Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Estimating and Improving Fairness with Adversarial Learning
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2103.04243.
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 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T04:09:07.591374Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-05T13:19:14.668357Z
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 77f051e7-7ed2-4846-be9e-5728844d18e3 · inbound
Evaluating Fairness and Mitigating Bias in Machine Learning: A Novel Technique using Tensor Data and Bayesian Regression cites this paper.
Evaluating Fairness and Mitigating Bias in Machine Learning: A Novel Technique using Tensor Data and Bayesian Regression Estimating and Improving Fairness with Adversarial Learning
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T04:09:07.591374Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3cabcaa5-aea5-4040-acda-eff546bca31b · inbound
Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning cites this paper.
Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning Estimating and Improving Fairness with Adversarial Learning
Reference 37
- Resolution
- verified exact
- local_arxiv, observed 2026-08-05T13:19:14.740320Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.