research-log-formatter

Formatting SP-STM experiment notes involves validating mandatory frontmatter fields, guiding missing parameters without fabrication, and enforcing a consistent note structure. This process ensures raw experimental records become falsification-friendly and audit-ready.

Yes, generating a new research log from a brief measurement description is supported. The tool creates the structured note while clearly marking unknown experimental values for user follow-up instead of fabricating missing parameters.

Frontmatter validation detects missing mandatory fields and prompts for user confirmation. It enforces strict anti-hallucination rules by marking unknown values clearly rather than inventing raw data or experimental parameters.

Yes, the formatting process preserves existing AI-spec callouts and performs append-only revision history behavior. This maintains experimental provenance and ensures downstream analysis traceability for your research logs.

The best way to prevent data fabrication is enforcing anti-hallucination rules during note formatting. This requires strict MUST-field compliance, unknown-value marking instead of fabrication, and append-only revision history for experimental provenance.

Conclusion cards are inserted into research logs when appropriate during the formatting process. They serve as required auditing modules that ensure day-by-day lab documentation remains suitable for review and downstream traceability.