Skip to main content

PERSPECTIVES

Standards for AI in science

What good AI science tooling looks like. We define the standards — evidence-first workflows, reproducible pipelines — and let you evaluate which tools meet them.

evidence-first reproducibility scientific-ai bc7: 82 Score breakdown docs 85 tests 80 activity 83 compat 80 total 82

Evidence-First vs Agentic Authorship

What each approach means for scientific research, why agentic authorship fails at the boundaries of truth, and how evidence-locked workflows prevent hallucination.

open-source data-ownership compliance bc7: 82 Score breakdown docs 85 tests 80 activity 83 compat 80 total 82

Open-Source vs Hosted SaaS

Reproducibility, audit rights, data ownership, and the regulatory implications of choosing between open-source and hosted platforms for scientific AI.

reproducibility provenance scientific-ai bc7: 82 Score breakdown docs 85 tests 80 activity 83 compat 80 total 82

Reproducible vs Black Box

What reproducibility actually requires in AI-assisted science: provenance manifests, deterministic scripts, cryptographic hashing, and grounded text validation.

Page 1 of 1