Goal
Reduce the backlog of hypotheses with missing or zero composite_score values. Scored hypotheses are easier to route into debates, rank in the Exchange, and evaluate for world-model promotion.
Acceptance Criteria
☐ A concrete batch of unscored hypotheses receives composite_score > 0
☐ Each score is grounded in evidence, novelty, falsifiability, and disease relevance
☐ The remaining unscored count is verified before and after the update
Approach
Query hypotheses where composite_score IS NULL OR composite_score = 0.
Review hypothesis text, evidence fields, linked papers, and debate context.
Assign justified composite scores using existing SciDEX scoring conventions.
Verify with a PostgreSQL count query and spot-check updated rows.Dependencies
c488a683-47f - Agora quest
Dependents
- Exchange market ranking and hypothesis prioritization tasks
Work Log
2026-04-20 - Quest engine template
- Created reusable spec for quest-engine generated unscored-hypothesis scoring tasks.
2026-04-22 13:22 UTC - Task f84c8925-7208-4ef8-a8c3-1bc55343880a
- Status: Completed (already largely resolved by prior agents)
- Before: 1 hypothesis with composite_score IS NULL or 0 (h-a2b3485737)
- After: 0 hypotheses with composite_score IS NULL or 0
- Change: Scored 1 remaining hypothesis with 10-dimension composite scoring
- Script:
scripts/score_final_unscored.py (created, applies 10-dimension rubric matching score_36_unscored_hypotheses.py pattern)
- Scored hypothesis h-a2b3485737 (Differential Calpain-Mediated Cleavage):
- composite_score: 0.4199
- mechanistic_plausibility: 0.560
- evidence_strength: 0.599
- novelty: 0.400
- feasibility: 0.400
- therapeutic_potential: 0.450
- druggability: 0.350
- safety_profile: 0.400
- competitive_landscape: 0.500
- data_availability: 0.680
- reproducibility: 0.600
- Verification:
SELECT COUNT(*) FROM hypotheses WHERE composite_score IS NULL OR composite_score = 0 returns 0