Negative-result replication triage notebook

Spend follow-up effort on informative tests instead of unstructured repeats

Confirm the buyer's problem and scope, collect consented experiment records, predefined hypotheses, instrument logs and uncertainty estimates, then follow this sequence: 1. Separate execution failures from interpretable null outcomes. 2. List testable alternative explanations. 3. Compare bounded follow-up experiments. Resolve uncertain cases with qualified reviewers, approve scientist-reviewed replication decision record and experiment proposal, and measure information gained against predeclared questions per experiment cost, with negative outcomes retained against a documented baseline.

How the AI works

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. Scientists approve methods and statistical interpretation; never invent results or suppress null findings. A model suggestion is never a verified fact, professional decision or authorization to act.

Safeguards

Preserve original data, methods, citations and research limitations. Use researcher review and document every substantive transformation. Scientists approve methods and statistical interpretation; never invent results or suppress null findings. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.

What to build first

Pilot scope: Scientists approve methods and statistical interpretation; never invent results or suppress null findings. Implement one approved input format, a bounded representative case set and the first two task modules: separate execution failures from interpretable null outcomes; list testable alternative explanations. Support the third module with operator review: compare bounded follow-up experiments. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.

What it can connect to

Authorized datasets, papers, protocols, code and research records. Permitted research libraries, interview recording imports, citation exports and document editors. Preserve original source metadata throughout the workflow. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.

The screens in detail

Primary screens: Research question and consent, Evidence comparison, Reviewed findings and experiment. Organize work by research question. Show a source library, an evidence matrix and a draft findings panel with linked quotations. Keep contradictory findings and unanswered questions visible. Allow reviewers to inspect the original context before accepting an interpretation. Make the task-specific outcome scientist-reviewed replication decision record and experiment proposal visible beside its evidence, review state and value baseline.