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Paper Citation Record · LEDGER

Overcoming Common Flaws in the Evaluation of Selective Classification Systems

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2407.01032.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2407.01032 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T20:09:16.897171Z

measured 0 of 1 external citation measurements

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 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.

source=pdf_text observed=2026-08-04T20:09:16.897171Z digest=sha256:2095a611b7c233e0c844984b0c1fe4f39fab9df680a0334a9574778e78aac62f

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-21T18:06:33.927948Z digest=sha256:5b61d087b6ec499faedb5f4ea0957c6dc87f59b79099edec3ba32fda39130663

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-06-30T12:59:57.639608Z digest=sha256:561164f6222ed89f39cb27d4afd603eafe0f61baca111b13b565ae080457e974

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.

source=pdf_text observed=2026-07-15T07:56:01.018667Z digest=sha256:261d535f059a1512f187397170330a7a1c635fa58cda0ebd22aa54683edea686

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.

source=arxiv_source observed=2026-08-02T13:37:01.726624Z digest=sha256:d189751b2886a27ce911cda3af20052271a2cc0e618ececd3557b63b2de12814