refactor(ml): drop dead tagger/suggestion settings + columns (#1199)

Hygiene follow-up to the Camie retirement (#1189) — these were left inert to
bound that change; nothing reads them now. Migration 0068 drops:
- ml_settings: tagger_store_floor, tagger_model_version, suggestion_threshold_
  character/general (already dead pre-retirement — scoring uses per-head
  thresholds), video_min_tag_frames (only the deleted video-prediction
  aggregator used it).
- image_record: tagger_model_version (no writer), centroid_scores (dead JSON
  cache, no reader).

Also: ml_admin _EDITABLE/GET/_validate pruned (dropped the store-floor invariant
+ video_min_tag_frames check); MLThresholdSliders trimmed to a video-embedding
card (interval + max frames only); importer no longer resets the dropped cols;
download_models drops the Camie fetch; stale CASCADE comments in cleanup_service
no longer name the removed tables. Tests updated.

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

backend/app/models/image_record.py

@@ -9,7 +9,6 @@ from datetime import datetime
 
 from pgvector.sqlalchemy import Vector
 from sqlalchemy import (
-    JSON,
     BigInteger,
     DateTime,
     Enum,
@@ -77,19 +76,13 @@ class ImageRecord(Base):
         ForeignKey("artist.id", ondelete="SET NULL"), nullable=True, index=True
     )
 
-    # ML fields (populated by FC-2's ml-worker). Per-tag predictions live in the
-    # normalized image_prediction table (#768) — the tagger_predictions JSON
-    # column was dropped in migration 0046. tagger_model_version stays as the
-    # "has this been tagged / is it current?" signal the backfill sweep reads.
-    tagger_model_version: Mapped[str | None] = mapped_column(String(128), nullable=True)
-    # 1152 = SigLIP-so400m embedding dim. Swapping models in FC-2 may require
-    # a column-width migration.
+    # ML fields (populated by the ml-worker / GPU agent). 1152 = SigLIP-so400m
+    # embedding dim; siglip_model_version stamps which model produced it (so an
+    # operator model swap, #1190, can re-embed the stale rows). A different-dim
+    # model would need a column-width migration.
     siglip_embedding: Mapped[list[float] | None] = mapped_column(Vector(1152), nullable=True)
     siglip_model_version: Mapped[str | None] = mapped_column(String(128), nullable=True)
 
-    # Centroid score cache (populated post-tagging)
-    centroid_scores: Mapped[dict | None] = mapped_column(JSON, nullable=True)
-
     created_at: Mapped[datetime] = mapped_column(
         DateTime(timezone=True), nullable=False, server_default=func.now()
     )