Goal
Populate data_support_score for active hypotheses so quality gates can distinguish computationally grounded hypotheses from literature-only or speculative claims. Scores should reflect actual linked analyses, datasets, KG edges, citations, or an explicit lack of supporting data.
Acceptance Criteria
☐ The selected active hypotheses have data_support_score values between 0 and 1
☐ Each score has a concise rationale from linked data, citations, KG edges, or caveats
☐ No support is fabricated where the evidence is absent
☐ The before/after missing-score count is recorded
Approach
Query active hypotheses where data_support_score IS NULL, ordered by impact, market, or composite score.
Inspect linked analyses, papers, datasets, KG edges, and existing evidence fields.
Assign calibrated scores and rationale using existing database write patterns.
Verify score ranges and count reduction.Dependencies
quest-engine-ci - Generates this task when queue depth is low and data-support gaps exist.
Dependents
- Hypothesis ranking, quality gates, and Exchange allocation depend on data-support scores.
Work Log
2026-04-21 21:55 PT — Slot 76 (MiniMax)
- Started: Task spec + AGENTS.md read
- Approach: Calibrated scoring from 5 dimensions:
- KG edge count (0-0.3): strong grounding gets 0.3, none gets 0
- Evidence citations (0-0.4): count + quality (high/medium/year ≥ 2020)
- Debate count (0-0.15): more scrutiny = higher score
- Analysis linkage (0-0.1): formal derivation from debate
- Artifact links (0-0.05): notebooks/datasets = computational grounding
- Output:
scripts/score_data_support.py — reusable, well-documented
- Results:
- Before: 203 promoted/debated missing, 747 total missing
- 20 hypotheses scored: range 0.600–0.950
- After: 183 promoted/debated missing, 727 total missing
- All 20 scores verified in [0, 1] range
- Scores: h-var-95b0f9a6bc (0.950, MAPT), h-var-261452bfb4 (0.950, ACSL4), h-var-22c38d11cd (0.950, ACSL4), h-var-ce41f0efd7 (0.950, TREM2), h-var-97b18b880d (0.950, ALOX15), h-0e675a41 (0.950, HDAC3), h-42f50a4a (0.950, APOE), h-var-f96e38ec20 (0.950, ACSL4), h-11795af0 (0.900, APOE), h-856feb98 (0.900, BDNF), h-var-9e8fc8fd3d (0.900, PVALB), h-d0a564e8 (0.900, APOE), h-807d7a82 (0.900, AQP4), h-48858e2a (0.900, TREM2), h-43f72e21 (0.900, PRKAA1), h-3f02f222 (0.900, BCL2L1), h-var-e4cae9d286 (0.850, LPCAT3), h-var-c56b26facf (0.850, LPCAT3), h-9e9fee95 (0.700, HCRTR1/HCRTR2), hyp-SDA-2026-04-12-20260411-082446-2c1c9e2d-2 (0.600, CHRNA7/BACE1 — no KG edges)
- Committed:
scripts/score_data_support.py [task:f7f4133c-4b99-48ef-888b-fa1b08387a24]
- Result: Done — 20 hypotheses scored with validated evidence-based rationales