scoring/configs", json={
+            "name": "bad",
+            "formula": "nonexistent_formula",
+            "weights": {"embedding_similarity": 1.0},
+        })
+        assert resp.status_code == 422
+
+    def test_unknown_signal_key_returns_422(self, client, session):
+        resp = client.post("/scoring/configs", json={
+            "name": "bad",
+            "formula": "linear",
+            "weights": {"nonexistent_signal": 1.0},
+        })
+        assert resp.status_code == 422
+
+    def test_invalid_evidence_weight_value_returns_422(self, client, session):
+        resp = client.post("/scoring/configs", json={
+            "name": "bad",
+            "formula": "linear",
+            "weights": {"embedding_similarity": 1.0},
+            "evidence_weights": {"IEA": 1.5},
+        })
+        assert resp.status_code == 422
+
+    def test_unknown_evidence_code_returns_422(self, client, session):
+        resp = client.post("/scoring/configs", json={
+            "name": "bad",
+            "formula": "linear",
+            "weights": {"embedding_similarity": 1.0},
+            "evidence_weights": {"BADCODE": 0.5},
+        })
+        assert resp.status_code == 422
+
+    def test_evidence_weighted_formula_accepted(self, client, session):
+        cfg = _make_config("ew-config", formula="evidence_weighted")
+        session.flush = MagicMock()
+
+        def add_side_effect(obj):
+            obj.id = cfg.id
+            obj.created_at = cfg.created_at
+
+        session.add.side_effect = add_side_effect
+
+        resp = client.post("/scoring/configs", json={
+            "name": "ew-config",
+            "formula": "evidence_weighted",
+            "weights": {"embedding_similarity": 1.0},
+        })
+        assert resp.status_code == 201
+
+
+# ---------------------------------------------------------------------------
+# GET /configs/{config_id}
+# ---------------------------------------------------------------------------
+
+class TestGetScoringConfig:
+    def test_found(self, client, session):
+        cfg = _make_config("found")
+        session.get.return_value = cfg
+        resp = client.get(f"/scoring/configs/{cfg.id}")
+        assert resp.status_code == 200
+        assert resp.json()["name"] == "found"
+
+    def test_not_found(self, client, session):
+        session.get.return_value = None
+        resp = client.get(f"/scoring/configs/{uuid4()}")
+        assert resp.status_code == 404
+
+
+# ---------------------------------------------------------------------------
+# DELETE /configs/{config_id}
+# ---------------------------------------------------------------------------
+
+class TestDeleteScoringConfig:
+    def test_delete_existing(self, client, session):
+        cfg = _make_config()
+        session.get.return_value = cfg
+        resp = client.delete(f"/scoring/configs/{cfg.id}")
+        assert resp.status_code == 204
+        session.delete.assert_called_once_with(cfg)
+
+    def test_delete_not_found(self, client, session):
+        session.get.return_value = None
+        resp = client.delete(f"/scoring/configs/{uuid4()}")
+        assert resp.status_code == 404
+
+
+# ---------------------------------------------------------------------------
+# POST /configs/presets
+# ---------------------------------------------------------------------------
+
+class TestCreatePresets:
+    def test_creates_all_presets_when_none_exist(self, client, session):
+        session.query.return_value.all.return_value = []
+        resp = client.post("/scoring/configs/presets")
+        assert resp.status_code == 201
+        data = resp.json()
+        assert "created" in data
+        assert len(data["created"]) > 0
+
+    def test_skips_existing_presets(self, client, session):
+        from protea.api.routers.scoring import _PRESET_CONFIGS
+        all_names = [(p["name"],) for p in _PRESET_CONFIGS]
+        session.query.return_value.all.return_value = all_names
+        resp = client.post("/scoring/configs/presets")
+        assert resp.status_code == 201
+        assert resp.json()["created"] == []
+
+
+# ---------------------------------------------------------------------------
+# GET /prediction-sets/{set_id}/score.tsv — 404 preflight checks
+# ---------------------------------------------------------------------------
+
+class TestScoredTSV:
+    def test_prediction_set_not_found(self, client, session):
+        session.get.return_value = None
+        resp = client.get(
+            f"/scoring/prediction-sets/{uuid4()}/score.tsv"
+            f"?scoring_config_id={uuid4()}"
+        )
+        assert resp.status_code == 404
+
+    def test_scoring_config_not_found(self, client, session):
+        from unittest.mock import call
+        from protea.infrastructure.orm.models.embedding.prediction_set import PredictionSet
+        # First get (PredictionSet) found, second (ScoringConfig) not found
+        session.get.side_effect = [MagicMock(), None]
+        resp = client.get(
+            f"/scoring/prediction-sets/{uuid4()}/score.tsv"
+            f"?scoring_config_id={uuid4()}"
+        )
+        assert resp.status_code == 404
+
+
+# ---------------------------------------------------------------------------
+# GET /prediction-sets/{set_id}/metrics — 404 preflight checks
+# ---------------------------------------------------------------------------
+
+class TestMetricsEndpoint:
+    def _url(self):
+        return (
+            f"/scoring/prediction-sets/{uuid4()}/metrics"
+            f"?scoring_config_id={uuid4()}"
+            f"&old_annotation_set_id={uuid4()}"
+            f"&new_annotation_set_id={uuid4()}"
+            f"&ontology_snapshot_id={uuid4()}"
+        )
+
+    def test_prediction_set_not_found(self, client, session):
+        session.get.return_value = None
+        resp = client.get(self._url())
+        assert resp.status_code == 404
+
+    def test_scoring_config_not_found(self, client, session):
+        session.get.side_effect = [MagicMock(), None]
+        resp = client.get(self._url())
+        assert resp.status_code == 404
diff --git a/tests/test_support_maintenance_routers.py b/tests/test_support_maintenance_routers.py
new file mode 100644
index 0000000..beb86be
--- /dev/null
+++ b/tests/test_support_maintenance_routers.py
@@ -0,0 +1,209 @@
+"""Unit tests for support and maintenance API routers — no real DB required."""
+from __future__ import annotations
+
+from contextlib import contextmanager
+from datetime import datetime, timezone
+from unittest.mock import MagicMock, patch
+from uuid import uuid4
+
+import pytest
+from fastapi import FastAPI
+from fastapi.testclient import TestClient
+
+from protea.api.routers.support import router as support_router
+from protea.api.routers.maintenance import router as maintenance_router
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+@contextmanager
+def _mock_scope(session):
+    yield session
+
+
+def _make_app_with_router(router, session):
+    app = FastAPI()
+    factory = MagicMock()
+    app.state.session_factory = factory
+    app.include_router(router)
+    return app, factory
+
+
+# ---------------------------------------------------------------------------
+# Support router
+# ---------------------------------------------------------------------------
+
+@pytest.fixture()
+def support_session():
+    return MagicMock()
+
+
+@pytest.fixture()
+def support_client(support_session):
+    app, _ = _make_app_with_router(support_router, support_session)
+    with patch("protea.api.routers.support.session_scope",
+               side_effect=lambda _: _mock_scope(support_session)):
+        yield TestClient(app, raise_server_exceptions=True)
+
+
+class TestGetSupport:
+    def test_returns_count_and_comments(self, support_client, support_session):
+        support_session.query.return_value.count.return_value = 5
+        support_session.query.return_value.filter.return_value.order_by.return_value.limit.return_value.all.return_value = []
+        resp = support_client.get("/support")
+        assert resp.status_code == 200
+        data = resp.json()
+        assert "count" in data
+        assert "comments" in data
+
+    def test_all_comments_flag(self, support_client, support_session):
+        support_session.query.return_value.count.return_value = 3
+        support_session.query.return_value.filter.return_value.order_by.return_value.limit.return_value.all.return_value = []
+        resp = support_client.get("/support?all_comments=true")
+        assert resp.status_code == 200
+
+    def test_comments_serialized(self, support_client, support_session):
+        entry = MagicMock()
+        entry.id = uuid4()
+        entry.comment = "Great tool!"
+        entry.created_at = datetime(2026, 1, 1, tzinfo=timezone.utc)
+        support_session.query.return_value.count.return_value = 1
+        support_session.query.return_value.filter.return_value.order_by.return_value.limit.return_value.all.return_value = [entry]
+        resp = support_client.get("/support")
+        assert resp.status_code == 200
+        comments = resp.json()["comments"]
+        assert len(comments) == 1
+        assert comments[0]["comment"] == "Great tool!"
+
+
+class TestPostSupport:
+    def test_submit_with_comment(self, support_client, support_session):
+        entry = MagicMock()
+        entry.id = uuid4()
+
+        def add_side(obj):
+            obj.id = entry.id
+
+        support_session.add.side_effect = add_side
+        support_session.flush = MagicMock()
+        support_session.query.return_value.count.return_value = 10
+
+        resp = support_client.post("/support", json={"comment": "Nice work!"})
+        assert resp.status_code == 201
+        assert resp.json()["count"] == 10
+
+    def test_submit_without_comment(self, support_client, support_session):
+        entry = MagicMock()
+        entry.id = uuid4()
+
+        def add_side(obj):
+            obj.id = entry.id
+
+        support_session.add.side_effect = add_side
+        support_session.flush = MagicMock()
+        support_session.query.return_value.count.return_value = 1
+
+        resp = support_client.post("/support", json={})
+        assert resp.status_code == 201
+
+    def test_empty_string_comment_stored_as_none(self, support_client, support_session):
+        entry = MagicMock()
+        entry.id = uuid4()
+
+        captured = {}
+
+        def add_side(obj):
+            obj.id = entry.id
+            captured["comment"] = obj.comment
+
+        support_session.add.side_effect = add_side
+        support_session.flush = MagicMock()
+        support_session.query.return_value.count.return_value = 1
+
+        support_client.post("/support", json={"comment": "   "})
+        # Whitespace-only comment should be stored as None
+        assert captured.get("comment") is None
+
+
+# ---------------------------------------------------------------------------
+# Maintenance router
+# ---------------------------------------------------------------------------
+
+@pytest.fixture()
+def maint_session():
+    return MagicMock()
+
+
+@pytest.fixture()
+def maint_client(maint_session):
+    app, _ = _make_app_with_router(maintenance_router, maint_session)
+    with patch("protea.api.routers.maintenance.session_scope",
+               side_effect=lambda _: _mock_scope(maint_session)):
+        yield TestClient(app, raise_server_exceptions=True)
+
+
+class TestVacuumSequencesPreview:
+    def test_returns_counts(self, maint_client, maint_session):
+        maint_session.query.return_value.count.return_value = 100
+        maint_session.execute.return_value.scalar.return_value = 10
+        resp = maint_client.get("/maintenance/vacuum-sequences/preview")
+        assert resp.status_code == 200
+        data = resp.json()
+        assert data["total_sequences"] == 100
+        assert data["orphan_sequences"] == 10
+        assert data["referenced_sequences"] == 90
+
+
+class TestVacuumSequences:
+    def test_no_orphans(self, maint_client, maint_session):
+        maint_session.execute.return_value.fetchall.return_value = []
+        resp = maint_client.post("/maintenance/vacuum-sequences")
+        assert resp.status_code == 200
+        assert resp.json()["deleted_sequences"] == 0
+
+    def test_with_orphans(self, maint_client, maint_session):
+        maint_session.execute.return_value.fetchall.return_value = [(1,), (2,), (3,)]
+        maint_session.query.return_value.filter.return_value.delete.return_value = 3
+        resp = maint_client.post("/maintenance/vacuum-sequences")
+        assert resp.status_code == 200
+        assert resp.json()["deleted_sequences"] == 3
+
+
+class TestVacuumEmbeddingsPreview:
+    def test_returns_counts(self, maint_client, maint_session):
+        maint_session.query.return_value.count.return_value = 500
+        maint_session.execute.return_value.scalar.return_value = 50
+        resp = maint_client.get("/maintenance/vacuum-embeddings/preview")
+        assert resp.status_code == 200
+        data = resp.json()
+        assert data["total_embeddings"] == 500
+        assert data["unindexed_embeddings"] == 50
+        assert data["indexed_embeddings"] == 450
+
+
+class TestVacuumEmbeddings:
+    def test_no_unindexed(self, maint_client, maint_session):
+        maint_session.execute.return_value.fetchall.return_value = []
+        resp = maint_client.post("/maintenance/vacuum-embeddings")
+        assert resp.status_code == 200
+        assert resp.json()["deleted_embeddings"] == 0
+
+    def test_with_unindexed(self, maint_client, maint_session):
+        maint_session.execute.return_value.fetchall.return_value = [(10,), (20,)]
+        maint_session.query.return_value.filter.return_value.delete.return_value = 2
+        resp = maint_client.post("/maintenance/vacuum-embeddings")
+        assert resp.status_code == 200
+        assert resp.json()["deleted_embeddings"] == 2
+
+
+class TestMaintenanceSessionFactoryMissing:
+    def test_raises_when_no_factory(self):
+        from protea.api.routers.maintenance import router as maint_router
+        app = FastAPI()
+        # Intentionally do NOT set app.state.session_factory
+        app.include_router(maint_router)
+        client = TestClient(app, raise_server_exceptions=False)
+        resp = client.get("/maintenance/vacuum-sequences/preview")
+        assert resp.status_code == 500