Paper Citation Record · LEDGER

Estimating prevalence with precision and accuracy

As of 8 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 1 inbound Pith citation observation for arXiv:2507.06061.

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

pith.paper-citation-record.v1
2507.06061 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:18:39.364955Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-11T10:38:19.251502Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

30 of 30 outbound references displayed

verified exact13
verified fuzzy7
unresolved9
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9c9199e5-1574-450a-a1be-1d4959801407 · outbound

This paper cites Counting positives accurately despite inaccurate classification.

Estimating prevalence with precision and accuracy Counting positives accurately despite inaccurate classification

Reference 1

Resolution: verified exact; doi, observed 2026-08-06T19:18:41.866019Z

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 11ac1edd-c7e4-4dee-ade2-2238825d4b3d · outbound

This paper cites Quantifying counts and costs via classification.

Estimating prevalence with precision and accuracy Quantifying counts and costs via classification

Reference 2

Resolution: verified exact; doi, observed 2026-08-06T19:18:41.523031Z

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 dcba73db-a235-4a97-9752-bd91fc0be52e · outbound

This paper cites Ag- gregative quantification for regression.

Estimating prevalence with precision and accuracy Ag- gregative quantification for regression

Reference 3

Resolution: verified exact; doi, observed 2026-08-06T19:18:41.222773Z

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 57be76ba-2fdd-4a6f-9400-1f0391ce524e · outbound

This paper cites Why is quantification an interesting learning problem? 6(1):53–58.

Estimating prevalence with precision and accuracy Why is quantification an interesting learning problem? 6(1):53–58

Reference 4

Resolution: verified exact; doi, observed 2026-08-06T19:18:40.848249Z

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 e390e3b6-9c41-4b4a-b529-d80ecc0ec5a3 · outbound

This paper cites Learning to Quantify, volume 47 of The Information Retrieval Series.

Estimating prevalence with precision and accuracy Learning to Quantify, volume 47 of The Information Retrieval Series

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T19:18:36.772667Z

Source-reported events for the cited work

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Observation e2142356-8c50-428e-b6f1-8fd550ea92a1 · outbound

This paper cites Confidence intervals for class prevalences under prior probability shift.

Estimating prevalence with precision and accuracy Confidence intervals for class prevalences under prior probability shift

Reference 6

Resolution: verified exact; doi, observed 2026-08-06T19:18:40.578869Z

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 c5a6db3f-d73b-4db7-ae7b-8e26ca6bcf6b · outbound

This paper cites When training and test sets are different: Characterizing learning transfer.

Estimating prevalence with precision and accuracy When training and test sets are different: Characterizing learning transfer

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:18:44.928891Z

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 bde0deb1-8630-4e00-94e9-bef4140c86a1 · outbound

This paper cites URL https://doi.org/10.7551/ mitpress/9780262170055.003.0001.

Estimating prevalence with precision and accuracy URL https://doi.org/10.7551/ mitpress/9780262170055.003.0001

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T19:18:37.144278Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e6b56d4-bdc5-40b4-8339-45df60146d09 · outbound

This paper cites Bayesian quantification with black-box estimators.

Estimating prevalence with precision and accuracy Bayesian quantification with black-box estimators

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:18:44.725110Z

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 54d4e12f-c96a-4cd1-a48e-52a4422cca2a · outbound

This paper cites Uncertainty-aware generative models for inferring document class prevalence.

Estimating prevalence with precision and accuracy Uncertainty-aware generative models for inferring document class prevalence

Reference 10

Resolution: malformed identifier; no resolver link, observed 2026-08-06T19:18:37.336277Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 607a3133-0dc7-4d07-92da-d314be0c42f2 · outbound

This paper cites Asif Naeem.

Estimating prevalence with precision and accuracy Asif Naeem

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T19:18:37.481670Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 60712cc3-c6db-4142-bd50-5473bc000f10 · outbound

This paper cites Generalized bayes quantifica- tion learning under dataset shift.

Estimating prevalence with precision and accuracy Generalized bayes quantifica- tion learning under dataset shift

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T19:18:37.573261Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6227f5ad-e256-470f-9a12-f9dfbbaf8fbe · outbound

This paper cites Detecting and Correcting for Label Shift with Black Box Predictors.

Estimating prevalence with precision and accuracy Detecting and Correcting for Label Shift with Black Box Predictors

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T19:18:37.753788Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e1f48b07-00de-4ea9-b8a8-a8dec6043534 · outbound

This paper cites QuaPy: A python-based framework for quantification.

Estimating prevalence with precision and accuracy QuaPy: A python-based framework for quantification

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T19:18:37.855854Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4caadce5-b264-49bc-9348-1cba638d69ea · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Estimating prevalence with precision and accuracy BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T19:18:37.941753Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9cf99a40-71bf-4065-bdcc-b1c46c1db30a · outbound

This paper cites The importance of the test set size in quantification assessment.

Estimating prevalence with precision and accuracy The importance of the test set size in quantification assessment

Reference 16

Resolution: verified exact; doi, observed 2026-08-06T19:18:40.281951Z

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 e612b9d8-17c5-45f1-9bd4-9b243ce9f8c2 · outbound

This paper cites Quan- tification via probability estimators.

Estimating prevalence with precision and accuracy Quan- tification via probability estimators

Reference 17

Resolution: verified exact; raw_fallback, observed 2026-08-06T19:18:42.892135Z

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 bde34629-426c-4b8d-b966-6e03251644ec · outbound

This paper cites Guzmán- Martínez, and Enrique Alegre.

Estimating prevalence with precision and accuracy Guzmán- Martínez, and Enrique Alegre

Reference 18

Resolution: verified exact; doi, observed 2026-08-06T19:18:40.021314Z

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 8d725617-efa3-431e-81eb-9644504b1a30 · outbound

This paper cites Adjusting the outputs of a classifier to new a priori probabilities: A simple procedure.

Estimating prevalence with precision and accuracy Adjusting the outputs of a classifier to new a priori probabilities: A simple procedure

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T19:18:38.297343Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa767fca-0b65-4e42-b781-8a96e117dc99 · outbound

This paper cites A critical reassessment of the saerens- latinne-decaestecker algorithm for posterior probability adjustment.

Estimating prevalence with precision and accuracy A critical reassessment of the saerens- latinne-decaestecker algorithm for posterior probability adjustment

Reference 20

Resolution: verified exact; doi, observed 2026-08-06T19:18:39.797529Z

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 b23e5a19-e841-457f-be33-1dd7467cc428 · outbound

This paper cites Maximum likelihood with bias- corrected calibration is hard-to-beat at label shift adaptation.

Estimating prevalence with precision and accuracy Maximum likelihood with bias- corrected calibration is hard-to-beat at label shift adaptation

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:18:44.433070Z

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 6e480561-d366-4eea-b873-5b71d154054d · outbound

This paper cites Minimising quantifier variance under prior probability shift.

Estimating prevalence with precision and accuracy Minimising quantifier variance under prior probability shift

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-08-06T19:18:42.643587Z

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 0a2055dd-a2fe-4fea-9287-f3e0f029419b · outbound

This paper cites Quantification-oriented learning based on reliable classifiers.

Estimating prevalence with precision and accuracy Quantification-oriented learning based on reliable classifiers

Reference 23

Resolution: verified exact; doi, observed 2026-08-06T19:18:39.586997Z

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 7860acd7-49ca-49b5-a7d8-5591c832b151 · outbound

This paper cites A Comparative Evaluation of Quantification Methods.

Estimating prevalence with precision and accuracy A Comparative Evaluation of Quantification Methods

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-08-06T19:18:42.416341Z

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 cdd0fc84-a299-41f2-bb75-dfb46d96e006 · outbound

This paper cites Quantification under prior probability shift: the ratio estimator and its extensions.

Estimating prevalence with precision and accuracy Quantification under prior probability shift: the ratio estimator and its extensions

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:18:44.108914Z

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 00113b7c-43a8-4575-89e9-ae1c256850bb · outbound

This paper cites an unresolved cited work.

Estimating prevalence with precision and accuracy Unresolved cited work

Reference 26

Resolution: verified exact; raw_fallback, observed 2026-08-06T19:18:42.205187Z

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 92b1b437-a058-4a3d-bb8e-f8bda209c7d0 · outbound

This paper cites an unresolved cited work.

Estimating prevalence with precision and accuracy Unresolved cited work

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T19:18:39.124306Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 37868674-2e63-41c1-bb4e-f50f4af1aeb6 · outbound

This paper cites Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods.

Estimating prevalence with precision and accuracy Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:18:43.837554Z

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 7f7ead77-4e7d-4350-aec3-2846c20203d4 · outbound

This paper cites That is, TPR and FPR are the same for the validation set and the test set.

Estimating prevalence with precision and accuracy That is, TPR and FPR are the same for the validation set and the test set

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:18:43.627678Z

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 bb88520b-8cb9-4451-bde7-1b4562c5016f · outbound

This paper cites class 1 prevalence in our case) in the validation data.

Estimating prevalence with precision and accuracy class 1 prevalence in our case) in the validation data

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:18:43.323066Z

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.

Pith citing papers

Observation 2799ab98-eeb2-4fdf-9e7e-7a86c6734c25 · inbound

Geometry-Aware Bayesian Quantification via Compositional Data Analysis cites this paper.

Geometry-Aware Bayesian Quantification via Compositional Data Analysis Estimating prevalence with precision and accuracy

Reference 26

Resolution: unresolved; no resolver link, observed 2026-07-11T10:38:19.251502Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.