give segmentation QA the same threshold-independent view as detection

Detection QA reports a precision/recall curve, average precision and a
calibration sweep; segmentation QA reported a single operating point. Both rank
their outputs by confidence, so the same view applies, and the asymmetry meant
the two panels answered different questions about comparable runs — an
inconsistency introduced when detection gained the curve.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

backend/tests/test_sprint9_segmentation_foundation.py

@@ -362,6 +362,11 @@ def test_segmentation_qa_persists_quality_check_and_metrics() -> None:
         "mean_iou",
         "false_positive_count",
         "false_negative_count",
+        # Threshold-independent, as detection QA already reported. The two
+        # panels previously answered different questions about the same run.
+        "average_precision",
+        "best_f1",
+        "best_f1_threshold",
     ]