Support browser calibration summaries in evidence export

CHANGELOG.md

@@ -7,6 +7,13 @@
 
 # Changelog
 
+## Sprint 137 Browser calibration summary evidence bundle handoff (2026-07-08)
+
+- Extended `scripts/export_detection_calibration_evidence.sh` so it accepts Detection Lab `detection-calibration-summary.json` browser exports in addition to the older operator calibration summary format.
+- Added summary normalization for browser-exported `rows`, root `project_id`, persisted `quality_check_id` values and best-mode fallback selection.
+- Updated operator docs with the direct browser summary command.
+- No backend API, migration, inference, provider fetching, model download or frontend runtime behavior changed.
+
 ## Sprint 136 Guided calibration summary export (2026-07-08)
 
 - Added a Detection Lab `Download calibration summary` action for guided calibration rows.

backend/tests/test_sprint137_browser_calibration_summary_evidence_script.py

@@ -0,0 +1,25 @@
+from pathlib import Path
+
+
+ROOT = Path(__file__).resolve().parents[2]
+
+
+def test_evidence_bundle_script_accepts_browser_calibration_summary_export() -> None:
+    script_path = ROOT / "scripts" / "export_detection_calibration_evidence.sh"
+    readme_path = ROOT / "scripts" / "README.md"
+    todo_path = ROOT / "docs" / "TODO.md"
+
+    script = script_path.read_text(encoding="utf-8")
+    readme = readme_path.read_text(encoding="utf-8")
+    todo = todo_path.read_text(encoding="utf-8")
+
+    assert "detection-calibration-summary.json" in script
+    assert "export_type" in script
+    assert "detection_calibration_summary" in script
+    assert "normalize_calibration_items" in script
+    assert "summary.get(\"rows\")" in script
+    assert "root_project_id = summary.get(\"project_id\")" in script
+    assert "quality_check_ids" in script
+    assert "Browser Detection Lab calibration summary" in readme
+    assert "bash scripts/export_detection_calibration_evidence.sh http://192.168.10.150:1202 ./detection-calibration-summary.json" in readme
+    assert "[x] Allow the evidence bundle script to consume Detection Lab calibration summary exports" in todo

docs/CODEX_EXECUTION_LOG.md

@@ -1,3 +1,27 @@
+## Sprint 137 Browser calibration summary evidence bundle handoff (2026-07-08)
+
+Changed:
+- Extended `scripts/export_detection_calibration_evidence.sh` so it can consume Detection Lab `detection-calibration-summary.json` browser exports as well as the existing operator `calibration_summary.json` format.
+- Added summary normalization for browser-exported `rows`, root `project_id`, persisted `quality_check_id` values and `CALIBRATION_EVIDENCE_MODE=best` fallback selection.
+- Updated `scripts/README.md`, `CHANGELOG.md` and `docs/TODO.md`.
+- Added regression coverage in `backend/tests/test_sprint137_browser_calibration_summary_evidence_script.py`.
+
+Tested:
+- Red step: `python -m pytest backend\tests\test_sprint137_browser_calibration_summary_evidence_script.py -q` failed while browser summary support was absent.
+- `python -m pytest backend\tests\test_sprint137_browser_calibration_summary_evidence_script.py backend\tests\test_sprint136_calibration_summary_export_ui.py backend\tests\test_sprint125_detection_calibration_evidence_bundle.py -q` (`3 passed`)
+- `bash -n scripts/export_detection_calibration_evidence.sh`
+- `python -m compileall backend/app`
+- `bash scripts/run_readiness_check.sh` (`416 passed`; frontend typecheck/build passed; Alembic head `202606120900`; live smoke syntax passed)
+
+Open:
+- Live Tower deploy validation still needs to run for this pass.
+
+Limitations:
+- This is operator evidence tooling only. It does not add backend endpoints, change migrations, rerun inference, create new QA metrics, promote thresholds, mutate model configuration, download models, add provider fetching or change frontend runtime behavior.
+
+Next recommended pass:
+- Add a tiny local fixture smoke for the evidence bundle script that uses a saved browser-style summary plus mocked canonical evidence responses, so the bundle renderer itself is tested beyond static contract checks.
+
 ## Sprint 136 Guided calibration summary export (2026-07-08)
 
 Changed:

docs/TODO.md

@@ -423,5 +423,6 @@ This file now starts with the current implementation status. Older preparation/b
 - [x] Add guided in-app detection calibration runner for explicit threshold sweeps.
 - [x] Link guided calibration rows to the QA evidence map.
 - [x] Add guided calibration summary export from the Detection Lab.
+- [x] Allow the evidence bundle script to consume Detection Lab calibration summary exports.
 - [ ] Add more AOIs after the tile-level baseline so the next local model attempt is not limited to Geel/Mol/Turnhout.
 - [ ] Add negative/background AOIs so the next tile dataset is not all positive tiles.

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 = []