Paper Citation Record · LEDGER

A Novel Method to Evaluate Models on Unreliable, Noisy and Inconsistent Labels: Adaptive Resolution Label Aggregation (ARLA)

As of 8 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:2607.11214.

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

pith.paper-citation-record.v1
2607.11214 v1

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-14T06:14:56.071808Z

measured 27 of 27 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

27 of 27 outbound references displayed

verified exact14
verified fuzzy0
unresolved13
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 14916bf6-812b-4280-b7c7-28129f155d7c · outbound

This paper cites Guidelines to compare semantic segmentation maps at different resolutions.IEEE Transactions on Geoscience and Remote Sensing, 62:1–16, 2024.

A Novel Method to Evaluate Models on Unreliable, Noisy and Inconsistent Labels: Adaptive Resolution Label Aggregation (ARLA) Guidelines to compare semantic segmentation maps at different resolutions.IEEE Transactions on Geoscience and Remote Sensing, 62:1–16, 2024

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-07-14T06:20:16.794993Z

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 4fa8ec17-e9d8-4102-b7a6-7afad821396e · outbound

This paper cites Active label cleaning for improved dataset quality under resource constraints.Nature communications, 13(1):1161, 2022.

A Novel Method to Evaluate Models on Unreliable, Noisy and Inconsistent Labels: Adaptive Resolution Label Aggregation (ARLA) Active label cleaning for improved dataset quality under resource constraints.Nature communications, 13(1):1161, 2022

Reference 2

Resolution: unresolved; no resolver link, observed 2026-07-14T06:14:56.071808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d5144566-6dd6-4e07-abef-d095b25130af · outbound

This paper cites Boundary iou: Improving object-centric image segmentation evaluation.

A Novel Method to Evaluate Models on Unreliable, Noisy and Inconsistent Labels: Adaptive Resolution Label Aggregation (ARLA) Boundary iou: Improving object-centric image segmentation evaluation

Reference 3

Resolution: unresolved; no resolver link, observed 2026-07-14T06:14:56.071808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c435deb4-7bf0-4508-bd0d-48c66a7344ba · outbound

This paper cites Weighted intersection over union (wiou) for evaluating image segmentation.Pattern Recognition Letters, 185:101–107, 2024.

A Novel Method to Evaluate Models on Unreliable, Noisy and Inconsistent Labels: Adaptive Resolution Label Aggregation (ARLA) Weighted intersection over union (wiou) for evaluating image segmentation.Pattern Recognition Letters, 185:101–107, 2024

Reference 4

Resolution: verified exact; doi, observed 2026-07-14T06:20:16.757381Z

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 6f566d4e-bd2c-4c89-be7e-c4d06393d426 · outbound

This paper cites The cityscapes dataset for semantic urban scene understanding.

A Novel Method to Evaluate Models on Unreliable, Noisy and Inconsistent Labels: Adaptive Resolution Label Aggregation (ARLA) The cityscapes dataset for semantic urban scene understanding

Reference 5

Resolution: unresolved; no resolver link, observed 2026-07-14T06:14:56.071808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 101000dc-e2a0-473f-9f13-0a4bebc3fd52 · outbound

This paper cites Copernicus emergency management service on-demand mapping, 2026.

A Novel Method to Evaluate Models on Unreliable, Noisy and Inconsistent Labels: Adaptive Resolution Label Aggregation (ARLA) Copernicus emergency management service on-demand mapping, 2026

Reference 6

Resolution: unresolved; no resolver link, observed 2026-07-14T06:14:56.071808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 581841fe-3bf6-4c85-a8a8-2733d0ddeb99 · outbound

This paper cites Data labeling: An empirical investigation into industrial challenges and mitigation strategies.

A Novel Method to Evaluate Models on Unreliable, Noisy and Inconsistent Labels: Adaptive Resolution Label Aggregation (ARLA) Data labeling: An empirical investigation into industrial challenges and mitigation strategies

Reference 7

Resolution: verified exact; doi, observed 2026-07-14T06:20:16.803264Z

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 f8ab2f8d-1ceb-432a-a658-23c0dc5409f5 · outbound

This paper cites Learning and evaluation in presence of non-iid label noise.

A Novel Method to Evaluate Models on Unreliable, Noisy and Inconsistent Labels: Adaptive Resolution Label Aggregation (ARLA) Learning and evaluation in presence of non-iid label noise

Reference 8

Resolution: unresolved; no resolver link, observed 2026-07-14T06:14:56.071808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8063c8c2-cd73-4dbf-8f9d-b0cafa32a3fe · outbound

This paper cites Evaluating Classification Systems Against Soft Labels with Fuzzy Precision and Recall.

A Novel Method to Evaluate Models on Unreliable, Noisy and Inconsistent Labels: Adaptive Resolution Label Aggregation (ARLA) Evaluating Classification Systems Against Soft Labels with Fuzzy Precision and Recall

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-07-14T06:20:16.808247Z

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 d833d624-00dc-4ff0-ab64-3a68ac4269dc · outbound

This paper cites Perfect labelling: A review and outlook of label optimization techniques in dynamic earth observation.Remote Sensing, 17(7):1246, 2025.

A Novel Method to Evaluate Models on Unreliable, Noisy and Inconsistent Labels: Adaptive Resolution Label Aggregation (ARLA) Perfect labelling: A review and outlook of label optimization techniques in dynamic earth observation.Remote Sensing, 17(7):1246, 2025

Reference 10

Resolution: verified exact; doi, observed 2026-07-14T06:20:16.728854Z

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 514a3b32-4135-4f9a-bfbf-112b915e86af · outbound

This paper cites Soft labels for training and evaluating semantic segmentation models.

A Novel Method to Evaluate Models on Unreliable, Noisy and Inconsistent Labels: Adaptive Resolution Label Aggregation (ARLA) Soft labels for training and evaluating semantic segmentation models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-07-14T06:14:56.071808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation afe994e7-2820-4bc7-96f8-ee45b9b266f9 · outbound

This paper cites Deep learning with noisy labels: Ex- ploring techniques and remedies in medical image analysis.Medical image analysis, 65:101759, 2020.

A Novel Method to Evaluate Models on Unreliable, Noisy and Inconsistent Labels: Adaptive Resolution Label Aggregation (ARLA) Deep learning with noisy labels: Ex- ploring techniques and remedies in medical image analysis.Medical image analysis, 65:101759, 2020

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-07-14T06:20:16.769278Z

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 d07ab7a1-8af3-4c84-b2b5-be47daec294c · outbound

This paper cites Noisy Annotations in Semantic Segmentation.

A Novel Method to Evaluate Models on Unreliable, Noisy and Inconsistent Labels: Adaptive Resolution Label Aggregation (ARLA) Noisy Annotations in Semantic Segmentation

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-07-14T06:20:16.775726Z

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 a0c2e879-e03b-4180-b8df-2f8700a21eca · outbound

This paper cites Evaluating segmentation error without ground truth.

A Novel Method to Evaluate Models on Unreliable, Noisy and Inconsistent Labels: Adaptive Resolution Label Aggregation (ARLA) Evaluating segmentation error without ground truth

Reference 14

Resolution: unresolved; no resolver link, observed 2026-07-14T06:14:56.071808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fada2e6e-d0bd-40ff-8fd0-dcdd59d54e93 · outbound

This paper cites Evaluating classifiers by means of test data with noisy labels.

A Novel Method to Evaluate Models on Unreliable, Noisy and Inconsistent Labels: Adaptive Resolution Label Aggregation (ARLA) Evaluating classifiers by means of test data with noisy labels

Reference 15

Resolution: unresolved; no resolver link, observed 2026-07-14T06:14:56.071808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4558be6-e4e3-41a0-aafe-58f14264a753 · outbound

This paper cites Computing precision and recall with missing or uncertain ground truth.

A Novel Method to Evaluate Models on Unreliable, Noisy and Inconsistent Labels: Adaptive Resolution Label Aggregation (ARLA) Computing precision and recall with missing or uncertain ground truth

Reference 16

Resolution: verified exact; doi, observed 2026-07-14T06:20:16.788422Z

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 2c3ff120-60d5-422f-bfe9-4d4e3450d806 · outbound

This paper cites an unresolved cited work.

A Novel Method to Evaluate Models on Unreliable, Noisy and Inconsistent Labels: Adaptive Resolution Label Aggregation (ARLA) Unresolved cited work

Reference 17

Resolution: verified exact; doi, observed 2026-07-14T06:20:16.740151Z

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 03103ebe-d77e-4e02-a9aa-988958671723 · outbound

This paper cites Confident learning: Estimating uncertainty in dataset labels.Journal of Artificial Intelligence Research, 70:1373–1411, 2021.

A Novel Method to Evaluate Models on Unreliable, Noisy and Inconsistent Labels: Adaptive Resolution Label Aggregation (ARLA) Confident learning: Estimating uncertainty in dataset labels.Journal of Artificial Intelligence Research, 70:1373–1411, 2021

Reference 18

Resolution: unresolved; no resolver link, observed 2026-07-14T06:14:56.071808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c8e8a57a-9880-4be8-9c03-8b7aa0cb71ca · outbound

This paper cites Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks.

A Novel Method to Evaluate Models on Unreliable, Noisy and Inconsistent Labels: Adaptive Resolution Label Aggregation (ARLA) Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks

Reference 19

Resolution: unresolved; no resolver link, observed 2026-07-14T06:14:56.071808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c1fdefba-6803-49ba-a1a0-6f080fbacf36 · outbound

This paper cites an unresolved cited work.

A Novel Method to Evaluate Models on Unreliable, Noisy and Inconsistent Labels: Adaptive Resolution Label Aggregation (ARLA) Unresolved cited work

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-07-14T06:20:16.767679Z

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 770af0cb-0712-4364-8afd-3f41e4281cb0 · outbound

This paper cites Data programming: Creating large training sets, quickly.Advances in neural information processing systems, 29, 2016.

A Novel Method to Evaluate Models on Unreliable, Noisy and Inconsistent Labels: Adaptive Resolution Label Aggregation (ARLA) Data programming: Creating large training sets, quickly.Advances in neural information processing systems, 29, 2016

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-07-14T06:20:16.729364Z

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 e055a0c8-4383-44e2-9fd7-32c0a06034d3 · outbound

This paper cites Deep Learning is Robust to Massive Label Noise.

A Novel Method to Evaluate Models on Unreliable, Noisy and Inconsistent Labels: Adaptive Resolution Label Aggregation (ARLA) Deep Learning is Robust to Massive Label Noise

Reference 22

Resolution: unresolved; no resolver link, observed 2026-07-14T06:14:56.071808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f542fdaf-0ebd-423b-97b6-52e8248a172f · outbound

This paper cites Learning from noisy labels with deep neural networks: A survey.IEEE transactions on neural networks and learning systems, 34(11):8135–8153, 2022.

A Novel Method to Evaluate Models on Unreliable, Noisy and Inconsistent Labels: Adaptive Resolution Label Aggregation (ARLA) Learning from noisy labels with deep neural networks: A survey.IEEE transactions on neural networks and learning systems, 34(11):8135–8153, 2022

Reference 23

Resolution: unresolved; no resolver link, observed 2026-07-14T06:14:56.071808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 413d0520-88f1-49b3-bfed-3368e6f08238 · outbound

This paper cites Evaluating medical ai systems in dermatology under uncertain ground truth.Medical Image Analysis, 103:103556, 2025.

A Novel Method to Evaluate Models on Unreliable, Noisy and Inconsistent Labels: Adaptive Resolution Label Aggregation (ARLA) Evaluating medical ai systems in dermatology under uncertain ground truth.Medical Image Analysis, 103:103556, 2025

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-07-14T06:20:16.753309Z

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 3191a562-2785-46bf-b5f7-a6c55a8e4199 · outbound

This paper cites Are ground truth labels reproducible? an empirical study.

A Novel Method to Evaluate Models on Unreliable, Noisy and Inconsistent Labels: Adaptive Resolution Label Aggregation (ARLA) Are ground truth labels reproducible? an empirical study

Reference 25

Resolution: unresolved; no resolver link, observed 2026-07-14T06:14:56.071808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d4bf0901-baea-49fe-ad33-4c91139602dd · outbound

This paper cites Assessing inter-annotator agreement for medical image segmentation.IEEe Access, 11:21300–21312, 2023.

A Novel Method to Evaluate Models on Unreliable, Noisy and Inconsistent Labels: Adaptive Resolution Label Aggregation (ARLA) Assessing inter-annotator agreement for medical image segmentation.IEEe Access, 11:21300–21312, 2023

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-07-14T06:20:16.782226Z

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 845b989b-5868-4264-84d3-bff9c1eda4b6 · outbound

This paper cites Uanet: An uncertainty-aware network for feature calibration and boundary refinement in medical image segmentation.Biomedical Signal Processing and Control, 112:108413, 2026.

A Novel Method to Evaluate Models on Unreliable, Noisy and Inconsistent Labels: Adaptive Resolution Label Aggregation (ARLA) Uanet: An uncertainty-aware network for feature calibration and boundary refinement in medical image segmentation.Biomedical Signal Processing and Control, 112:108413, 2026

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-07-14T06:20:16.735321Z

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

No inbound Pith citation observations are available.