validate

Schema compliance for SWARM claims is enforced by checking adherence to defined schemas and documenting critical experimental parameters. Boundary checks prevent the introduction of flawed or incomplete data into the knowledge vault.

To check if a claim meets confidence criteria before adding it to a knowledge vault, run validation to automatically flag inconsistencies or missing information. It enforces requirements for effect sizes, correction methods, and sample sizes for medium to high confidence claims.

Statistical soundness requirements for claims include effect sizes, correction methods, and sample sizes. Validation enforces these for medium to high confidence claims to ensure the reliability and accuracy of research findings.

Yes, you can automatically flag incomplete provenance data in SWARM claims. Validation checks that all supporting evidence links to valid and complete run data, flagging any inconsistencies or missing information before claims are added to the knowledge vault.

Automated claim validation for schema adherence is limited to checking predefined schemas, boundary conditions, and documented experimental parameters. It flags missing information but cannot assess the qualitative accuracy of the underlying research findings.