Paper Citation Record · LEDGER

Estimating label quality and errors in semantic segmentation data via any model

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2307.05080.

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

pith.paper-citation-record.v1
2307.05080 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 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 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:30:05.706927Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T23:20:45.141897Z

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 10e1e8e8-7d15-4051-bddb-8de807e0b41c · inbound

HyperSORT: Self-Organising Robust Training with hyper-networks cites this paper.

HyperSORT: Self-Organising Robust Training with hyper-networks Estimating label quality and errors in semantic segmentation data via any model

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T22:30:05.706927Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df0d1acb-f236-4a61-9683-583d9e5fdb41 · inbound

Ordinal Adaptive Correction: A Data-Centric Approach to Ordinal Image Classification with Noisy Labels cites this paper.

Ordinal Adaptive Correction: A Data-Centric Approach to Ordinal Image Classification with Noisy Labels Estimating label quality and errors in semantic segmentation data via any model

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-21T23:20:45.144218Z

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 6e103b69-b573-4edd-a3e4-d08d0cffc8e2 · inbound

Towards Fairness under Label Bias in Image Segmentation: Impact, Measurement and Mitigation cites this paper.

Towards Fairness under Label Bias in Image Segmentation: Impact, Measurement and Mitigation Estimating label quality and errors in semantic segmentation data via any model

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-11T04:45:55.273328Z

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 9bded5ee-9fa2-490c-9ef2-083a52a9a5ab · inbound

Why Domain Matters: Domain-Aware Benchmarking of Underwater Object Detection and Annotation Quality cites this paper.

Why Domain Matters: Domain-Aware Benchmarking of Underwater Object Detection and Annotation Quality Estimating label quality and errors in semantic segmentation data via any model

Reference 14

Resolution: unresolved; no resolver link, observed 2026-07-14T10:43:35.408579Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.