Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
On the Viability of Semi-Supervised Segmentation Methods for Statistical Shape Modeling
As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2407.15260.
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 2026-08-23T06:30:58.430688+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T21:28:18.651885Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-11T11:11:01.049104Z
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 972e51f4-43fd-4a55-8d80-6a00a760fa22 · inbound
BoundarySeg:An Embarrassingly Simple Method To Boost Medical Image Segmentation Performance for Low Data Regimes cites this paper.
BoundarySeg:An Embarrassingly Simple Method To Boost Medical Image Segmentation Performance for Low Data Regimes On the Viability of Semi-Supervised Segmentation Methods for Statistical Shape Modeling
Reference 22
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T21:28:18.651885Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ab45d425-f75f-4bc6-b08e-ea1508ed5fc2 · inbound
MorphoFlow: Sparse-Supervised Generative Shape Modeling with Adaptive Latent Relevance cites this paper.
MorphoFlow: Sparse-Supervised Generative Shape Modeling with Adaptive Latent Relevance On the Viability of Semi-Supervised Segmentation Methods for Statistical Shape Modeling
Reference 29
- Resolution
- verified exact
- arxiv_id, observed 2026-08-04T01:51:28.580080Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.