feat(wip): soft title tier — sketch/doodle vocab + ring-loud audit (#1474)

Extends WIP title-tagging to lower-precision cues (sketch/doodle/scribble) safely.

- wip_title.py: soft matcher (word-anchored; sketchbook/kadoodle don't trip it);
  WIP_TITLE_SOFT_SOURCE + soft SQL prefilter; apply_wip_image_tags takes a source arg.
- training_data._AUTO_SOURCES += 'wip_title_soft' → the soft tier is PROVISIONAL and
  never trains the wip head (a finished "sketch" can't pollute it). Only the hard
  tier (wip_title) + manual train.
- ImportSettings.wip_soft_title_tagging_enabled (OFF by default, opt-in). Migration 0087.
- importer: hard tier wins, soft is the fallback (source wip_title_soft).
- backfill: refactored into a shared _backfill_wip_tier; hard always, soft when enabled.
- heads.soft_wip_conflict_audit + daily beat: score soft-tagged images against content
  heads, flag ring-loud ones (PresentationReview mode=process) for the review strip —
  the operator's "measure if they got falsely tagged" safety.
- api settings toggle; ImportFiltersForm soft toggle.
- tests: soft matcher pos/neg; soft source not a training positive; audit flags
  ring-loud + spares quiet.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

backend/app/models/import_settings.py

@@ -126,6 +126,13 @@ class ImportSettings(Base):
     wip_title_tagging_enabled: Mapped[bool] = mapped_column(
         Boolean, nullable=False, default=True, server_default="true",
     )
+    # Soft WIP title tier (#1474): also tag sketch/doodle/scribble titles, but with
+    # a PROVISIONAL source (`wip_title_soft`) that never trains the head, since these
+    # are lower-precision (a finished "sketch" isn't WIP). OFF by default — a lower-
+    # precision tier is opt-in (the ring-loud audit surfaces false positives).
+    wip_soft_title_tagging_enabled: Mapped[bool] = mapped_column(
+        Boolean, nullable=False, default=False, server_default="false",
+    )
 
     @classmethod
     async def load(cls, session) -> ImportSettings: