Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Self-Prompting Polyp Segmentation in Colonoscopy using Hybrid Yolo-SAM 2 Model
As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2409.09484.
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 2026-08-11T19:05:16.902361Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-06T17:56:04.502152Z
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 a7be5402-2de8-40c9-91ab-59412d854243 · inbound
CrackESS: A Self-Prompting Crack Segmentation System for Edge Devices cites this paper.
CrackESS: A Self-Prompting Crack Segmentation System for Edge Devices Self-Prompting Polyp Segmentation in Colonoscopy using Hybrid Yolo-SAM 2 Model
Reference 18
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T19:05:16.902361Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e11aa6cd-0ed6-410e-9c79-f61587fc195e · inbound
Advancements in Medical Image Classification through Fine-Tuning Natural Domain Foundation Models cites this paper.
Advancements in Medical Image Classification through Fine-Tuning Natural Domain Foundation Models Self-Prompting Polyp Segmentation in Colonoscopy using Hybrid Yolo-SAM 2 Model
Reference 26
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:09:17.116757Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0756f776-404d-4cdb-9da9-1c80605c9b28 · 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 Self-Prompting Polyp Segmentation in Colonoscopy using Hybrid Yolo-SAM 2 Model
Reference 40
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T17:56:04.507057Z
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.