Paper Citation Record · LEDGER

Statistical Uncertainty Quantification for Aggregate Performance Metrics in Machine Learning Benchmarks

As of 11 August 2026, this Paper Citation Record lists 30 of 30 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.

pith.paper-citation-record.v1
2501.04234 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T21:43:06.616805Z

measured 35 of 35 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 2026-08-04T23:34:30.024509Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-02T23:47:27.677181Z

Reference resolution

30 of 30 outbound references displayed

verified exact1
verified fuzzy21
unresolved8
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3ee6cec1-6a14-4895-9c83-9fe2550131a8 · outbound

This paper cites GPT-4 Technical Report.

Statistical Uncertainty Quantification for Aggregate Performance Metrics in Machine Learning Benchmarks GPT-4 Technical Report

Reference 1

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Observation 6ce775bc-e399-466f-87b3-61d0fddea82c · outbound

This paper cites Bayesian inferences on uncertain ranks and orderings: Application to ranking players and lineups.

Statistical Uncertainty Quantification for Aggregate Performance Metrics in Machine Learning Benchmarks Bayesian inferences on uncertain ranks and orderings: Application to ranking players and lineups

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:43:07.007179Z

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Observation 4212ce72-189f-44e7-b5af-716038db8c01 · outbound

This paper cites Time for a change: A tutorial for comparing multiple classifiers through Bayesian analysis.

Statistical Uncertainty Quantification for Aggregate Performance Metrics in Machine Learning Benchmarks Time for a change: A tutorial for comparing multiple classifiers through Bayesian analysis

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:43:06.994702Z

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Observation c040c238-9c07-47ee-85a6-7f3826dee3b5 · outbound

This paper cites Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S.

Statistical Uncertainty Quantification for Aggregate Performance Metrics in Machine Learning Benchmarks Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:43:06.982741Z

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Observation b24df635-f720-4d26-88fc-70487398d3e6 · outbound

This paper cites Accounting for variance in machine learning benchmarks.

Statistical Uncertainty Quantification for Aggregate Performance Metrics in Machine Learning Benchmarks Accounting for variance in machine learning benchmarks

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:43:06.970718Z

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Observation 5bce9be4-2f6d-4392-92b4-75330a7ec61e · outbound

This paper cites What are the best systems? N ew perspectives on NLP benchmarking.

Statistical Uncertainty Quantification for Aggregate Performance Metrics in Machine Learning Benchmarks What are the best systems? N ew perspectives on NLP benchmarking

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:43:06.957749Z

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Observation 3c0f5b98-4385-4e21-9fbf-bbdac12fa03b · outbound

This paper cites The Benchmark Lottery.

Statistical Uncertainty Quantification for Aggregate Performance Metrics in Machine Learning Benchmarks The Benchmark Lottery

Reference 7

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Observation 86d6398b-416a-41b6-96a4-69e0c5ef7c5f · outbound

This paper cites Statistical comparisons of classifiers over multiple data sets.

Statistical Uncertainty Quantification for Aggregate Performance Metrics in Machine Learning Benchmarks Statistical comparisons of classifiers over multiple data sets

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:43:06.945859Z

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Observation 2ae27b53-2cea-4c6a-a380-6436a2d15947 · outbound

This paper cites Bayesian aggregation of order-based rank data.

Statistical Uncertainty Quantification for Aggregate Performance Metrics in Machine Learning Benchmarks Bayesian aggregation of order-based rank data

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:43:06.933812Z

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Observation afae105b-2648-43b3-b27e-d32223867a54 · outbound

This paper cites Approximate statistical tests for comparing supervised classification learning algorithms.

Statistical Uncertainty Quantification for Aggregate Performance Metrics in Machine Learning Benchmarks Approximate statistical tests for comparing supervised classification learning algorithms

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:43:06.920637Z

Source-reported events for the cited work

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Observation fa0b86bb-d71f-46a2-9cf1-52fcf4c1d1a3 · outbound

This paper cites Statistical significance testing for natural language processing.

Statistical Uncertainty Quantification for Aggregate Performance Metrics in Machine Learning Benchmarks Statistical significance testing for natural language processing

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:43:06.908376Z

Source-reported events for the cited work

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Observation a03c59fb-ff64-451b-b43c-6be47a494543 · outbound

This paper cites An Introduction to the Bootstrap.

Statistical Uncertainty Quantification for Aggregate Performance Metrics in Machine Learning Benchmarks An Introduction to the Bootstrap

Reference 12

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Observation 6cf60cdd-e389-4cdb-9b7b-6d4f3e37775b · outbound

This paper cites The graphical presentation of a collection of means.

Statistical Uncertainty Quantification for Aggregate Performance Metrics in Machine Learning Benchmarks The graphical presentation of a collection of means

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:43:06.889354Z

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 eca38a4f-76de-4952-b108-f88de834458d · outbound

This paper cites League tables and their limitations: S tatistical issues in comparisons of institutional performance.

Statistical Uncertainty Quantification for Aggregate Performance Metrics in Machine Learning Benchmarks League tables and their limitations: S tatistical issues in comparisons of institutional performance

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:43:06.878323Z

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Observation d7da6371-c1dc-4d90-9473-3a247f0731aa · outbound

This paper cites Randomized significance tests in machine translation.

Statistical Uncertainty Quantification for Aggregate Performance Metrics in Machine Learning Benchmarks Randomized significance tests in machine translation

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:43:06.866528Z

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Observation 651d2b0e-5c5b-4fd7-8a29-f72b5187211c · outbound

This paper cites Modeling the variability of rankings.

Statistical Uncertainty Quantification for Aggregate Performance Metrics in Machine Learning Benchmarks Modeling the variability of rankings

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:43:06.853267Z

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 2e5a9dd2-22fb-4e09-b1a6-6779713d3a43 · outbound

This paper cites Statistical comparisons of classifiers by generalized stochastic dominance.

Statistical Uncertainty Quantification for Aggregate Performance Metrics in Machine Learning Benchmarks Statistical comparisons of classifiers by generalized stochastic dominance

Reference 17

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Observation 123283bf-cb14-447c-a32c-e9a9b52625be · outbound

This paper cites Active Bayesian assessment of black-box classifiers.

Statistical Uncertainty Quantification for Aggregate Performance Metrics in Machine Learning Benchmarks Active Bayesian assessment of black-box classifiers

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:43:06.827661Z

Source-reported events for the cited work

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Observation 0c55c288-6523-4424-abc1-446d5bc5ca62 · outbound

This paper cites Theory of Point Estimation.

Statistical Uncertainty Quantification for Aggregate Performance Metrics in Machine Learning Benchmarks Theory of Point Estimation

Reference 19

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Observation acdeee00-44d8-4ef0-b3d4-c74dbc1a7e7b · outbound

This paper cites Bayesian analysis of rank data with covariates and heterogeneous rankers.

Statistical Uncertainty Quantification for Aggregate Performance Metrics in Machine Learning Benchmarks Bayesian analysis of rank data with covariates and heterogeneous rankers

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:43:06.802155Z

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Observation 000e0b1b-144c-424f-8db9-63accd29a40c · outbound

This paper cites Slice sampling.

Statistical Uncertainty Quantification for Aggregate Performance Metrics in Machine Learning Benchmarks Slice sampling

Reference 21

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Observation 8dca4664-9298-47f1-83da-5451cd1b1a9e · outbound

This paper cites Uncertainty in Ranking.

Statistical Uncertainty Quantification for Aggregate Performance Metrics in Machine Learning Benchmarks Uncertainty in Ranking

Reference 22

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Observation 730be737-3b13-4fff-97fa-a7ed45edcdc4 · outbound

This paper cites On comparing classifiers: Pitfalls to avoid and a recommended approach.

Statistical Uncertainty Quantification for Aggregate Performance Metrics in Machine Learning Benchmarks On comparing classifiers: Pitfalls to avoid and a recommended approach

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:43:06.782703Z

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Observation 2790ecde-d15d-4e1c-b6a3-1a9f61e32af1 · outbound

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Statistical Uncertainty Quantification for Aggregate Performance Metrics in Machine Learning Benchmarks Unresolved cited work

Reference 24

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Observation a8292bee-30c6-4278-a8ea-7e7cb8bbfc9e · outbound

This paper cites Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models.

Statistical Uncertainty Quantification for Aggregate Performance Metrics in Machine Learning Benchmarks Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Reference 25

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Statistical Uncertainty Quantification for Aggregate Performance Metrics in Machine Learning Benchmarks Classifier uncertainty: E vidence, potential impact, and probabilistic treatment

Reference 26

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Observation 56df0d95-102f-4570-a73d-4924f262a84b · outbound

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Statistical Uncertainty Quantification for Aggregate Performance Metrics in Machine Learning Benchmarks LLaMA: Open and Efficient Foundation Language Models

Reference 27

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This paper cites Follow the leader (board) with confidence: E stimating p-values from a single test set with item and response variance.

Statistical Uncertainty Quantification for Aggregate Performance Metrics in Machine Learning Benchmarks Follow the leader (board) with confidence: E stimating p-values from a single test set with item and response variance

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:43:06.757704Z

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Observation 86801887-c27a-478a-b0c3-9e707c3d3173 · outbound

This paper cites Confidence intervals for population ranks in the presence of ties and near ties.

Statistical Uncertainty Quantification for Aggregate Performance Metrics in Machine Learning Benchmarks Confidence intervals for population ranks in the presence of ties and near ties

Reference 29

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Observation de1b81ab-b83a-4acf-955c-cdf7dd101341 · outbound

This paper cites A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark.

Statistical Uncertainty Quantification for Aggregate Performance Metrics in Machine Learning Benchmarks A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark

Reference 30

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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

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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

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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

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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

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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

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