Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-12T12:06:06.342647Z
Paper Citation Record · LEDGER
Uncertainty quantification for White Matter Hyperintensity segmentation detects silent failures and improves automated Fazekas quantification
As of 13 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 0 inbound Pith citation observations for arXiv:2411.17571.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
14 of 14 outbound references displayed
- verified exact1
- verified fuzzy6
- unresolved5
- parse uncertain0
- malformed identifier1
- metadata mismatch1
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 308c7633-e867-4d5c-869b-a77a5352100a · outbound
This paper cites On Stein Variational Neural Network Ensembles.
Uncertainty quantification for White Matter Hyperintensity segmentation detects silent failures and improves automated Fazekas quantification On Stein Variational Neural Network Ensembles
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T12:06:06.190105Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation be9c8e27-f337-4da4-af48-ccc4e7c6d82f · outbound
This paper cites V olume Only: Only the estimated volume from the SEnt model is used as a feature.
Uncertainty quantification for White Matter Hyperintensity segmentation detects silent failures and improves automated Fazekas quantification V olume Only: Only the estimated volume from the SEnt model is used as a feature
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T12:06:07.158028Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 4881236f-3b20-4161-abfa-e3402fe02652 · outbound
This paper cites Implicit Anatomical Rendering for Medical Image Segmentation with Stochastic Experts.
Uncertainty quantification for White Matter Hyperintensity segmentation detects silent failures and improves automated Fazekas quantification Implicit Anatomical Rendering for Medical Image Segmentation with Stochastic Experts
Reference 8
- Resolution
- verified exact
- local_arxiv, observed 2026-08-12T12:06:06.464176Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation eb61b4ed-a1ec-46f8-ad3f-8296cdf6f385 · outbound
This paper cites V olume Only: Only the estimated volume from the SEnt model is used as a feature.
Uncertainty quantification for White Matter Hyperintensity segmentation detects silent failures and improves automated Fazekas quantification V olume Only: Only the estimated volume from the SEnt model is used as a feature
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T12:06:07.122570Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 87b3d27b-4fb3-4766-9b17-982280f7c540 · outbound
This paper cites V olume Only: Only the estimated volume from the SEnt model is used as a feature.
Uncertainty quantification for White Matter Hyperintensity segmentation detects silent failures and improves automated Fazekas quantification V olume Only: Only the estimated volume from the SEnt model is used as a feature
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T12:06:07.097165Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 7ad9db23-8f4a-4590-86a4-56fe9119f9d9 · outbound
This paper cites V olume Only: Only the estimated volume from the SEnt model is used as a feature.
Uncertainty quantification for White Matter Hyperintensity segmentation detects silent failures and improves automated Fazekas quantification V olume Only: Only the estimated volume from the SEnt model is used as a feature
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T12:06:07.061370Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 4a25d72d-bc79-413d-bdc5-d4c8e1ede7f3 · outbound
This paper cites V olume Only: Only the estimated volume from the SEnt model is used as a feature.
Uncertainty quantification for White Matter Hyperintensity segmentation detects silent failures and improves automated Fazekas quantification V olume Only: Only the estimated volume from the SEnt model is used as a feature
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T12:06:07.024365Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation cde15f83-9835-4c58-8f18-ae0980089886 · outbound
This paper cites V olume Only: Only the estimated volume from the SEnt model is used as a feature.
Uncertainty quantification for White Matter Hyperintensity segmentation detects silent failures and improves automated Fazekas quantification V olume Only: Only the estimated volume from the SEnt model is used as a feature
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T12:06:06.994309Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation ac21c7ac-2a4d-47a6-8fbc-3c90c84af23b · outbound
This paper cites Stroke 42 (7), 1917–1922.
Uncertainty quantification for White Matter Hyperintensity segmentation detects silent failures and improves automated Fazekas quantification Stroke 42 (7), 1917–1922
Reference 2011
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T12:06:06.238370Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1297802c-c6c0-4eb5-87b4-9d5c43e17e63 · outbound
This paper cites an unresolved cited work.
Uncertainty quantification for White Matter Hyperintensity segmentation detects silent failures and improves automated Fazekas quantification Unresolved cited work
Reference 2013
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T12:06:06.258926Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4b994110-76cd-4b5c-9aa7-5c1e59374c79 · outbound
This paper cites Variational Bayesian Last Layers.
Uncertainty quantification for White Matter Hyperintensity segmentation detects silent failures and improves automated Fazekas quantification Variational Bayesian Last Layers
Reference 2018
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T12:06:06.224153Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2c61f7e2-1ab0-4310-8661-87e3efe832fe · outbound
This paper cites an unresolved cited work.
Uncertainty quantification for White Matter Hyperintensity segmentation detects silent failures and improves automated Fazekas quantification Unresolved cited work
Reference 2021
- Resolution
- unresolved
- raw_fallback, observed 2026-08-12T12:06:07.190408Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 872d6354-9ddf-4b45-bb1c-e4183a550ad6 · outbound
This paper cites an unresolved cited work.
Uncertainty quantification for White Matter Hyperintensity segmentation detects silent failures and improves automated Fazekas quantification Unresolved cited work
Reference 2022
- Resolution
- malformed identifier
- doi_truncated, observed 2026-08-12T12:06:06.413498Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 06aea9c7-c1ca-40cb-b271-cc382c701d67 · outbound
This paper cites In: International Conference on Medical Image Computing and Computer-Assisted Intervention.
Uncertainty quantification for White Matter Hyperintensity segmentation detects silent failures and improves automated Fazekas quantification In: International Conference on Medical Image Computing and Computer-Assisted Intervention
Reference 2023
- Resolution
- metadata mismatch
- raw_fallback, observed 2026-08-12T12:06:06.880162Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Pith citing papers
No inbound Pith citation observations are available.