Support browser calibration summaries in evidence export

scripts/README.md

@@ -442,6 +442,12 @@ CALIBRATION_SUMMARY_PATH=/mnt/user/appdata/geointel/artifacts/detection-calibrat
 bash scripts/export_detection_calibration_evidence.sh http://192.168.10.150:1202
 ```
 
+Browser Detection Lab calibration summary exports are supported too:
+
+```bash
+bash scripts/export_detection_calibration_evidence.sh http://192.168.10.150:1202 ./detection-calibration-summary.json
+```
+
 The evidence export reads each persisted `quality_check_id`, calls the existing
 QA evidence GeoJSON endpoint, writes `calibration_evidence.geojson`,
 `calibration_evidence_summary.json` and a standalone

scripts/export_detection_calibration_evidence.sh

@@ -10,8 +10,12 @@ Usage:
 or:
   bash scripts/export_detection_calibration_evidence.sh [base_url] /path/to/calibration_summary.json
 
+or with the browser Detection Lab export:
+  bash scripts/export_detection_calibration_evidence.sh [base_url] /path/to/detection-calibration-summary.json
+
 Required input:
-  CALIBRATION_SUMMARY_PATH     calibration_summary.json produced by run_detection_calibration_sweep.sh.
+  CALIBRATION_SUMMARY_PATH     calibration_summary.json produced by run_detection_calibration_sweep.sh
+                               or detection-calibration-summary.json from the Detection Lab.
 
 Optional environment:
   CALIBRATION_EVIDENCE_MODE    all or best, default: all.
@@ -86,9 +90,42 @@ summary_path, manifest_path, mode = sys.argv[1:4]
 with open(summary_path, "r", encoding="utf-8") as handle:
     summary = json.load(handle)
 
-items = summary.get("items") or []
+def normalize_calibration_items(summary):
+    root_project_id = summary.get("project_id")
+    if summary.get("export_type") == "detection_calibration_summary":
+        rows = summary.get("rows") or []
+        quality_check_ids = summary.get("quality_check_ids") or []
+        items = []
+        for row in rows:
+            if not isinstance(row, dict):
+                continue
+            quality_check_id = row.get("quality_check_id")
+            if not quality_check_id:
+                continue
+            items.append(
+                {
+                    "project_id": row.get("project_id") or root_project_id,
+                    "analysis_run_id": row.get("analysis_run_id"),
+                    "job_id": row.get("job_id"),
+                    "quality_check_id": quality_check_id,
+                    "threshold": row.get("threshold"),
+                    "detection_count": row.get("detection_count"),
+                    "quality_score": row.get("quality_score") or row.get("f1_score"),
+                    "precision": row.get("precision"),
+                    "recall": row.get("recall"),
+                    "f1_score": row.get("f1_score"),
+                }
+            )
+        if quality_check_ids and not items:
+            raise SystemExit("detection-calibration-summary.json has quality_check_ids but no export rows")
+        return items
+    return summary.get("items") or []
+
+items = normalize_calibration_items(summary)
 if mode == "best":
     best = summary.get("best_by_score")
+    if best is None and items:
+        best = max(items, key=lambda item: item.get("quality_score") if isinstance(item.get("quality_score"), (int, float)) else float("-inf"))
     if not isinstance(best, dict):
         raise SystemExit("calibration_summary.json has no best_by_score object")
     items = [best]
@@ -133,9 +170,46 @@ summary_path, output_dir, mode = sys.argv[1:4]
 with open(summary_path, "r", encoding="utf-8") as handle:
     calibration_summary = json.load(handle)
 
-selected_items = calibration_summary.get("items") or []
+def normalize_calibration_items(summary):
+    root_project_id = summary.get("project_id")
+    if summary.get("export_type") == "detection_calibration_summary":
+        rows = summary.get("rows") or []
+        quality_check_ids = summary.get("quality_check_ids") or []
+        items = []
+        for row in rows:
+            if not isinstance(row, dict):
+                continue
+            quality_check_id = row.get("quality_check_id")
+            if not quality_check_id:
+                continue
+            items.append(
+                {
+                    "project_id": row.get("project_id") or root_project_id,
+                    "analysis_run_id": row.get("analysis_run_id"),
+                    "job_id": row.get("job_id"),
+                    "quality_check_id": quality_check_id,
+                    "threshold": row.get("threshold"),
+                    "detection_count": row.get("detection_count"),
+                    "quality_score": row.get("quality_score") or row.get("f1_score"),
+                    "precision": row.get("precision"),
+                    "recall": row.get("recall"),
+                    "f1_score": row.get("f1_score"),
+                }
+            )
+        if quality_check_ids and not items:
+            raise SystemExit("detection-calibration-summary.json has quality_check_ids but no export rows")
+        return items
+    return summary.get("items") or []
+
+selected_items = normalize_calibration_items(calibration_summary)
 if mode == "best":
-    selected_items = [calibration_summary["best_by_score"]]
+    best = calibration_summary.get("best_by_score")
+    if best is None and selected_items:
+        best = max(
+            selected_items,
+            key=lambda item: item.get("quality_score") if isinstance(item.get("quality_score"), (int, float)) else float("-inf"),
+        )
+    selected_items = [best]
 items_by_quality_check = {str(item["quality_check_id"]): item for item in selected_items}
 
 combined_features = []