Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Adapting Segment Anything Model (SAM) through Prompt-based Learning for Enhanced Protein Identification in Cryo-EM Micrographs
As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2311.16140.
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 2026-08-19T06:32:44.657259+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T21:44:05.758783Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-11T05:13:08.015239Z
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 302c9b31-0921-4f97-a429-fa5e56ec787f · inbound
Optimizing Prompt Strategies for SAM: Advancing lesion Segmentation Across Diverse Medical Imaging Modalities cites this paper.
Optimizing Prompt Strategies for SAM: Advancing lesion Segmentation Across Diverse Medical Imaging Modalities Adapting Segment Anything Model (SAM) through Prompt-based Learning for Enhanced Protein Identification in Cryo-EM Micrographs
Reference 4
- Resolution
- verified exact
- local_arxiv, observed 2026-08-11T05:13:08.020684Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 60dc2b1e-450b-4386-aace-8cfd29bfa0f5 · inbound
Promoting SAM for Camouflaged Object Detection via Selective Key Point-based Guidance cites this paper.
Promoting SAM for Camouflaged Object Detection via Selective Key Point-based Guidance Adapting Segment Anything Model (SAM) through Prompt-based Learning for Enhanced Protein Identification in Cryo-EM Micrographs
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T21:44:05.758783Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.