Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Selective Prediction via Training Dynamics
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2205.13532.
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 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T13:20:25.232239Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-25T07:40:28.615097Z
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 e16bae3c-8d28-4139-8820-698a4d4ff201 · inbound
Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings cites this paper.
Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings Selective Prediction via Training Dynamics
Reference 70
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:20:25.232239Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 695c2e1b-6aa3-4f99-8b4e-dc54e2a37865 · inbound
Are Targeted Data Poisoning Attacks as Effective as We Think? cites this paper.
Are Targeted Data Poisoning Attacks as Effective as We Think? Selective Prediction via Training Dynamics
Reference 16
- Resolution
- verified exact
- arxiv_id, observed 2026-05-25T07:40:28.618199Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation c4a77153-e72b-4ad7-ba94-648280b05e0d · inbound
LiLAW: Lightweight Learnable Adaptive Weighting to Learn Sample Difficulty & Improve Noisy Training cites this paper.
LiLAW: Lightweight Learnable Adaptive Weighting to Learn Sample Difficulty & Improve Noisy Training Selective Prediction via Training Dynamics
Reference 21
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T15:02:41.180582Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.