Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Learning Optimal Fair Classification Trees: Trade-offs Between Interpretability, Fairness, and Accuracy
As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2201.09932.
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 2026-08-19T06:32:44.657259+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-16T11:00:08.806517Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-24T05:03:55.575382Z
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 1ba42c18-444c-40ac-8d36-78cd826d366b · inbound
Privacy Constrained Fairness Estimation for Decision Trees cites this paper.
Privacy Constrained Fairness Estimation for Decision Trees Learning Optimal Fair Classification Trees: Trade-offs Between Interpretability, Fairness, and Accuracy
Reference 28
- Resolution
- verified exact
- arxiv_id, observed 2026-05-24T05:03:55.577800Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 1eba08a0-ca41-4e8d-9276-f643b5dbf7c8 · inbound
Engineering the Law-Machine Learning Translation Problem: Developing Legally Aligned Models cites this paper.
Engineering the Law-Machine Learning Translation Problem: Developing Legally Aligned Models Learning Optimal Fair Classification Trees: Trade-offs Between Interpretability, Fairness, and Accuracy
Reference 69
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:00:08.806517Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.