Complete temporal detection safety gate

backend/app/core/request_context.py

@@ -0,0 +1,18 @@
+from __future__ import annotations
+
+from contextvars import ContextVar, Token
+
+
+_request_id: ContextVar[str] = ContextVar("geointel_request_id", default="-")
+
+
+def get_request_id() -> str:
+    return _request_id.get()
+
+
+def set_request_id(value: str) -> Token:
+    return _request_id.set(value)
+
+
+def reset_request_id(token: Token) -> None:
+    _request_id.reset(token)

backend/app/main.py

@@ -1,6 +1,8 @@
 from __future__ import annotations
 
 import logging
+import re
+import time
 import uuid
 from contextlib import asynccontextmanager
 
@@ -13,11 +15,13 @@ from app.api.routes import analysis, areas, assistant, datasets, demo, detection
 from app.core.config import get_settings
 from app.core.errors import AppError
 from app.core.logging import configure_logging
+from app.core.request_context import reset_request_id, set_request_id
 from app.db.session import SessionLocal
 from app.services.runtime_reconciliation_service import RuntimeReconciliationService
 
 
 logger = logging.getLogger("geointel")
+SAFE_REQUEST_ID = re.compile(r"^[A-Za-z0-9._:-]{1,128}$")
 
 
 def _to_error_payload(
@@ -91,11 +95,25 @@ def create_app() -> FastAPI:
 
     @app.middleware("http")
     async def request_identity(request: Request, call_next):
-        request_id = request.headers.get("x-request-id") or str(uuid.uuid4())
+        supplied_request_id = request.headers.get("x-request-id", "")
+        request_id = supplied_request_id if SAFE_REQUEST_ID.fullmatch(supplied_request_id) else str(uuid.uuid4())
         request.state.request_id = request_id
-        response = await call_next(request)
-        response.headers["x-request-id"] = request_id
-        return response
+        token = set_request_id(request_id)
+        started_at = time.perf_counter()
+        try:
+            response = await call_next(request)
+            response.headers["x-request-id"] = request_id
+            logger.info(
+                "request_complete request_id=%s method=%s path=%s status=%s duration_ms=%.1f",
+                request_id,
+                request.method,
+                request.url.path,
+                response.status_code,
+                (time.perf_counter() - started_at) * 1000,
+            )
+            return response
+        finally:
+            reset_request_id(token)
 
     @app.exception_handler(AppError)
     async def app_error(request: Request, exc: AppError):  # noqa: ARG001

backend/app/services/detection_service.py

@@ -2,6 +2,7 @@ from __future__ import annotations
 
 import uuid
 import json
+import logging
 from datetime import UTC, datetime
 from pathlib import Path
 from typing import Any
@@ -13,6 +14,7 @@ from sqlalchemy import func
 
 from app.core.config import Settings, get_settings
 from app.core.errors import AppError
+from app.core.request_context import get_request_id
 from app.models import AnalysisRun, Dataset, Detection, Job, Project, VectorFeature
 from app.schemas.detection import DetectionListResponse, DetectionRead, DetectionRunListResponse, DetectionRunRead, DetectionRunResponse
 from app.services.detection_georeferencing import pixel_bbox_to_epsg4326_polygon
@@ -21,9 +23,13 @@ from app.services.model_asset_catalog_service import ModelAssetCatalogService
 from app.services.model_registry_service import ModelRegistryService
 from app.services.qa_service import QaService
 from app.services.quality_service import QualityService
+from app.services.temporal_compatibility_service import TemporalCompatibilityService
 from app.services.yolo_adapter import YoloDetectionAdapter
 
 
+logger = logging.getLogger("geointel.detection")
+
+
 class DetectionService:
     @staticmethod
     def _now() -> datetime:
@@ -58,6 +64,7 @@ class DetectionService:
                 details={"dataset_type": dataset.dataset_type},
                 status_code=400,
             )
+        TemporalCompatibilityService.ensure_detection_source_supported(dataset)
 
         selected_model_asset = None
         if model_id == resolved_settings.yolo_model_id and model_asset_id:
@@ -96,6 +103,15 @@ class DetectionService:
         }
         job = DetectionService._create_job(db, project_id, dataset_id, run_parameters)
         analysis_run = DetectionService._create_analysis_run(db, project_id, dataset_id, job.id, model, run_parameters)
+        logger.info(
+            "detection_started request_id=%s project_id=%s dataset_id=%s job_id=%s analysis_run_id=%s model_id=%s",
+            get_request_id(),
+            project_id,
+            dataset_id,
+            job.id,
+            analysis_run.id,
+            model.model_id,
+        )
 
         if not model.configured:
             message = model.limitation_message
@@ -281,6 +297,13 @@ class DetectionService:
             raise AppError(code="INVALID_DATASET_SCOPE", message="Reference dataset does not belong to detection project", status_code=400)
         if reference_dataset.dataset_type not in {"vector", "geojson"}:
             raise AppError(code="INVALID_DATASET_TYPE", message="Reference dataset must be vector data", status_code=400)
+        candidate_dataset = db.get(Dataset, run.dataset_id)
+        if not candidate_dataset:
+            raise AppError(code="DATASET_NOT_FOUND", message="Detection source dataset not found", status_code=404)
+        temporal_compatibility = TemporalCompatibilityService.assess_detection_qa(
+            candidate_dataset,
+            reference_dataset,
+        )
 
         detections = DetectionService._query_detection_rows(
             db,
@@ -421,6 +444,7 @@ class DetectionService:
                 "class_name": class_name,
                 "min_confidence": min_confidence,
                 "coverage_policy": coverage_summary["mode"],
+                "temporal_compatibility": temporal_compatibility,
             },
             findings={
                 "matches": evidence.matches,
@@ -429,6 +453,7 @@ class DetectionService:
                 "warnings": coverage_warnings + evidence.warnings,
                 "unsupported_geometry": evidence.unsupported,
                 "coverage": coverage_summary,
+                "temporal_compatibility": temporal_compatibility,
                 "box_to_footprint_diagnostics": box_to_footprint_diagnostics,
                 "match_evidence": evidence.match_evidence,
                 "false_positive_evidence": evidence.false_positive_evidence,
@@ -443,6 +468,17 @@ class DetectionService:
                 "false_negative_count": evidence.false_negatives,
             },
         )
+        logger.info(
+            "detection_qa_completed request_id=%s job_id=%s analysis_run_id=%s quality_check_id=%s "
+            "candidate_dataset_id=%s reference_dataset_id=%s status=%s",
+            get_request_id(),
+            run.job_id,
+            analysis_run_id,
+            quality_check.id,
+            run.dataset_id,
+            reference_dataset_id,
+            status,
+        )
         return {
             "status": status,
             "quality_check_id": str(quality_check.id),
@@ -462,6 +498,7 @@ class DetectionService:
             "iou_threshold": iou_threshold,
             "warnings": coverage_warnings + evidence.warnings,
             "coverage": coverage_summary,
+            "temporal_compatibility": temporal_compatibility,
             "box_to_footprint_diagnostics": box_to_footprint_diagnostics,
             "match_evidence": evidence.match_evidence,
             "false_positive_evidence": evidence.false_positive_evidence,

backend/app/services/temporal_compatibility_service.py

@@ -0,0 +1,153 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+from datetime import UTC, datetime
+from typing import Any
+
+from app.core.errors import AppError
+from app.models import Dataset
+
+
+@dataclass(frozen=True)
+class TemporalInterval:
+    start: datetime | None
+    end: datetime | None
+    granularity: str | None
+
+    @property
+    def bounded(self) -> bool:
+        return self.start is not None and self.end is not None
+
+    def as_dict(self) -> dict[str, Any]:
+        return {
+            "start": self.start.isoformat() if self.start else None,
+            "end": self.end.isoformat() if self.end else None,
+            "granularity": self.granularity,
+        }
+
+
+class TemporalCompatibilityService:
+    @staticmethod
+    def ensure_detection_source_supported(dataset: Dataset) -> None:
+        metadata = dataset.source_metadata if isinstance(dataset.source_metadata, dict) else {}
+        if metadata.get("supports_detection") is False:
+            raise AppError(
+                code="DETECTION_SOURCE_TEMPORALLY_UNSUPPORTED",
+                message="The selected raster edition is not approved for the configured detection model",
+                details={
+                    "dataset_id": str(dataset.id),
+                    "source_name": dataset.source_name,
+                    "product_key": metadata.get("product_key"),
+                    "observed_at": TemporalCompatibilityService._iso(dataset.observed_at),
+                    "valid_from": TemporalCompatibilityService._iso(dataset.valid_from),
+                    "valid_to": TemporalCompatibilityService._iso(dataset.valid_to),
+                },
+                status_code=422,
+            )
+
+    @staticmethod
+    def assess_detection_qa(candidate: Dataset, reference: Dataset) -> dict[str, Any]:
+        candidate_interval = TemporalCompatibilityService._interval(candidate)
+        reference_interval = TemporalCompatibilityService._interval(reference)
+        candidate_historical = TemporalCompatibilityService._is_historical_detection_source(candidate)
+
+        if candidate_historical:
+            if not reference_interval.bounded:
+                TemporalCompatibilityService._raise_mismatch(
+                    candidate,
+                    reference,
+                    candidate_interval,
+                    reference_interval,
+                    "Historical imagery requires a reference dataset with an explicit compatible validity period.",
+                )
+            if not TemporalCompatibilityService._overlaps(candidate_interval, reference_interval):
+                TemporalCompatibilityService._raise_mismatch(
+                    candidate,
+                    reference,
+                    candidate_interval,
+                    reference_interval,
+                    "The historical imagery and reference dataset validity periods do not overlap.",
+                )
+
+        if (
+            candidate_interval.bounded
+            and reference_interval.bounded
+            and not TemporalCompatibilityService._overlaps(candidate_interval, reference_interval)
+        ):
+            TemporalCompatibilityService._raise_mismatch(
+                candidate,
+                reference,
+                candidate_interval,
+                reference_interval,
+                "The candidate and reference dataset validity periods do not overlap.",
+            )
+
+        return {
+            "status": "compatible",
+            "policy": "explicit_interval_overlap_for_historical_sources",
+            "candidate_dataset_id": str(candidate.id),
+            "reference_dataset_id": str(reference.id),
+            "candidate_historical": candidate_historical,
+            "candidate_interval": candidate_interval.as_dict(),
+            "reference_interval": reference_interval.as_dict(),
+        }
+
+    @staticmethod
+    def _is_historical_detection_source(dataset: Dataset) -> bool:
+        metadata = dataset.source_metadata if isinstance(dataset.source_metadata, dict) else {}
+        if metadata.get("supports_detection") is False:
+            return True
+        product_key = str(metadata.get("product_key") or "").strip().lower()
+        return dataset.source_name == "digitaal_vlaanderen_orthophoto" and product_key not in {"", "most_recent"}
+
+    @staticmethod
+    def _interval(dataset: Dataset) -> TemporalInterval:
+        start = TemporalCompatibilityService._utc(dataset.valid_from or dataset.observed_at)
+        end = TemporalCompatibilityService._utc(dataset.valid_to)
+        granularity = dataset.temporal_granularity
+
+        if start is not None and end is None and granularity == "year":
+            end = datetime(start.year, 12, 31, 23, 59, 59, tzinfo=UTC)
+        elif start is not None and end is None and granularity == "day":
+            end = start.replace(hour=23, minute=59, second=59, microsecond=999999)
+
+        return TemporalInterval(start=start, end=end, granularity=granularity)
+
+    @staticmethod
+    def _overlaps(left: TemporalInterval, right: TemporalInterval) -> bool:
+        if not left.bounded or not right.bounded:
+            return True
+        return left.start <= right.end and right.start <= left.end
+
+    @staticmethod
+    def _raise_mismatch(
+        candidate: Dataset,
+        reference: Dataset,
+        candidate_interval: TemporalInterval,
+        reference_interval: TemporalInterval,
+        message: str,
+    ) -> None:
+        raise AppError(
+            code="DETECTION_QA_TEMPORAL_MISMATCH",
+            message=message,
+            details={
+                "candidate_dataset_id": str(candidate.id),
+                "reference_dataset_id": str(reference.id),
+                "candidate_interval": candidate_interval.as_dict(),
+                "reference_interval": reference_interval.as_dict(),
+            },
+            status_code=422,
+        )
+
+    @staticmethod
+    def _utc(value: datetime | None) -> datetime | None:
+        if value is None:
+            return None
+        if value.tzinfo is None:
+            return value.replace(tzinfo=UTC)
+        return value.astimezone(UTC)
+
+    @staticmethod
+    def _iso(value: datetime | None) -> str | None:
+        normalized = TemporalCompatibilityService._utc(value)
+        return normalized.isoformat() if normalized else None

backend/tests/test_rc_detection_temporal_safety.py

@@ -0,0 +1,137 @@
+from __future__ import annotations
+
+from datetime import UTC, datetime
+from pathlib import Path
+from uuid import uuid4
+
+import pytest
+
+from app.core.errors import AppError
+from app.models import Dataset
+from app.services.temporal_compatibility_service import TemporalCompatibilityService
+
+
+ROOT = Path(__file__).parents[2]
+
+
+def dataset(
+    *,
+    dataset_type: str,
+    source_name: str,
+    observed_at: datetime | None = None,
+    valid_from: datetime | None = None,
+    valid_to: datetime | None = None,
+    temporal_granularity: str | None = None,
+    source_metadata: dict | None = None,
+) -> Dataset:
+    return Dataset(
+        id=uuid4(),
+        project_id=uuid4(),
+        name="temporal-source",
+        dataset_type=dataset_type,
+        source=source_name,
+        source_name=source_name,
+        observed_at=observed_at,
+        valid_from=valid_from,
+        valid_to=valid_to,
+        temporal_granularity=temporal_granularity,
+        source_metadata=source_metadata,
+    )
+
+
+def test_historical_orthophoto_is_rejected_for_detection() -> None:
+    historical = dataset(
+        dataset_type="raster",
+        source_name="digitaal_vlaanderen_orthophoto",
+        observed_at=datetime(2020, 1, 1, tzinfo=UTC),
+        valid_from=datetime(2020, 1, 1, tzinfo=UTC),
+        valid_to=datetime(2020, 12, 31, 23, 59, 59, tzinfo=UTC),
+        temporal_granularity="year",
+        source_metadata={"product_key": "2020", "supports_detection": False},
+    )
+
+    with pytest.raises(AppError) as exc_info:
+        TemporalCompatibilityService.ensure_detection_source_supported(historical)
+
+    assert exc_info.value.code == "DETECTION_SOURCE_TEMPORALLY_UNSUPPORTED"
+    assert exc_info.value.status_code == 422
+
+
+def test_historical_detection_qa_rejects_current_reference() -> None:
+    historical = dataset(
+        dataset_type="raster",
+        source_name="digitaal_vlaanderen_orthophoto",
+        valid_from=datetime(2020, 1, 1, tzinfo=UTC),
+        valid_to=datetime(2020, 12, 31, 23, 59, 59, tzinfo=UTC),
+        temporal_granularity="year",
+        source_metadata={"product_key": "2020", "supports_detection": False},
+    )
+    current_reference = dataset(
+        dataset_type="vector",
+        source_name="grb",
+        valid_from=datetime(2026, 7, 1, tzinfo=UTC),
+        valid_to=datetime(2026, 7, 31, 23, 59, 59, tzinfo=UTC),
+        temporal_granularity="month",
+    )
+
+    with pytest.raises(AppError) as exc_info:
+        TemporalCompatibilityService.assess_detection_qa(historical, current_reference)
+
+    assert exc_info.value.code == "DETECTION_QA_TEMPORAL_MISMATCH"
+    assert exc_info.value.status_code == 422
+
+
+def test_historical_detection_qa_accepts_overlapping_reference_edition() -> None:
+    historical = dataset(
+        dataset_type="raster",
+        source_name="digitaal_vlaanderen_orthophoto",
+        valid_from=datetime(2020, 1, 1, tzinfo=UTC),
+        valid_to=datetime(2020, 12, 31, 23, 59, 59, tzinfo=UTC),
+        temporal_granularity="year",
+        source_metadata={"product_key": "2020", "supports_detection": False},
+    )
+    historical_reference = dataset(
+        dataset_type="vector",
+        source_name="manual",
+        valid_from=datetime(2020, 6, 1, tzinfo=UTC),
+        valid_to=datetime(2020, 6, 30, 23, 59, 59, tzinfo=UTC),
+        temporal_granularity="month",
+    )
+
+    result = TemporalCompatibilityService.assess_detection_qa(historical, historical_reference)
+
+    assert result["status"] == "compatible"
+    assert result["candidate_historical"] is True
+    assert result["candidate_interval"]["start"].startswith("2020-01-01")
+    assert result["reference_interval"]["start"].startswith("2020-06-01")
+
+
+def test_current_source_with_unbounded_current_reference_remains_supported() -> None:
+    current = dataset(
+        dataset_type="raster",
+        source_name="digitaal_vlaanderen_orthophoto",
+        observed_at=datetime(2026, 7, 17, tzinfo=UTC),
+        valid_from=datetime(2026, 7, 17, tzinfo=UTC),
+        temporal_granularity="snapshot",
+        source_metadata={"product_key": "most_recent", "supports_detection": True},
+    )
+    current_reference = dataset(
+        dataset_type="vector",
+        source_name="grb",
+        observed_at=datetime(2026, 7, 16, tzinfo=UTC),
+        temporal_granularity="snapshot",
+    )
+
+    TemporalCompatibilityService.ensure_detection_source_supported(current)
+    result = TemporalCompatibilityService.assess_detection_qa(current, current_reference)
+
+    assert result["status"] == "compatible"
+    assert result["candidate_historical"] is False
+
+
+def test_detection_frontend_has_no_implicit_first_raster_fallback() -> None:
+    source = (ROOT / "frontend" / "src" / "hooks" / "useDetectionWorkflow.ts").read_text(encoding="utf-8")
+
+    assert "rasterDatasets[0]" not in source
+    assert "setSelectedDetectionDatasetId(rasterDatasets" not in source
+    assert "const datasetId = selectedDetectionDatasetId" in source

backend/tests/test_rc_runtime_observability.py

@@ -0,0 +1,31 @@
+from pathlib import Path
+
+from fastapi.testclient import TestClient
+
+from app.main import app
+
+
+ROOT = Path(__file__).parents[2]
+
+
+def test_valid_request_id_is_returned() -> None:
+    response = TestClient(app).get("/health/live", headers={"x-request-id": "rc3-check.123"})
+
+    assert response.status_code == 200
+    assert response.headers["x-request-id"] == "rc3-check.123"
+
+
+def test_unsafe_request_id_is_replaced() -> None:
+    response = TestClient(app).get("/health/live", headers={"x-request-id": "unsafe request/id"})
+
+    assert response.status_code == 200
+    assert response.headers["x-request-id"] != "unsafe request/id"
+    assert " " not in response.headers["x-request-id"]
+
+
+def test_runtime_report_is_read_only_by_default_and_requires_confirmation() -> None:
+    source = (ROOT / "scripts" / "runtime_state_report.py").read_text(encoding="utf-8")
+
+    assert '"mode": "read_only"' in source
+    assert "if args.reconcile and args.confirm != RECONCILE_CONFIRMATION" in source
+    assert "RuntimeReconciliationService.reconcile(db)" in source

backend/tests/test_sprint8_detection_foundation.py

@@ -105,6 +105,25 @@ def test_non_raster_dataset_request_is_rejected() -> None:
     assert getattr(exc_info.value, "code", None) == "INVALID_DATASET_TYPE"
 
 
+def test_historical_raster_marked_unsupported_is_rejected_before_run_creation() -> None:
+    db, project_id, dataset_id = _project_and_dataset()
+    source = db.get(Dataset, dataset_id)
+    source.source_name = "digitaal_vlaanderen_orthophoto"
+    source.source_metadata = {"product_key": "2020", "supports_detection": False}
+
+    with pytest.raises(Exception) as exc_info:
+        DetectionService.run_detection(
+            db=db,
+            project_id=project_id,
+            dataset_id=dataset_id,
+            model_id="yolo-placeholder",
+            confidence_threshold=0.5,
+        )
+
+    assert getattr(exc_info.value, "code", None) == "DETECTION_SOURCE_TEMPORALLY_UNSUPPORTED"
+    assert db.added == []
+
+
 def test_fixture_detector_persists_detections_only_with_explicit_fixture_mode() -> None:
     db, project_id, dataset_id = _project_and_dataset()
 

backend/tests/test_sprint8c_detection_visualization_qa.py

@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 import json
+from datetime import UTC, datetime
 from uuid import uuid4
 
 import pytest
@@ -11,7 +12,7 @@ from shapely.geometry import Polygon, box
 from app.core.errors import AppError
 from app.main import app
 from app.db.session import get_db
-from app.models import AnalysisRun, Dataset, Detection, Job, Metric, Project, QualityCheck, VectorFeature
+from app.models import AnalysisRun, Dataset, Detection, Metric, QualityCheck, VectorFeature
 from app.services.detection_service import DetectionService
 
 
@@ -89,6 +90,17 @@ def _detection(project_id, dataset_id, analysis_run_id, class_name="building", c
     )
 
 
+def _source_dataset(project_id, dataset_id):
+    return Dataset(
+        id=dataset_id,
+        project_id=project_id,
+        name="source.tif",
+        dataset_type="raster",
+        source="manual",
+        source_name="manual",
+    )
+
+
 def test_detection_geojson_feature_collection_shape() -> None:
     project_id = uuid4()
     dataset_id = uuid4()
@@ -206,6 +218,7 @@ def test_detection_qa_persists_quality_check_and_metrics() -> None:
     db = FakeSession(
         objects={
             (AnalysisRun, analysis_run_id): AnalysisRun(id=analysis_run_id, project_id=project_id, dataset_id=dataset_id, analysis_type="detection", status="success", parameters_json={}),
+            (Dataset, dataset_id): _source_dataset(project_id, dataset_id),
             (Dataset, reference_dataset_id): reference_dataset,
         },
         query_rows={Detection: [detection], VectorFeature: [reference_feature]},
@@ -227,6 +240,8 @@ def test_detection_qa_persists_quality_check_and_metrics() -> None:
     assert result["quality_check_id"] == str(quality_checks[0].id)
     assert quality_checks[0].analysis_run_id == analysis_run_id
     assert quality_checks[0].reference_dataset_id == reference_dataset_id
+    assert quality_checks[0].parameters_json["temporal_compatibility"]["status"] == "compatible"
+    assert quality_checks[0].findings_json["temporal_compatibility"]["status"] == "compatible"
     assert [metric.metric_key for metric in metrics] == [
         "precision",
         "recall",
@@ -237,6 +252,53 @@ def test_detection_qa_persists_quality_check_and_metrics() -> None:
     ]
 
 
+def test_detection_qa_rejects_non_overlapping_historical_reference_editions() -> None:
+    project_id = uuid4()
+    dataset_id = uuid4()
+    reference_dataset_id = uuid4()
+    analysis_run_id = uuid4()
+    source_dataset = _source_dataset(project_id, dataset_id)
+    source_dataset.source_name = "digitaal_vlaanderen_orthophoto"
+    source_dataset.source_metadata = {"product_key": "2020", "supports_detection": False}
+    source_dataset.valid_from = datetime(2020, 1, 1, tzinfo=UTC)
+    source_dataset.valid_to = datetime(2020, 12, 31, 23, 59, 59, tzinfo=UTC)
+    reference_dataset = Dataset(
+        id=reference_dataset_id,
+        project_id=project_id,
+        name="current-grb.geojson",
+        dataset_type="vector",
+        source="grb",
+        source_name="grb",
+        dataset_role="reference",
+        valid_from=datetime(2026, 7, 1, tzinfo=UTC),
+        valid_to=datetime(2026, 7, 31, 23, 59, 59, tzinfo=UTC),
+    )
+    db = FakeSession(
+        objects={
+            (AnalysisRun, analysis_run_id): AnalysisRun(
+                id=analysis_run_id,
+                project_id=project_id,
+                dataset_id=dataset_id,
+                analysis_type="detection",
+                status="success",
+                parameters_json={},
+            ),
+            (Dataset, dataset_id): source_dataset,
+            (Dataset, reference_dataset_id): reference_dataset,
+        },
+    )
+
+    with pytest.raises(AppError) as exc_info:
+        DetectionService.compare_detections_with_reference(
+            db=db,
+            analysis_run_id=analysis_run_id,
+            reference_dataset_id=reference_dataset_id,
+        )
+
+    assert exc_info.value.code == "DETECTION_QA_TEMPORAL_MISMATCH"
+    assert db.added == []
+
+
 def test_detection_qa_no_match_case_persists_zero_scores() -> None:
     project_id = uuid4()
     dataset_id = uuid4()
@@ -260,6 +322,7 @@ def test_detection_qa_no_match_case_persists_zero_scores() -> None:
     db = FakeSession(
         objects={
             (AnalysisRun, analysis_run_id): AnalysisRun(id=analysis_run_id, project_id=project_id, dataset_id=dataset_id, analysis_type="detection", status="success", parameters_json={}),
+            (Dataset, dataset_id): _source_dataset(project_id, dataset_id),
             (Dataset, reference_dataset_id): reference_dataset,
         },
         query_rows={Detection: [detection], VectorFeature: [reference_feature]},
@@ -311,6 +374,7 @@ def test_configured_yolo_qa_requires_persisted_tile_manifest_provenance() -> Non
                 model_name="yolo-configured",
                 parameters_json={"model_id": "yolo-configured"},
             ),
+            (Dataset, dataset_id): _source_dataset(project_id, dataset_id),
             (Dataset, reference_dataset_id): reference_dataset,
         },
         query_rows={Detection: [detection], VectorFeature: [reference_feature]},
@@ -391,6 +455,7 @@ def test_detection_qa_excludes_references_outside_persisted_tile_coverage(tmp_pa
                     "tile_manifest_path": str(manifest_path),
                 },
             ),
+            (Dataset, dataset_id): _source_dataset(project_id, dataset_id),
             (Dataset, reference_dataset_id): reference_dataset,
         },
         query_rows={Detection: [detection], VectorFeature: [inside_reference, outside_reference]},
@@ -452,6 +517,7 @@ def test_detection_qa_reports_box_to_footprint_diagnostic_without_changing_stric
                     "tile_manifest_path": str(manifest_path),
                 },
             ),
+            (Dataset, dataset_id): _source_dataset(project_id, dataset_id),
             (Dataset, reference_dataset_id): reference_dataset,
         },
         query_rows={Detection: [detection], VectorFeature: [reference_feature]},