Evidence and Provenance Workflow

Core separation

Drafting and evidence verification are different activities.

• Drafting organizes language from already recorded evidence IDs. A draft may remain incomplete and uncertain.
• Verification requires an accountable human to open the source, confirm the exact support and locator, check bibliographic metadata, and record who verified it and when.
• A fluent sentence is not evidence. Search snippets, generated summaries, memory, and another paper's reference list are discovery aids, not verified support.

Registries

The scaffold creates five linked records:

1. source_manifest.json assigns each source an E ID and records identifiers, location, confidentiality class, and verification state.
2. claims.csv assigns each claim a C ID, stores a hash rather than raw claim text, and maps the claim to one or more verified E IDs.
3. consistency_manifest.json binds numeric facts, methods, outcomes, analysis intent, units, denominators, sample sizes, and result locations.
4. authorship.json records human authorship criteria, CRediT roles, accountability, declarations, and AI use.
5. reporting_coverage.json records high-level coverage without scoring or certifying the manuscript.

These registries contain metadata, not full source documents. Do not paste unpublished manuscripts, peer-review files, PHI, sensitive datasets, proprietary content, or source documents into them.

Claim markers

Append machine-readable markers to every factual or numeric assertion while drafting:

[claim:C001] [evidence:E001,E002]

An alternative citation marker is [@E001]. Keep the claim and evidence markers on the same line until the audit passes. A final publisher conversion may replace evidence IDs with rendered citations only after preserving an auditable mapping.

Verification procedure

For each source:

1. Open the authoritative source or record.
2. Confirm title, author or organization, publication state, year, and identifiers.
3. Record the exact supporting location, such as section, page, table, figure, or registry field.
4. Confirm that the source supports the claim's direction, population, intervention or exposure, outcome, time point, and uncertainty.
5. Record caveats, retractions, corrections, expressions of concern, or version status.
6. Mark the source verified only after a named human completes the check.

For each claim:

1. Classify it as factual, numeric, method, result, interpretive, or declaration.
2. Hash the normalized claim text and store the hash in claims.csv.
3. Map it to verified evidence IDs.
4. Record uncertainty and whether the analysis was confirmatory, exploratory, descriptive, or not applicable.
5. Keep unsupported or conflicting claims out of submission-ready prose. If useful, retain them in a clearly marked unresolved-issues log.

Drafting gates

Do not infer or complete missing:

• citations, identifiers, quotations, or source locators;
• data values, denominators, units, sample sizes, or statistical results;
• methods, protocol details, analysis decisions, or deviations;
• approvals, consent, registrations, author contributions, conflicts, funding, or availability statements.

Use explicit missing states. Preserve negative, null, adverse, unexpected, and inconclusive findings. Never convert absence of evidence into evidence of no effect.

Final audit

Run:

python3 scripts/validate_manifest.py source_manifest.json --kind source --require-verified
python3 scripts/audit_claims.py manuscript.md claims.csv source_manifest.json
python3 scripts/check_consistency.py consistency_manifest.json
python3 scripts/check_references.py source_manifest.json

Tool output contains IDs and line numbers, not manuscript or source text. A passing machine audit supports review; it does not replace human scientific judgment.