feat(heads): production per-concept heads — train + score backend (#114 A)

The eval (#1130) proved the frozen-embedding + trained-head spine; this lands
its production form (the first of three slices that make heads the suggestion
source, replacing Camie + centroid).

- tag_head: one logistic-regression head per general/character concept with
  enough labelled positives. Weights (pgvector), honest CV-derived suggest
  threshold + earned-auto-apply point, and per-concept quality metrics.
- head_training_run: persisted batch lifecycle (mirrors tag_eval_run) so the
  admin card shows live + historical status across navigation.
- services/ml/heads.py: TRAIN (sync, ml worker, reuses tag_eval's proven data
  loaders + metric math so production heads match measured eval numbers) and
  SCORE (async, API worker — numpy via pgvector, no scikit-learn): score one
  image's embedding against all heads → the rail's suggestions, cached on
  (count, max trained_at) so a retrain invalidates without per-request loads.
- tasks.ml.train_heads (ml queue, commits per head so a kill leaves progress)
  + recover_stalled_head_training_runs sweep + retention(20) + 5-min beat
  (rule 89).
- api/heads.py: POST /api/heads/train (one run at a time, 409 guard) + GET
  /api/heads (count, graduated, last-trained, running, per-concept table,
  recent runs).
- ml_settings: head_min_positives + head_auto_apply_precision, tunable via
  /api/ml/settings.

Scoring isn't wired into the rail yet (slice C) and the admin UI is slice B —
this slice makes training + scoring exist and CI-verifiable. 'precision' column
stored as precision_cv (SQL reserved word). Migration 0058.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa

backend/app/models/__init__.py

@@ -26,10 +26,12 @@ from .series_suggestion import SeriesSuggestion
 from .source import Source
 from .subscribestar_failed_media import SubscribeStarFailedMedia
 from .subscribestar_seen_media import SubscribeStarSeenMedia
+from .head_training_run import HeadTrainingRun
 from .tag import Tag, TagKind, image_tag
 from .tag_alias import TagAlias
 from .tag_allowlist import TagAllowlist
 from .tag_eval_run import TagEvalRun
+from .tag_head import TagHead
 from .tag_positive_confirmation import TagPositiveConfirmation
 from .tag_reference_embedding import TagReferenceEmbedding
 from .tag_suggestion_rejection import TagSuggestionRejection
@@ -65,9 +67,11 @@ __all__ = [
     "ImportSettings",
     "LibraryAuditRun",
     "MLSettings",
+    "HeadTrainingRun",
     "TagAlias",
     "TagAllowlist",
     "TagEvalRun",
+    "TagHead",
     "TagPositiveConfirmation",
     "TagReferenceEmbedding",
     "TagSuggestionRejection",