Paper Citation Record · LEDGER

Towards Modeling Data Quality and Machine Learning Model Performance

As of 23 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 1 inbound Pith citation observation for arXiv:2412.05882.

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

pith.paper-citation-record.v1
2412.05882 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T20:18:53.155293Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-30T22:27:30.692356Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T14:05:45.805197Z

Reference resolution

34 of 34 outbound references displayed

verified exact3
verified fuzzy24
unresolved7
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e6ffb729-f7dd-46c0-a333-7beaa57dd5d5 · outbound

This paper cites Localization of unidentified events with raw microblogging data.

Towards Modeling Data Quality and Machine Learning Model Performance Localization of unidentified events with raw microblogging data

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:18:53.806366Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 235eacc1-41b4-4b5a-85cc-f69e010ddd1b · outbound

This paper cites TBAM: Towards An Agent-Based Model to Enrich Twitter Data.

Towards Modeling Data Quality and Machine Learning Model Performance TBAM: Towards An Agent-Based Model to Enrich Twitter Data

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-08-11T20:18:53.278892Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 373cb3fb-6574-4e84-be20-6cdd8b0d379d · outbound

This paper cites Data quality and explainable ai.

Towards Modeling Data Quality and Machine Learning Model Performance Data quality and explainable ai

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:18:53.791021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ce84d6cb-ea27-43a5-8215-97485305bfd6 · outbound

This paper cites Principles for Evaluation of AI/ML Model Performance and Robustness.

Towards Modeling Data Quality and Machine Learning Model Performance Principles for Evaluation of AI/ML Model Performance and Robustness

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-08-11T20:18:53.253272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 61369873-108e-4946-9f18-da3aeeba3d91 · outbound

This paper cites Performance evaluation in machine learning: the good, the bad, the ugly, and the way forward.

Towards Modeling Data Quality and Machine Learning Model Performance Performance evaluation in machine learning: the good, the bad, the ugly, and the way forward

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:18:53.772353Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ca0530ad-ea7b-47bb-9f5e-31713f05e254 · outbound

This paper cites fontclos/hitandrun: Initial release, Jun 2021.

Towards Modeling Data Quality and Machine Learning Model Performance fontclos/hitandrun: Initial release, Jun 2021

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:18:53.755222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e66cd7a2-17f7-4c4b-8d26-6e6cdf27572b · outbound

This paper cites A survey of uncertainty in deep neural networks.

Towards Modeling Data Quality and Machine Learning Model Performance A survey of uncertainty in deep neural networks

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-11T20:18:53.004213Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 306144f5-1cc9-4ed5-a381-da4566a75405 · outbound

This paper cites Uncertainty in big data analytics: survey, opportunities, and challenges.

Towards Modeling Data Quality and Machine Learning Model Performance Uncertainty in big data analytics: survey, opportunities, and challenges

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:18:53.727478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2ce1058e-9fb6-4096-aeed-8236a848114f · outbound

This paper cites Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods.

Towards Modeling Data Quality and Machine Learning Model Performance Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-11T20:18:53.013612Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 280f1c9f-6ad0-4ac3-a575-205ad5bbcac9 · outbound

This paper cites To trust or not to trust a classifier.

Towards Modeling Data Quality and Machine Learning Model Performance To trust or not to trust a classifier

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-11T20:18:53.018629Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7bb752ce-9187-48ad-9dd1-ec11b9d07362 · outbound

This paper cites Trust in artificial intelligence: Meta-analytic findings.

Towards Modeling Data Quality and Machine Learning Model Performance Trust in artificial intelligence: Meta-analytic findings

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:18:53.685457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation acca04a9-bcd4-454a-a0dd-cf5c74b05ca3 · outbound

This paper cites Data-centric ai solutions and emerging technologies in the healthcare ecosystem.

Towards Modeling Data Quality and Machine Learning Model Performance Data-centric ai solutions and emerging technologies in the healthcare ecosystem

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:18:53.661422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 4bcde68d-a346-482f-9f47-5bcb2cf20244 · outbound

This paper cites Big data: Structured and unstructured.

Towards Modeling Data Quality and Machine Learning Model Performance Big data: Structured and unstructured

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:18:53.644404Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation af46a8ec-a017-4753-aa35-7e18fbb39544 · outbound

This paper cites A survey on bias and fairness in machine learning.

Towards Modeling Data Quality and Machine Learning Model Performance A survey on bias and fairness in machine learning

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:18:53.625570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9237b868-7656-45d7-a1a5-6bfaa21ec1b8 · outbound

This paper cites Bias and unfairness in machine learning models: A systematic review on datasets, tools, fairness metrics, and identification and mitigation methods.

Towards Modeling Data Quality and Machine Learning Model Performance Bias and unfairness in machine learning models: A systematic review on datasets, tools, fairness metrics, and identification and mitigation methods

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:18:53.607564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 6170d73e-38dd-4955-9666-7445786a37ca · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library.

Towards Modeling Data Quality and Machine Learning Model Performance Pytorch: An imperative style, high-performance deep learning library

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:18:53.589250Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation dcc56ab7-a059-46b6-9602-3987c5e2e09f · outbound

This paper cites Pedregosa, G.

Towards Modeling Data Quality and Machine Learning Model Performance Pedregosa, G

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T20:18:53.052569Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 265d96d4-15cf-4e83-81b0-018c7d99feb7 · outbound

This paper cites Learning from noisy data.

Towards Modeling Data Quality and Machine Learning Model Performance Learning from noisy data

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:18:53.560415Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8e65b77b-9442-458c-96fd-ac41fb5aba55 · outbound

This paper cites Ross Quinlan.

Towards Modeling Data Quality and Machine Learning Model Performance Ross Quinlan

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:18:53.544989Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation da550a9f-c756-4452-83a8-b3936a88206d · outbound

This paper cites What is the value of data? on mathematical methods for data quality estimation.

Towards Modeling Data Quality and Machine Learning Model Performance What is the value of data? on mathematical methods for data quality estimation

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:18:53.527026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b8e159f9-22bb-49c9-81f4-5932b4741c9e · outbound

This paper cites Understanding machine learning: From theory to algorithms.

Towards Modeling Data Quality and Machine Learning Model Performance Understanding machine learning: From theory to algorithms

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-11T20:18:53.072949Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b62b4372-7ec1-4484-8444-fb7d1d712f4b · outbound

This paper cites A mathematical theory of communication.

Towards Modeling Data Quality and Machine Learning Model Performance A mathematical theory of communication

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-11T20:18:53.077607Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e5fff21-cb8e-4fd0-a9bf-31bc34e1f951 · outbound

This paper cites Efficient monte carlo procedures for generating points uniformly distributed over bounded regions.

Towards Modeling Data Quality and Machine Learning Model Performance Efficient monte carlo procedures for generating points uniformly distributed over bounded regions

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:18:53.476738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f2c31d27-6da9-4f89-b63e-4404520ddca3 · outbound

This paper cites Evaluation of uncertainty quantification in deep learning.

Towards Modeling Data Quality and Machine Learning Model Performance Evaluation of uncertainty quantification in deep learning

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:18:53.452487Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2eaf7480-0b58-4c91-b866-04d387e3e64e · outbound

This paper cites Trust and artificial intelligence.

Towards Modeling Data Quality and Machine Learning Model Performance Trust and artificial intelligence

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:18:53.434069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 36393646-248c-4b71-91a3-60e9854a6b0e · outbound

This paper cites Diameter-based active learning.

Towards Modeling Data Quality and Machine Learning Model Performance Diameter-based active learning

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:18:53.414610Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 02250ce1-b4f8-40d3-aee7-676266a6e5f2 · outbound

This paper cites Rethinking statistical learning theory: learning using statistical invariants.

Towards Modeling Data Quality and Machine Learning Model Performance Rethinking statistical learning theory: learning using statistical invariants

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:18:53.393975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 43bc7ac2-6916-437c-bd5c-c17fd4ebeaea · outbound

This paper cites A framework for analysis of data quality research.

Towards Modeling Data Quality and Machine Learning Model Performance A framework for analysis of data quality research

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:18:53.373796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9e71448b-b3ef-4fa1-ad09-170d011fa6f9 · outbound

This paper cites Data collection and quality challenges in deep learning: A data-centric ai perspective.

Towards Modeling Data Quality and Machine Learning Model Performance Data collection and quality challenges in deep learning: A data-centric ai perspective

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:18:53.356273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 798f65e0-304e-493f-9313-6408c78ecf7e · outbound

This paper cites How Much Can We Really Trust You? Towards Simple, Interpretable Trust Quantification Metrics for Deep Neural Networks.

Towards Modeling Data Quality and Machine Learning Model Performance How Much Can We Really Trust You? Towards Simple, Interpretable Trust Quantification Metrics for Deep Neural Networks

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-08-11T20:18:53.226602Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 0b1a64ee-3d46-4245-975c-d882d21bca8f · outbound

This paper cites Current time series anomaly detection benchmarks are flawed and are creating the illusion of progress.

Towards Modeling Data Quality and Machine Learning Model Performance Current time series anomaly detection benchmarks are flawed and are creating the illusion of progress

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:18:53.336732Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 0d5debfa-05c6-4e24-aabe-2b984d564ca4 · outbound

This paper cites Data-centric ai: Perspectives and challenges.

Towards Modeling Data Quality and Machine Learning Model Performance Data-centric ai: Perspectives and challenges

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:18:53.316713Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d87a1566-8705-4207-9e81-9c3b7117fd83 · outbound

This paper cites Data-centric Artificial Intelligence: A Survey.

Towards Modeling Data Quality and Machine Learning Model Performance Data-centric Artificial Intelligence: A Survey

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-11T20:18:53.136901Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f7ead42b-1d20-4323-836f-b49f3353a25d · outbound

This paper cites Class noise vs.

Towards Modeling Data Quality and Machine Learning Model Performance Class noise vs

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:18:53.297409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Pith citing papers

Observation f743abf2-1f98-4e03-96f5-394f60ffa80c · inbound

From Accuracy to Auditability: A Survey of Determinism in Financial AI Systems cites this paper.

From Accuracy to Auditability: A Survey of Determinism in Financial AI Systems Towards Modeling Data Quality and Machine Learning Model Performance

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-07-01T14:05:45.806797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.