Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
An Empirical Study of Automated Mislabel Detection in Real World Vision Datasets
As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2312.02200.
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 2026-08-15T06:32:42.880941+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-11T23:41:33.625326Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-11T23:22:03.719078Z
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 b9b50a8e-17b8-4657-86a8-a9d006391dfa · inbound
Noisy Ostracods: A Fine-Grained, Imbalanced Real-World Dataset for Benchmarking Robust Machine Learning and Label Correction Methods cites this paper.
Noisy Ostracods: A Fine-Grained, Imbalanced Real-World Dataset for Benchmarking Robust Machine Learning and Label Correction Methods An Empirical Study of Automated Mislabel Detection in Real World Vision Datasets
Reference 57
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T23:41:33.625326Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 77a5982a-0991-46bb-92cc-9ebb82bb52ff · inbound
Class-wise Autoencoders Measure Classification Difficulty And Detect Label Mistakes cites this paper.
Class-wise Autoencoders Measure Classification Difficulty And Detect Label Mistakes An Empirical Study of Automated Mislabel Detection in Real World Vision Datasets
Reference 41
- Resolution
- verified exact
- local_arxiv, observed 2026-08-11T23:22:03.728745Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.