Backfills 9,815 type-NULL rows to 'document' (extension classifier, 100% hit)
and 12,109 created_at-NULL rows via five batches:
C1 filepath_stat: 9,649 filesystem mtime via metadata.filepath
C2 watcher_state_unique: 676 unique source-name lookup in watcher_state
C3 watcher_state_collision_pick_latest_of_N:
234 collision; most-recent watcher mtime
C4 chatgpt_export: 1,548 convo create_time from export JSONs
(168/168 distinct convo_ids resolved)
C5 sentinel: 2 2026-04-26T00:00:00Z (pgvector migration date)
Provenance written to metadata.type_source and metadata.created_at_source
on every row changed by this run. type_source is empty on rows where the
type field was already populated pre-run; in those cases the snapshot
table is the source of truth for what changed.
Snapshot: embeddings_backup_2026_05_03 (CREATE TABLE AS SELECT id, type,
created_at, metadata FROM embeddings; 14,069 rows; revertable via id-join).
Verification:
V1 live counts: type_null=0 ca_null=0
V2 spot-check 11 rows across cohorts: provenance correct
V3 snapshot intact: 14,069 rows, pre-backfill NULL counts preserved
V4 cross-check vs snapshot: reconciles per-provenance to dry-run
Read-side use (B + C: writer enforcement + minimal retrieval read) deferred
to a separate session. The backfill is complete and verified, but the type
and created_at fields are not yet load-bearing — every current reader still
ignores them. Without B+C this lands as data prep, not behavior change.
Read-only inspection of the frame data Mistral produces in Stage 2, in
service of Track 2 substrate design (Step 2.4 operation set spec).
Artifacts:
- New SQL view `stage2_frames_v` over `stage_3_queue.stage2_metadata`
(CREATE OR REPLACE; idempotent; raw JSONB exposed alongside structured
fields so worker-version drift is inspectable).
- Analysis script: frequency, label-hygiene collisions, per-doc count,
co-occurrence (top-K), file-type \u00d7 frame cross-tab, worker-version split,
data-gap accounting, corpus-wide coverage.
- JSON sidecar for diff-across-runs reproducibility.
- Markdown report with explicit Track 2 viability section.
Headline findings:
- Frames cluster meaningfully on the framed-doc subset (subject to
validation on larger samples for the file-type cross-tab).
- Only 56% of corpus has frame coverage. 198 conversation sources bypass
Stage 2 by design (`ingest_conversations.py` writes directly to
embeddings); 339 short docs (<2000 chars) skip Mistral by char-gate;
12 Stage 2 failures.
- All 14 voice notes and all 39 dream outputs are in the data gap.
Primary capture and self-reflection channels are silent to the frame
system. Dreamer cannot frame-condition on its own output.
- 54 normalized label collisions (`Professional Experience` vs
`Professional_Experience`, etc.) — any router must normalize first.
- "Education" is a near-universal frame (36% of frame-extracted docs);
cheap 20-doc hand-inspection diagnostic in report \u00a78 to distinguish
prompt artifact from corpus shape.
- File-type \u00d7 frame stratification is concrete signal that ties to
Improvement #2 (`embeddings.type` backfill); currently NULL for 71% of
rows.
No production code touched. View is droppable; script is read-only.
aaron