Trust & Transparency

This page documents how GaiaLab scores evidence, flags contradictions, and produces reproducible run snapshots. It does not constitute independent validation of any output. Accuracy metrics are self-reported against public datasets; third-party replication has not been performed.

Evidence Ledger
Contradiction Detection
Reproducible Snapshots

Live Trust Scoreboard

These metrics reflect the most recent analysis run stored in this browser. Evidence Density = signal volume relative to gene inputs. Contradiction Index = fraction of papers with conflicting signals (target <15%). Citation Coverage = claims backed by ≥1 PubMed citation, post-polarity gating. Grounded Evidence = pathways & strategies with confirmed supporting polarity — supplementary topic themes excluded from denominator. Quality Score = weighted sum of paper count, insight count, and full-text bonus, scaled by a grounding multiplier (0.5–1.0). Hover each metric for exact definitions. Run a new analysis to refresh.
Evidence Density (EDS) ?
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Signal volume relative to inputs. Target: >60%
Contradiction Index (CI) ?
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Conflicting evidence ratio. Target: <15%
Citation Coverage ?
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Claims backed by PubMed citations. Target: >70%
Quality Score ?
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Composite confidence. Target: ≥80/100
Cite-F1 (ALCE) ?
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Claim-level citation quality. Target: >60%
Quality Score — Run History
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How to Read These Signals

Trust signals update after each analysis run. Use Evidence Density and Citation Coverage to gauge data richness. Monitor the Contradiction Index to identify areas requiring manual review. The Quality Score provides a single composite measure for triage decisions.

Platform Integrity Snapshot

Live operational metrics from the additive Postgres analytics spine. This complements the browser-local latest run with cross-run export quality, workspace collaboration, and platform activity signals.

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Latest Run Detail

Detailed breakdown of the most recent analysis. Data is stored locally in this browser session.

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Pipeline Overview

A six-stage pipeline that keeps evidence provenance, scoring, and replayability transparent at every step.

1. Input Normalization
2. Multi-source Retrieval
3. Cross-validation
4. Consensus Scoring
5. Evidence Ledger
6. Snapshot Archive

Evidence Ledger Format

Each claim is tracked with source identifiers, confidence tier, and constraint notes.

Claim Evidence IDs Confidence Notes
EGFR is implicated in NSCLC PMID:123456; OpenTargets:OTAR1234 High Strong agreement across sources
KRAS drives MAPK activation PMID:987654; KEGG:hsa04010 High Consensus across pathway databases

Confidence Scoring Policy

Confidence is consensus-weighted across independent sources. The formula applies diminishing returns, contradiction penalties, and a hard cap at 0.95 to prevent false certainty.

Scoring formula
agreement_bonus = 0.12 * (1 - exp(-0.9 * A))
contradiction_penalty = 0.25 * CI
final = clamp(base + bonus - penalty, 0, 0.95)

A = number of independent source domains in agreement
CI = contradiction index for this claim

Contradiction Handling

When sources disagree, GaiaLab does not average away conflict. Contradictions are surfaced with supporting and refuting evidence for manual adjudication.

Example: Conflicting evidence across cohorts
Severity: Medium
Support: PMID:112233
Refute: PMID:445566

Reproducible Snapshots

Every analysis produces a replayable snapshot containing inputs, source versions, scoring policy, model configuration, evidence ledger, and visualization state.

Snapshots are also accessible at /analysis/{id} after each run. The analysis ID appears in every API response under analysisId.

Prediction Calibration

GaiaLab prospectively records drug repurposing predictions and checks them against ClinicalTrials.gov outcomes. A well-calibrated system's 80% confidence predictions should be correct ~80% of the time.

Concordance Rate (self-defined)
77%
Candidates with a matching trial entry — retrospective + prospective combined. Not a measure of drug efficacy.
Precision@10
100%
Top 10 predictions matched active trials
AUROC
0.5448
0.50 = random baseline — see note below
How to interpret these metrics honestly

Research Direction Rate (RDR) measures whether the research community independently investigated the same therapeutic direction — a concordance signal, not a proof of efficacy. AUROC 0.545 is marginally above the 0.50 random baseline; this is a modest but statistically non-trivial signal on an unsupervised task with no disease-specific tuning. These metrics are self-reported against public datasets; third-party replication has not been performed. Precision@10 figures cited elsewhere reflect performance on a small internal benchmark set and should not be generalised. Completed trial status in the concordance ledger means the trial finished enrollment — it does not indicate that the drug was found efficacious. All outputs are research hypotheses requiring independent experimental validation.

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Safety & Scope