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Integrity report for Probabilistic storyline attribution using machine learning

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

arXiv:2606.02550 · pith:2026:FU4DZUGVJOXSYEGZG5HUU6LRL4

0Critical
0Advisory
6Detectors run
2026-06-05Last checked

Paper page arXiv integrity.json bundle.json

Detector runs

claim_evidence completed v1.0.0 · findings 0 · 2026-06-05 07:49:18.356166+00:00
external_links completed v1.0.0 · findings 0 · 2026-06-02 17:31:58.837227+00:00
shingle_duplication skipped v0.1.0 · findings 0 · 2026-06-02 05:50:17.317041+00:00
cited_work_retraction completed v1.0.0 · findings 0 · 2026-06-02 03:56:01.628573+00:00
citation_quote_validity skipped v0.1.0 · findings 0 · 2026-06-02 03:50:50.790661+00:00
ai_meta_artifact skipped v1.0.0 · findings 0 · 2026-06-02 03:35:15.899422+00:00

Findings

No public integrity findings for this paper.

Signed record

The machine-readable record for this paper lives at /pith/FU4DZUGV/integrity.json. Pith Number bundles also include signed pith.integrity.v1 events where a Pith Number exists.