Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Enhancing the Reliability of Segment Anything Model for Auto-Prompting Medical Image Segmentation with Uncertainty Rectification
As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2311.10529.
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, observed 2026-08-11T12:54:14.232792Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-11T08:20:57.703686Z
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 01be1d59-c2e9-4ad9-aeb0-85caaff60391 · inbound
Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation cites this paper.
Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation Enhancing the Reliability of Segment Anything Model for Auto-Prompting Medical Image Segmentation with Uncertainty Rectification
Reference 23
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T12:54:14.232792Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f88cf439-37fe-4eda-9b9b-7d550bb4b553 · inbound
Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges cites this paper.
Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges Enhancing the Reliability of Segment Anything Model for Auto-Prompting Medical Image Segmentation with Uncertainty Rectification
Reference 87
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T17:56:03.838493Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 943391fd-1164-4bb4-a074-9c855e09dcde · inbound
RobustMedSAM: Degradation-Resilient Medical Image Segmentation via Robust Foundation Model Adaptation cites this paper.
RobustMedSAM: Degradation-Resilient Medical Image Segmentation via Robust Foundation Model Adaptation Enhancing the Reliability of Segment Anything Model for Auto-Prompting Medical Image Segmentation with Uncertainty Rectification
Reference 21
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T08:20:57.706162Z
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.