Scientific AI evaluation
Scientific review of environmental AI
Make AI-assisted environmental work reviewable through scientific claim checks, evidence tracing and clear evaluation criteria.
| Review field | What to record |
|---|---|
| Claim | The exact consequential statement. |
| Evidence | The supporting passage and applicable conditions. |
| Judgement | Supported, uncertain or requires correction — with reason. |
| Next action | Correction, further evidence or expert decision. |
Example deliverable structure. No client data or project results.
The decision
Questions to resolve
- Are the chemistry, biology, units and system boundaries coherent?
- Do cited sources support the actual claims and their stated conditions?
- Does the output distinguish evidence, inference and uncertainty?
A useful starting point
Information to bring together
- Intended task, audience and consequences of incorrect output.
- Approved source material, sample outputs and available model/version context.
- Existing review criteria, error examples and information-handling requirements.
What BGF can provide
Outputs for the next step
- Claim–evidence review with corrections and scientific rationale.
- Domain evaluation questions, scoring rubric and error taxonomy.
- Review workflow with escalation criteria and documented limitations.
Scientific domain evaluation is scoped separately from software development, cybersecurity testing and formal certification. Confidential inputs are handled only within the agreed project arrangements.
Take the next step
What needs to be clearer in your project?
Start with a short, non-confidential description of the problem and the decision ahead.