document the analysis contracts and correct the job-queue claim

The README's architecture diagram and stack list promised Redis + RQ. There is
no such thing in this codebase: redis_url is a settings field nothing reads,
rq is not a dependency, and the only worker is an asyncio poller over a
Postgres job table. Describe what exists.

API_CONTRACTS and KNOWN_LIMITATIONS gain the rules a reader needs to interpret
a result: deterministic confidence-ranked matching, the precision/recall curve,
the three flood-hazard cell populations, the sub-cell selection fallback, the
whole-feature object count next to clipped area metrics, and the required
manifest CRS. The new limitations are stated as limits rather than buried:
raster analysis has no sub-cell precision, and the object count is not
fractional.

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

docs/API_CONTRACTS.md

@@ -617,13 +617,39 @@ is not a temporal observation and receives no fabricated `observed_at` value.
 
 ### POST `/api/v1/projects/{project_id}/datasets/{dataset_id}/raster/flood-hazard/select`
 
-Returns mapped positive-depth area in hectares, share of the selection, mean,
-P90 and maximum modeled local depth and `modelled_max_depth_area_integral_m3`.
-Every result identifies mechanism, climate context, probability class and
-return period. The integral sums local modeled maximum depth times cell area;
-it is explicitly not concurrent flood storage, permanent waterbody content,
-current water level or bathymetry. These unsupported metrics remain listed in
-the response.
+Returns mapped positive-depth area in hectares, share of the *modelled* area,
+mean, P90 and maximum modeled local depth and
+`modelled_max_depth_area_integral_m3`. Every result identifies mechanism,
+climate context, probability class and return period. The integral sums local
+modeled maximum depth times cell area; it is explicitly not concurrent flood
+storage, permanent waterbody content, current water level or bathymetry. These
+unsupported metrics remain listed in the response.
+
+Three cell populations are reported separately, because conflating them turns
+missing data into a claim of safety:
+
+- `selected_cell_count` — cells inside the drawn selection;
+- `valid_cell_count` / `no_data_cell_count` — the split between cells the VMM
+  raster models and cells it does not;
+- `inundated_cell_count` — modelled cells with a positive depth.
+
+`inundated_fraction` and `modelled_inundated_share_pct` are shares of the
+modelled cells, not of the drawn selection. `inundated_fraction` is `null` when
+nothing was modelled at all. `data_coverage_ratio`, `model_coverage_pct`,
+`modelled_area_ha` and `selection_area_ha` make the difference between the
+drawn area and the analysed area explicit, and `coverage_warning` states it in
+words. A selection reaching past the modelled extent previously reported a
+diluted risk share for the whole rectangle.
+
+**Selections finer than one source cell.** `geometry_mask` selects a cell when
+its centre falls inside the geometry, so a rectangle smaller than a cell — or
+one landing between four centres — selected nothing and the analysis returned
+zeros indistinguishable from "nothing here". Every raster selection now falls
+back to the cells the geometry touches and reports that in
+`cell_selection_warning` (`coverage_warning` for flood hazard), because the
+result then covers more ground than was drawn. This applies to terrain,
+bathymetry, thematic raster and flood hazard, in both the single-dataset and
+the partitioned paths.
 
 ### POST `/api/v1/projects/{project_id}/datasets/raster/flood-hazard/select`
 
@@ -1056,6 +1082,21 @@ Return vector bounds and feature count.
 
 ### POST `/api/v1/projects/{project_id}/datasets/{dataset_id}/vector/select`
 
+Selection metrics describe two different populations and now say so.
+`intersection_area` and `intersection_length` clip each feature to the
+selection, while the object count treats any feature touching the selection as
+whole — which is what an operator expects from "objecten", but overstates the
+count along every edge. The response therefore adds
+`fully_covered_feature_count`, `partially_covered_feature_count` and
+`selection_edge_warning`, and marks the count metric as an estimate whenever
+the selection cuts features. Area and length metrics stay exact and do not
+inherit that caveat.
+
+Partitioned selection (`/datasets/vector/partitions/select`) de-duplicates on
+`source_feature_id` across municipal partitions for the returned geometry as
+well as for the count. A feature on a shared boundary was previously counted
+once but drawn once per partition.
+
 Read-only spatial selection over persisted `vector_features`.
 
 Request:
@@ -1619,6 +1660,44 @@ reproducibility. Clients must not submit arbitrary model paths.
 
 GeoIntel does not download model weights automatically. Configured YOLO runs read existing tile files from the manifest, convert YOLO pixel-space boxes to EPSG:4326 detection polygons and persist detections as first-class records.
 
+The manifest must carry explicit CRS metadata (`crs`, `source_crs` or
+`dataset_crs`). A manifest without it fails with
+`DETECTION_TILE_MANIFEST_INVALID` rather than being georeferenced against an
+assumed EPSG:4326, which would place detections plausibly but wrongly.
+
+Tiles are read with rasterio: the visible RGB bands are selected explicitly and
+percentile-stretched to 8-bit, so 16-bit and 4-band (RGB + NIR) orthophotos
+reach the model as the kind of image it was trained on. Tiles are predicted in
+batches of `YOLO_BATCH_SIZE`.
+
+Post-processing removes two artefacts of tiled inference:
+
+- boxes truncated by an *interior* tile edge are dropped, because the
+  overlapping neighbouring tile observed the same object completely
+  (`YOLO_SUPPRESS_TILE_EDGE_DETECTIONS`, default on). Boxes against the outer
+  raster edge are kept;
+- duplicates are suppressed on IoU *and* on intersection-over-smaller-area, so
+  an object wider than the tile overlap does not survive as two partial boxes.
+
+`result_json` reports `raw_detection_count`, `suppressed_detection_count`,
+`tile_edge_truncated_count`, `duplicate_iou_threshold` and
+`containment_suppression_threshold`.
+
+### POST `/api/v1/detection/run-async`
+
+Same request body as `POST /api/v1/detection/run`, but queues the run instead of
+executing it inside the request, and returns a `JobRead`. Tiled GPU inference
+over up to `YOLO_MAX_TILES` tiles takes minutes; performing it in the request
+holds a worker thread and times the client out. Cheap validation (project,
+dataset, dataset type) still happens synchronously, so an invalid request is
+rejected immediately rather than by a job that fails minutes later.
+
+Queued jobs are executed by the background analysis worker
+(`GEOINTEL_ANALYSIS_WORKER_ENABLED`, poll interval
+`GEOINTEL_ANALYSIS_WORKER_POLL_SECONDS`), which claims a job before dispatching
+it so the same run is never started twice. Poll `GET /api/v1/jobs/{id}` for
+progress. `POST /api/v1/segmentation/run-async` behaves identically.
+
 Unavailable model response:
 
 ```json
@@ -1759,6 +1838,30 @@ candidate geometry versus the persisted reference footprint. Envelope results
 are explicitly `diagnostic_only` and are persisted in
 `quality_checks.findings_json`; they never replace or inflate canonical metrics.
 
+`box_to_footprint_diagnostics.candidate_geometry_mode` reports whether the
+candidates are `axis_aligned_boxes` or `footprint_polygons`. For a box detector
+the strict footprint IoU has a ceiling below 1 on rotated or non-rectangular
+buildings, and the response says so in `warnings` and in `interpretation`.
+
+Matching is deterministic. Candidates are ranked by confidence, highest first,
+with feature identity as tiebreaker, before the greedy IoU assignment. Database
+row order is not usable for this: every detection in a run shares one
+transaction timestamp, so ordering by `created_at` left the assignment — and
+therefore the score and the false-positive evidence shown to a reviewer —
+undefined between identical runs.
+
+The response also returns `precision_recall_curve`: precision, recall and F1 at
+every confidence value present in the run, plus `average_precision`, `best_f1`
+and `best_f1_threshold`. A single F1 describes one operating point and cannot
+compare two models whose calibration differs; the curve can. `average_precision`,
+`best_f1` and `best_f1_threshold` are persisted as `metrics` rows alongside the
+existing ones.
+
+Segmentation QA (`POST /api/v1/segmentation/runs/{analysis_run_id}/qa/reference`)
+applies the same tile-coverage clipping and returns the same `coverage` block.
+Without it, every reference feature outside the inferred tiles counted as a
+false negative and recall was understated by an arbitrary amount.
+
 Configured-YOLO QA fails closed with `DETECTION_QA_COVERAGE_UNAVAILABLE` when
 manifest provenance is absent, `DETECTION_QA_COVERAGE_MISMATCH` when it belongs
 to another raster, `DETECTION_QA_COVERAGE_INVALID` when bounds/CRS are invalid,