Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Achieving state-of-the-art performance in the Medical Out-of-Distribution (MOOD) challenge using plausible synthetic anomalies
As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2308.01412.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T04:25:18.327265Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T22:27:25.415964Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 6ade20b5-27bc-4630-9568-f41ec00835bc · inbound
PiPViT: Patch-based Visual Interpretable Prototypes for Retinal Image Analysis cites this paper.
PiPViT: Patch-based Visual Interpretable Prototypes for Retinal Image Analysis Achieving state-of-the-art performance in the Medical Out-of-Distribution (MOOD) challenge using plausible synthetic anomalies
Reference 37
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T04:25:18.327265Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a27bdefd-8056-4996-b478-d236c2020897 · inbound
AUCp: Pseudo-AUC for Inference Model Selection with Unlabeled Validation Data in Abnormality Detection cites this paper.
AUCp: Pseudo-AUC for Inference Model Selection with Unlabeled Validation Data in Abnormality Detection Achieving state-of-the-art performance in the Medical Out-of-Distribution (MOOD) challenge using plausible synthetic anomalies
Reference 12
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T22:27:25.417928Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.