diff --git a/lib/inat_inferrer.py b/lib/inat_inferrer.py
index dfcb608..afbefa5 100644
--- a/lib/inat_inferrer.py
+++ b/lib/inat_inferrer.py
@@ -272,6 +272,14 @@ def combine_results(self, raw_vision_scores, raw_geo_scores, filter_taxon, debug
             # multipliedby the normalized geo score
             leaf_scores["combined_score"] = leaf_scores["normalized_vision_score"] * \
                 leaf_scores["normalized_geo_score"]
+
+        sum_of_root_node_aggregated_combined_scores = leaf_scores["combined_score"].sum()
+        if sum_of_root_node_aggregated_combined_scores > 0:
+            leaf_scores["normalized_combined_score"] = leaf_scores[
+                "combined_score"] / sum_of_root_node_aggregated_combined_scores
+        else:
+            leaf_scores["normalized_combined_score"] = 0
+
         if debug:
             print("Score Combining Time: %0.2fms" % ((time.time() - start_time) * 1000.))
         leaf_scores.reset_index(drop=True, inplace=True)
diff --git a/lib/inat_vision_api_responses.py b/lib/inat_vision_api_responses.py
index c06133e..5cf292c 100644
--- a/lib/inat_vision_api_responses.py
+++ b/lib/inat_vision_api_responses.py
@@ -32,7 +32,7 @@ def object_response(leaf_scores, inferrer):
         common_ancestor = inferrer.common_ancestor_from_leaf_scores(leaf_scores, debug=True)
         if common_ancestor is not None:
             common_ancestor_frame = pd.DataFrame([common_ancestor])
-            common_ancestor_frame = InatVisionAPIResponses.update_aggregated_scores_scaling(
+            common_ancestor_frame = InatVisionAPIResponses.update_common_ancestor_scores_scaling(
                 common_ancestor_frame
             )
             common_ancestor = InatVisionAPIResponses.array_response_common_ancestor_columns(
@@ -135,7 +135,7 @@ def limit_leaf_scores_for_response(leaf_scores):
     @staticmethod
     def update_leaf_scores_scaling(leaf_scores):
         score_columns = [
-            "combined_score",
+            "normalized_combined_score",
             "geo_score",
             "normalized_vision_score",
             "geo_threshold"
@@ -145,6 +145,16 @@ def update_leaf_scores_scaling(leaf_scores):
         ].multiply(100, axis="index")
         return leaf_scores
 
+    @staticmethod
+    def update_common_ancestor_scores_scaling(aggregated_scores):
+        score_columns = [
+            "normalized_aggregated_combined_score"
+        ]
+        aggregated_scores[score_columns] = aggregated_scores[
+            score_columns
+        ].multiply(100, axis="index")
+        return aggregated_scores
+
     @staticmethod
     def update_aggregated_scores_scaling(aggregated_scores):
         score_columns = [
@@ -162,7 +172,7 @@ def update_aggregated_scores_scaling(aggregated_scores):
     @staticmethod
     def array_response_columns(leaf_scores):
         columns_to_return = [
-            "combined_score",
+            "normalized_combined_score",
             "geo_score",
             "taxon_id",
             "name",
@@ -171,25 +181,26 @@ def array_response_columns(leaf_scores):
         ]
         column_mapping = {
             "taxon_id": "id",
-            "normalized_vision_score": "vision_score"
+            "normalized_vision_score": "vision_score",
+            "normalized_combined_score": "combined_score"
         }
         return leaf_scores[columns_to_return].rename(columns=column_mapping)
 
     @staticmethod
     def array_response_common_ancestor_columns(common_ancestor_dataframe):
         columns_to_return = [
-            "aggregated_combined_score",
+            "normalized_aggregated_combined_score",
             "aggregated_geo_score",
             "taxon_id",
             "name",
-            "normalized_aggregated_vision_score",
+            "aggregated_vision_score",
             "aggregated_geo_threshold"
         ]
         column_mapping = {
-            "aggregated_combined_score": "combined_score",
+            "normalized_aggregated_combined_score": "combined_score",
             "aggregated_geo_score": "geo_score",
             "taxon_id": "id",
-            "normalized_aggregated_vision_score": "vision_score",
+            "aggregated_vision_score": "vision_score",
             "aggregated_geo_threshold": "geo_threshold"
         }
         return common_ancestor_dataframe[columns_to_return].rename(columns=column_mapping)