Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Evaluation of Segment Anything Model 2: The Role of SAM2 in the Underwater Environment
As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2408.02924.
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 2026-08-16T06:30:59.297886+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T04:37:38.523329Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-06T15:56:48.082352Z
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 e6b20397-241e-4f3d-813b-3dd378a79429 · inbound
Inspiring the Next Generation of Segment Anything Models: Comprehensively Evaluate SAM and SAM 2 with Diverse Prompts Towards Context-Dependent Concepts under Different Scenes cites this paper.
Inspiring the Next Generation of Segment Anything Models: Comprehensively Evaluate SAM and SAM 2 with Diverse Prompts Towards Context-Dependent Concepts under Different Scenes Evaluation of Segment Anything Model 2: The Role of SAM2 in the Underwater Environment
Reference 43
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T04:37:38.523329Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 516f6cbd-8cfb-4327-a907-6e4d737239f2 · inbound
Rethinking Detecting Salient and Camouflaged Objects in Unconstrained Scenes cites this paper.
Rethinking Detecting Salient and Camouflaged Objects in Unconstrained Scenes Evaluation of Segment Anything Model 2: The Role of SAM2 in the Underwater Environment
Reference 29
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T15:33:30.000229Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 880a2892-126b-4555-9ba5-b55e161e3c5c · inbound
When SAM2 Meets Video Shadow and Mirror Detection cites this paper.
When SAM2 Meets Video Shadow and Mirror Detection Evaluation of Segment Anything Model 2: The Role of SAM2 in the Underwater Environment
Reference 21
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T00:46:41.407535Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 96e6625d-0ec3-44e1-a84a-19de2b770795 · inbound
A Review of Generative AI in Aquaculture: Foundations, Applications, and Future Directions for Smart and Sustainable Farming cites this paper.
A Review of Generative AI in Aquaculture: Foundations, Applications, and Future Directions for Smart and Sustainable Farming Evaluation of Segment Anything Model 2: The Role of SAM2 in the Underwater Environment
Reference 239
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T17:00:38.532996Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5f5520be-901a-4af1-a9e8-ee5d349acc37 · inbound
AquaChat: An LLM-Guided ROV Framework for Adaptive Inspection of Aquaculture Net Pens cites this paper.
AquaChat: An LLM-Guided ROV Framework for Adaptive Inspection of Aquaculture Net Pens Evaluation of Segment Anything Model 2: The Role of SAM2 in the Underwater Environment
Reference 55
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T15:56:48.090669Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 3a7d664c-1f08-4346-a9b5-d2b4b8d8a5cb · inbound
Deep Learning for Accurate Vision-based Catch Composition in Tropical Tuna Purse Seiners cites this paper.
Deep Learning for Accurate Vision-based Catch Composition in Tropical Tuna Purse Seiners Evaluation of Segment Anything Model 2: The Role of SAM2 in the Underwater Environment
Reference 28
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T06:49:02.716378Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.