Context
PR #136 (merged) adds:
- JATS
article-type extraction in tier0
publication_type field in tier1 (LLM + JATS fallback)
publication_type TEXT column in protocols table
- UI: review article banner + relabeling in protocol card
What's needed
A full pipeline re-run on the A100 to:
- Re-run
tier0_extract.py (extracts article_type from JATS for all PMCIDs)
- Re-run
tier1_extract.py (adds publication_type to all predictions)
- Re-run
build_kg.py (rebuilds DB with 24-column schema)
- Commit the new
data/kg/atlas.db to deploy-render
Known review articles in corpus
- PMC11235738 (Qi 2024, "Modeling, applications and challenges of inner ear organoid") — confirmed review by user
- Currently shows: "Recipe card · 13 signaling factors · 100% grounded"
- Should show: yellow warning banner + "Review-derived summary"
Gate criteria
Review flag only shows when publication_type == 'review' in the DB. Until re-run, all existing cards show unchanged behavior.
Linked to Issue #8 S1 sprint — grounding queue accuracy also benefits from removing review-article reagent noise.
Context
PR #136 (merged) adds:
article-typeextraction in tier0publication_typefield in tier1 (LLM + JATS fallback)publication_type TEXTcolumn in protocols tableWhat's needed
A full pipeline re-run on the A100 to:
tier0_extract.py(extractsarticle_typefrom JATS for all PMCIDs)tier1_extract.py(addspublication_typeto all predictions)build_kg.py(rebuilds DB with 24-column schema)data/kg/atlas.dbto deploy-renderKnown review articles in corpus
Gate criteria
Review flag only shows when
publication_type == 'review'in the DB. Until re-run, all existing cards show unchanged behavior.Linked to Issue #8 S1 sprint — grounding queue accuracy also benefits from removing review-article reagent noise.