diff --git a/lib/inat_inferrer.py b/lib/inat_inferrer.py
index 2da6c7c..8088c6a 100644
--- a/lib/inat_inferrer.py
+++ b/lib/inat_inferrer.py
@@ -640,7 +640,7 @@ def limit_leaf_scores_that_include_humans(self, leaf_scores, strategy=None):
                 return top_results.head(1)
 
         # if requesting a more limited approach to human exclusion
-        if strategy == "limited" and self.taxonomy.mammals_taxon is not None:
+        if self.taxonomy.mammals_taxon is not None:
             mammals_results = top_results.query(
                 f"left > {self.taxonomy.mammals_taxon['left']} and "
                 f"right < {self.taxonomy.mammals_taxon['right']}"
diff --git a/tests/fixtures/taxonomy.csv b/tests/fixtures/taxonomy.csv
index deefb95..bce3457 100644
--- a/tests/fixtures/taxonomy.csv
+++ b/tests/fixtures/taxonomy.csv
@@ -24,3 +24,7 @@ parent_taxon_id,taxon_id,rank_level,leaf_class_id,iconic_class_id,spatial_class_
 43367,43575,30,,,23,Hominidae
 43575,43583,20,,,24,Homo
 43583,43584,10,,,25,Homo sapiens
+40151,43698,40,,,26,Rodentia
+43698,44185,30,,,27,Muridae
+44185,44678,20,,,28,Mus
+44678,44705,10,,,29,Mus musculus
diff --git a/tests/test_inat_inferrer.py b/tests/test_inat_inferrer.py
index 0643adc..df93fd9 100644
--- a/tests/test_inat_inferrer.py
+++ b/tests/test_inat_inferrer.py
@@ -112,6 +112,8 @@ def test_results_are_unchanged_if_they_dont_include_humans(self, inatInferrer):
         for n in range(20):
             results.append({
                 "taxon_id": n,
+                "left": 0,
+                "right": 0,
                 "combined_score": 1.0 - (n * 0.01)
             })
         results = pd.DataFrame(results)
@@ -120,46 +122,98 @@ def test_results_are_unchanged_if_they_dont_include_humans(self, inatInferrer):
     def test_results_are_unchanged_if_they_dont_include_humans_in_top_10(self, inatInferrer):
         assert inatInferrer.taxonomy.human_taxon["name"] == "Homo sapiens"
         assert inatInferrer.taxonomy.human_taxon["taxon_id"] == 43584
+
         results = []
         for n in range(20):
             results.append({
                 "taxon_id": n,
+                "left": 0,
+                "right": 0,
                 "combined_score": 1.0 - (n * 0.01)
             })
         results.append({
             "taxon_id": inatInferrer.taxonomy.human_taxon["taxon_id"],
+            "left": inatInferrer.taxonomy.human_taxon["left"],
+            "right": inatInferrer.taxonomy.human_taxon["right"],
             "combined_score": 0.001
         })
         results = pd.DataFrame(results)
         assert results.equals(inatInferrer.limit_leaf_scores_that_include_humans(results))
 
     def test_results_are_empty_if_humans_are_in_top_10_but_not_first(self, inatInferrer):
+        # ... and the results contain another mammal
         assert inatInferrer.taxonomy.human_taxon["name"] == "Homo sapiens"
         assert inatInferrer.taxonomy.human_taxon["taxon_id"] == 43584
+        mouse_taxon = inatInferrer.taxonomy.df.query("name == 'Mus musculus'").iloc[0]
+
         results = []
         for n in range(5):
             results.append({
                 "taxon_id": n,
+                "left": 0,
+                "right": 0,
                 "combined_score": 1.0 - (n * 0.01)
             })
+        results.append({
+            "taxon_id": mouse_taxon["taxon_id"],
+            "left": mouse_taxon["left"],
+            "right": mouse_taxon["right"],
+            "combined_score": 0.002
+        })
         results.append({
             "taxon_id": inatInferrer.taxonomy.human_taxon["taxon_id"],
+            "left": inatInferrer.taxonomy.human_taxon["left"],
+            "right": inatInferrer.taxonomy.human_taxon["right"],
             "combined_score": 0.001
         })
         results = pd.DataFrame(results)
         assert inatInferrer.limit_leaf_scores_that_include_humans(results).empty
 
+    def test_humans_excluded_if_humans_are_in_top_10_but_not_first(self, inatInferrer):
+        assert inatInferrer.taxonomy.human_taxon["name"] == "Homo sapiens"
+        assert inatInferrer.taxonomy.human_taxon["taxon_id"] == 43584
+
+        results = []
+        for n in range(5):
+            results.append({
+                "taxon_id": n,
+                "left": 0,
+                "right": 0,
+                "combined_score": 1.0 - (n * 0.01)
+            })
+        results.append({
+            "taxon_id": inatInferrer.taxonomy.human_taxon["taxon_id"],
+            "left": inatInferrer.taxonomy.human_taxon["left"],
+            "right": inatInferrer.taxonomy.human_taxon["right"],
+            "combined_score": 0.001
+        })
+        results = pd.DataFrame(results)
+        assert results.head(5).equals(inatInferrer.limit_leaf_scores_that_include_humans(results))
+
     def test_results_are_empty_if_humans_are_first_by_small_margin(self, inatInferrer):
+        # ... and the results contain another mammal
         assert inatInferrer.taxonomy.human_taxon["name"] == "Homo sapiens"
         assert inatInferrer.taxonomy.human_taxon["taxon_id"] == 43584
+        mouse_taxon = inatInferrer.taxonomy.df.query("name == 'Mus musculus'").iloc[0]
+
         results = []
         results.append({
             "taxon_id": inatInferrer.taxonomy.human_taxon["taxon_id"],
+            "left": inatInferrer.taxonomy.human_taxon["left"],
+            "right": inatInferrer.taxonomy.human_taxon["right"],
             "combined_score": 1.0
         })
+        results.append({
+            "taxon_id": mouse_taxon["taxon_id"],
+            "left": mouse_taxon["left"],
+            "right": mouse_taxon["right"],
+            "combined_score": 0.95
+        })
         for n in range(5):
             results.append({
                 "taxon_id": n,
+                "left": 0,
+                "right": 0,
                 "combined_score": 0.9 - (n * 0.01)
             })
         results = pd.DataFrame(results)
@@ -171,12 +225,16 @@ def test_only_humans_returned_if_first_by_large_margin(self, inatInferrer):
         results = []
         human_result = {
             "taxon_id": inatInferrer.taxonomy.human_taxon["taxon_id"],
+            "left": inatInferrer.taxonomy.human_taxon["left"],
+            "right": inatInferrer.taxonomy.human_taxon["right"],
             "combined_score": 1.0
         }
         results.append(human_result)
         for n in range(5):
             results.append({
                 "taxon_id": n,
+                "left": 0,
+                "right": 0,
                 "combined_score": 0.5 - (n * 0.01)
             })
         results = pd.DataFrame(results)
diff --git a/tests/test_model_taxonomy_dataframe.py b/tests/test_model_taxonomy_dataframe.py
index c38a120..7461d56 100644
--- a/tests/test_model_taxonomy_dataframe.py
+++ b/tests/test_model_taxonomy_dataframe.py
@@ -41,15 +41,15 @@ def test_human_taxon(self, capsys, taxonomy):
     def test_print(self, capsys, taxonomy):
         ModelTaxonomyDataframe.print(taxonomy.df)
         captured = capsys.readouterr()
-        assert "├──Animalia :: 0:33" in captured.out
-        assert "│   └──Chordata :: 1:32" in captured.out
+        assert "├──Animalia :: 0:41" in captured.out
+        assert "│   └──Chordata :: 1:40" in captured.out
 
     def test_print_with_aggregated_combined_score(self, capsys, taxonomy):
         taxonomy.df["aggregated_combined_score"] = 1
         ModelTaxonomyDataframe.print(taxonomy.df)
         captured = capsys.readouterr()
-        assert "├──Animalia :: 0:33" in captured.out
-        assert "│   └──Chordata :: 1:32" in captured.out
+        assert "├──Animalia :: 0:41" in captured.out
+        assert "│   └──Chordata :: 1:40" in captured.out
 
     def test_print_with_lambda(self, capsys, taxonomy):
         ModelTaxonomyDataframe.print(taxonomy.df, display_taxon_lambda=(