Integrity report for Rethinking Transfer Learning for Industrial Inspection: DINOv3 vs. ImageNet Pretraining Across RGB and X-ray Tasks

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

arXiv:2605.23472 · pith:2026:E6MTHLUAL2665LVSWXR4VCHPWH

0Critical

0Advisory

6Detectors run

2026-05-28Last checked

Paper page arXiv integrity.json bundle.json

Detector runs

claim_evidence completed v1.0.0 · findings 0 · 2026-05-28 13:24:50.539383+00:00

doi_title_agreement completed v1.0.0 · findings 0 · 2026-05-28 05:03:27.342419+00:00

doi_compliance completed v1.0.0 · findings 0 · 2026-05-28 04:44:16.136455+00:00

citation_quote_validity completed v0.1.0 · findings 0 · 2026-05-25 13:51:36.646305+00:00

cited_work_retraction completed v1.0.0 · findings 0 · 2026-05-25 09:23:57.778400+00:00

ai_meta_artifact skipped v1.0.0 · findings 0 · 2026-05-25 02:35:19.342375+00:00

Findings

No public integrity findings for this paper.

Signed record

The machine-readable record for this paper lives at /pith/E6MTHLUA/integrity.json. Pith Number bundles also include signed pith.integrity.v1 events where a Pith Number exists.