Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Overcoming Common Flaws in the Evaluation of Selective Classification Systems
As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2407.01032.
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 2026-08-14T06:32:32.682623+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-11T14:06:03.652547Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-06-30T13:04:40.110587Z
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 95d05641-8e19-4442-8cd0-fc7d7758316c · inbound
ReXTrust: A Model for Fine-Grained Hallucination Detection in AI-Generated Radiology Reports cites this paper.
ReXTrust: A Model for Fine-Grained Hallucination Detection in AI-Generated Radiology Reports Overcoming Common Flaws in the Evaluation of Selective Classification Systems
Reference 35
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T14:06:03.652547Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7f047c89-d920-4dfb-8929-36d56d9e9195 · inbound
Scaling Truth: The Confidence Paradox in AI Fact-Checking cites this paper.
Scaling Truth: The Confidence Paradox in AI Fact-Checking Overcoming Common Flaws in the Evaluation of Selective Classification Systems
Reference 45
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T20:09:16.897171Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9394bb44-52fb-4cf3-8efa-93a6c5e3c135 · inbound
A Systematic Analysis of Out-of-Distribution Detection Under Representation and Training Paradigm Shifts cites this paper.
A Systematic Analysis of Out-of-Distribution Detection Under Representation and Training Paradigm Shifts Overcoming Common Flaws in the Evaluation of Selective Classification Systems
Reference 13
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T18:10:27.778405Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 48b0ded2-737b-4b99-8df8-ec61991f7fe0 · inbound
Proper Scoring Rules for Agentic Uncertainty Quantification cites this paper.
Proper Scoring Rules for Agentic Uncertainty Quantification Overcoming Common Flaws in the Evaluation of Selective Classification Systems
Reference 23
- Resolution
- verified exact
- arxiv_id, observed 2026-06-30T13:04:40.112120Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation cfb01fa2-36bf-459a-bca1-b8f57a0c9b0b · inbound
Calibrated Selective Prediction Using Deep Ensembles for ROI-Based Thyroid Nodule Ultrasound Classification Under Dataset Shift: A Retrospective Evaluation cites this paper.
Calibrated Selective Prediction Using Deep Ensembles for ROI-Based Thyroid Nodule Ultrasound Classification Under Dataset Shift: A Retrospective Evaluation Overcoming Common Flaws in the Evaluation of Selective Classification Systems
Reference 21
- Resolution
- unresolved
- no resolver link, observed 2026-07-15T07:56:01.018667Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0f353563-41f3-443c-a68e-92f4782753b2 · inbound
SCOPE and SCION: A Benchmark and an Auditable Reference Pipeline for Schema Induction and Fusion from Text cites this paper.
SCOPE and SCION: A Benchmark and an Auditable Reference Pipeline for Schema Induction and Fusion from Text Overcoming Common Flaws in the Evaluation of Selective Classification Systems
Reference 146
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T13:37:01.726624Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.