GPS methodology in Heartwood

Heartwood is built around the Genealogical Proof Standard and Evidence Explained, not tree fields. Conclusions are outputs of evidence, never inputs.

Personas vs. people

Assertions attach to a persona — "the John Smith in this 1850 census" — not a stable Person node. Which real person a record describes is itself a conclusion requiring proof, so Heartwood models identity as a refutable claim with its own proof argument, backed by the personas it groups. That claim is un-mergeable and splittable, the same as any other conclusion.

Evidence classes

Every assertion carries an evidence classification — original/derivative and primary/secondary/indirect — and a GPS-style certainty: proved / probable / possible / disproved. No confidence percentages; decimals are meaningless to methodologists.

The pending lane

AI-origin content lives in a visibly distinct pending lane. It cannot feed a proof argument, be exported as fact, or be shared until a human confirms it against the original source. Confirmation is itself a recorded provenance event. This is the structural version of "AI can't put anything in your tree without you seeing it" — not a promise that AI can't be wrong, but a promise that nothing ships unseen.

Transcriptions are claims, not facts

A transcription is a refutable claim about what a source image says, kept distinct from the image itself — vision models misread handwriting confidently, and conflating the two would let a misreading masquerade as the source.

Negative evidence and conflicts

Negative evidence is first-class: an expectation ("a man this age should appear in the 1870 enumeration"), the null-result search log entry that evidences the absence, and the inference drawn — never expressed as a flag on a positive assertion. Conflicts between contradicting assertions are surfaced and resolved via proof argument, never auto-merged.

Proof arguments

A proof argument is structured, not prose: a conclusion, the supporting assertions with their evidence classes, explicit correlation reasoning, and treatment of contrary evidence — GPS's "analysis and correlation" as data, not a text field.

Honest research logging

The MCP server sees only what your AI reports. Every assertion- or source-creating tool call requires the search context that produced it, so the log is a byproduct of evidence capture rather than a separate, skippable step. Log entries stay agent-reported until a human marks coverage user-confirmed — an AI-only log can never satisfy a "reasonably exhaustive research" claim in a proof argument without that human checkpoint.