This applied research work examines how an evidence-based assistant could help reviewers investigate animal datasets. Reproducible pilots use pinned source files, explicit cohort definitions, graph and statistical calculations, and evidence cards. The work evaluates the analysis process; it does not claim a new biological discovery or a validated predictive model.
The contribution at a glance
- Our role
- Research engineering, source audit, reproducible analysis, and AI evaluation design.
- Work delivered
- Scoped research pilots, evidence cards, and requirements for a bounded research-screening workflow.
01
Defined source data
Establish versions, field meanings, and cohort definitions.
02
Reproducible checks
Test calculations, missing data, and alternative explanations.
03
Human-reviewed evidence
Preserve uncertainty and the checks that could overturn a result.
Scope & current status
Reproducible research pilots. Biological findings remain unresolved, with broader scientific validation still required.
The challenge
A plausible signal may be created by overlapping field definitions, missing outcomes, uncertain pedigree identity, or the animals selected for testing. A language model can describe a pattern convincingly while missing the reason it is misleading.
The problem is to help a reviewer form better questions and challenge a result while preserving the exact source, calculation, and limits behind it.
The approach
Source audits and deterministic calculations come first. Pedigree sensitivity checks investigate how a relationship conclusion changes when an uncertain source identity is masked. Phenotype checks inspect field overlap and sampling limitations. Longitudinal audits distinguish measurement coverage from biological change.
Evidence cards record the cohort, observed counts, source lineage, competing explanations, and checks that could overturn a result. A first scoped API workflow supports consent-checked descriptive screening and optional bounded AI critique. The API workbench and offline studies have different scopes and are not presented as one validated discovery product.
The work in detail
- 01
Pin and interpret the sources
Record source versions and field meanings before calculating associations or trends.
- 02
Reproduce the calculation
Use deterministic graph and statistical tools and compare results with independent references.
- 03
Challenge the apparent signal
Check field overlap, missingness, measurement coverage, family structure, and alternative explanations.
- 04
Keep review authority explicit
Retain unresolved status where evidence is incomplete. AI critique cannot change computed values or publish a finding.
What the work established
The pilots produced reproducible evidence cards and concrete examples of why an attractive research signal may need to be rejected or left unresolved. They also informed a bounded research-screening workflow. Broader scientific validation and prospective evaluation remain separate next steps.
Why it matters
A useful research assistant should help a reviewer reject a weak explanation as well as find a promising one. That requires explicit source semantics, reproducible computation, and a review process that can preserve uncertainty.