Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Statistical Uncertainty Quantification for Aggregate Performance Metrics in Machine Learning Benchmarks
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2501.04234.
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 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-04T23:34:30.024509Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T23:47:27.677181Z
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 665aba2b-438d-435f-a8da-e66539ee39eb · inbound
Rethinking LLM Parametric Knowledge as Post-retrieval Confidence for Dynamic Retrieval and Reranking cites this paper.
Rethinking LLM Parametric Knowledge as Post-retrieval Confidence for Dynamic Retrieval and Reranking Statistical Uncertainty Quantification for Aggregate Performance Metrics in Machine Learning Benchmarks
Reference 24
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T23:34:30.024509Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d087617a-4524-4cd8-a690-bc1ca6097709 · inbound
Unstable Rankings in Bayesian Deep Learning Evaluation cites this paper.
Unstable Rankings in Bayesian Deep Learning Evaluation Statistical Uncertainty Quantification for Aggregate Performance Metrics in Machine Learning Benchmarks
Reference 10
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-11T20:31:15.186122Z
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.
Observation 83b60adc-67a0-46a5-a3fc-5402f697f8ad · inbound
A Tale of Two Variances: When Single-Seed Benchmarks Fail in Bayesian Deep Learning cites this paper.
A Tale of Two Variances: When Single-Seed Benchmarks Fail in Bayesian Deep Learning Statistical Uncertainty Quantification for Aggregate Performance Metrics in Machine Learning Benchmarks
Reference 11
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T20:36:11.788732Z
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.
Observation c3a5bc77-6ef5-4ef8-bb6c-936e0b756488 · inbound
Rank Intervals for Leaderboards: A Hierarchical Framework for Model Evaluation cites this paper.
Rank Intervals for Leaderboards: A Hierarchical Framework for Model Evaluation Statistical Uncertainty Quantification for Aggregate Performance Metrics in Machine Learning Benchmarks
Reference 27
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-02T23:47:27.679532Z
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.
Observation 3514908c-2504-42d2-9ea3-a515cef37245 · inbound
Quantifying Ranking Uncertainty in LLM Benchmarks cites this paper.
Quantifying Ranking Uncertainty in LLM Benchmarks Statistical Uncertainty Quantification for Aggregate Performance Metrics in Machine Learning Benchmarks
Reference 2022
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T09:45:26.760955Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.