Paper Citation Record · LEDGER

Reliability analysis for BraTS-GoAT segmentation: a controlled robustness study of deep-ensemble uncertainty

As of 22 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 0 inbound Pith citation observations for arXiv:2608.13223.

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

pith.paper-citation-record.v1
2608.13223 v1

Coverage vector

measured 17 of 17 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T15:58:47.830547Z

measured 17 of 17 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+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

17 of 17 outbound references displayed

verified exact0
verified fuzzy5
unresolved8
parse uncertain0
malformed identifier3
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9dd52f24-ee64-4e23-bb15-2abc0d4582d8 · outbound

This paper cites The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification.

Reliability analysis for BraTS-GoAT segmentation: a controlled robustness study of deep-ensemble uncertainty The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification

Reference 1

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Source-reported events for the cited work

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Observation 463eb78f-4f43-4f4a-8490-45502441dec8 · outbound

This paper cites Scientific Data4, 170117 (2017).https://doi.org/10.1038/sdata.2017.

Reliability analysis for BraTS-GoAT segmentation: a controlled robustness study of deep-ensemble uncertainty Scientific Data4, 170117 (2017).https://doi.org/10.1038/sdata.2017

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-14T15:58:46.904077Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7567e821-8477-41ca-92df-c48eac0fa233 · outbound

This paper cites Synapse:syn74274097 (2026).

Reliability analysis for BraTS-GoAT segmentation: a controlled robustness study of deep-ensemble uncertainty Synapse:syn74274097 (2026)

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:58:49.002021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 542028b6-09a0-4851-9540-3744abdde9e1 · outbound

This paper cites an unresolved cited work.

Reliability analysis for BraTS-GoAT segmentation: a controlled robustness study of deep-ensemble uncertainty Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-08-14T15:58:48.962994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2255d82b-feaa-40e7-bad5-6b484c282d00 · outbound

This paper cites In: Advances in Neural Information Processing Systems (NeurIPS) (2017).

Reliability analysis for BraTS-GoAT segmentation: a controlled robustness study of deep-ensemble uncertainty In: Advances in Neural Information Processing Systems (NeurIPS) (2017)

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:58:48.871810Z

Source-reported events for the cited work

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Observation bb25d7bd-9bd8-4bcb-b706-4c3e789d9868 · outbound

This paper cites In: International Conference on Machine Learning (ICML).

Reliability analysis for BraTS-GoAT segmentation: a controlled robustness study of deep-ensemble uncertainty In: International Conference on Machine Learning (ICML)

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-14T15:58:47.087969Z

Source-reported events for the cited work

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Observation 63d8534b-dc9e-4b29-b3e3-77abb055f2c6 · outbound

This paper cites Nature Methods18(2), 203–211 (2021).https://doi.org/10.1038/ s41592-020-01008-z.

Reliability analysis for BraTS-GoAT segmentation: a controlled robustness study of deep-ensemble uncertainty Nature Methods18(2), 203–211 (2021).https://doi.org/10.1038/ s41592-020-01008-z

Reference 7

Resolution: malformed identifier; raw_fallback, observed 2026-08-14T15:58:48.725171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 119f1fc7-ed79-411e-bc35-c42c2d0c9953 · outbound

This paper cites In: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (BrainLes 2020).

Reliability analysis for BraTS-GoAT segmentation: a controlled robustness study of deep-ensemble uncertainty In: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (BrainLes 2020)

Reference 8

Resolution: malformed identifier; raw_fallback, observed 2026-08-14T15:58:48.635737Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 73cd3a7c-ef1d-49cf-98f7-ac8124347f36 · outbound

This paper cites Nature Machine Intelligence5, 799–810 (2023).https://doi.org/10.1038/ s42256-023-00652-2.

Reliability analysis for BraTS-GoAT segmentation: a controlled robustness study of deep-ensemble uncertainty Nature Machine Intelligence5, 799–810 (2023).https://doi.org/10.1038/ s42256-023-00652-2

Reference 9

Resolution: malformed identifier; raw_fallback, observed 2026-08-14T15:58:48.604491Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 035cb26d-8aaf-498e-adc8-ef7bddfe02ad · outbound

This paper cites In: Advances in Neural Information Processing Systems (NeurIPS) (2017).

Reliability analysis for BraTS-GoAT segmentation: a controlled robustness study of deep-ensemble uncertainty In: Advances in Neural Information Processing Systems (NeurIPS) (2017)

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:58:48.462793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 65f30018-92c6-4c9b-bab3-5bfb04159573 · outbound

This paper cites Extending nn-UNet for brain tumor segmentation.

Reliability analysis for BraTS-GoAT segmentation: a controlled robustness study of deep-ensemble uncertainty Extending nn-UNet for brain tumor segmentation

Reference 11

Resolution: metadata mismatch; local_arxiv, observed 2026-08-14T15:58:48.121132Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 88c8d737-c86b-4fde-8655-fc817c1665b0 · outbound

This paper cites Nature Methods21, 195–212 (2024).https://doi.org/10.

Reliability analysis for BraTS-GoAT segmentation: a controlled robustness study of deep-ensemble uncertainty Nature Methods21, 195–212 (2024).https://doi.org/10

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:58:48.317681Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9a34bf54-02db-42f0-a1b1-f0279a36b5ad · outbound

This paper cites QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results.

Reliability analysis for BraTS-GoAT segmentation: a controlled robustness study of deep-ensemble uncertainty QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-14T15:58:47.553910Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e7c8bd77-2544-499b-9fc8-721eef0e4d83 · outbound

This paper cites IEEE Transactions on MedicalImaging34(10),1993–2024(2015).https://doi.org/10.1109/TMI.2014.

Reliability analysis for BraTS-GoAT segmentation: a controlled robustness study of deep-ensemble uncertainty IEEE Transactions on MedicalImaging34(10),1993–2024(2015).https://doi.org/10.1109/TMI.2014

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-14T15:58:47.657109Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b700a340-3cd2-4737-834d-e18726cd349a · outbound

This paper cites In: Advances in Neural Information Pro- cessing Systems (NeurIPS) (2019).

Reliability analysis for BraTS-GoAT segmentation: a controlled robustness study of deep-ensemble uncertainty In: Advances in Neural Information Pro- cessing Systems (NeurIPS) (2019)

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:58:48.252347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3728b518-adac-472d-a149-5b5990c86095 · outbound

This paper cites The 2024 Brain Tumor Segmentation (BraTS) Challenge: Glioma Segmentation on Post-treatment MRI.

Reliability analysis for BraTS-GoAT segmentation: a controlled robustness study of deep-ensemble uncertainty The 2024 Brain Tumor Segmentation (BraTS) Challenge: Glioma Segmentation on Post-treatment MRI

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-14T15:58:47.821241Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ea9cd22-f628-428b-b26c-927853e86b3c · outbound

This paper cites Neurocomputing338, 34–45 (2019).

Reliability analysis for BraTS-GoAT segmentation: a controlled robustness study of deep-ensemble uncertainty Neurocomputing338, 34–45 (2019)

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-14T15:58:47.830547Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.