Integrity report for Pretrain-to-alignment learning paradigm to improve geophysical AI applicability under scarce field labels and synthetic-to-field gaps: A case study of relative geologic time estimation in global shelf-edge clinothems

A machine-verified record of the checks Pith has run against this paper: detector runs, findings, signed bundle events, and canonical identifiers.

arXiv:2605.16783 · pith:2026:44BBWI6PAN3CXUSR5TSMJ6IWOT

0Critical

0Advisory

5Detectors run

2026-05-24Last checked

Paper page arXiv integrity.json bundle.json

Detector runs

doi_compliance completed v1.0.0 · findings 0 · 2026-05-24 20:32:58.210883+00:00

doi_title_agreement completed v1.0.0 · findings 0 · 2026-05-24 20:02:36.772922+00:00

cited_work_retraction completed v1.0.0 · findings 0 · 2026-05-23 20:52:49.777278+00:00

claim_evidence completed v1.0.0 · findings 0 · 2026-05-21 19:22:33.627322+00:00

ai_meta_artifact skipped v1.0.0 · findings 0 · 2026-05-19 18:33:26.436405+00:00

Findings

No public integrity findings for this paper.

Signed record

The machine-readable record for this paper lives at /pith/44BBWI6P/integrity.json. Pith Number bundles also include signed pith.integrity.v1 events where a Pith Number exists.