Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Real-time Monitoring and Analysis of Track and Field Athletes Based on Edge Computing and Deep Reinforcement Learning Algorithm
As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2411.06720.
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 2026-08-13T06:32:02.005865+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T17:19:45.661561Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-11T21:57:28.757693Z
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 f446b84c-23c2-4315-92bc-d31508d5d447 · inbound
IoT-Based 3D Pose Estimation and Motion Optimization for Athletes: Application of C3D and OpenPose cites this paper.
IoT-Based 3D Pose Estimation and Motion Optimization for Athletes: Application of C3D and OpenPose Real-time Monitoring and Analysis of Track and Field Athletes Based on Edge Computing and Deep Reinforcement Learning Algorithm
Reference 74
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T17:19:45.661561Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 04fc72cd-ea4c-4008-8919-07c204c56153 · inbound
Optimized CNNs for Rapid 3D Point Cloud Object Recognition cites this paper.
Optimized CNNs for Rapid 3D Point Cloud Object Recognition Real-time Monitoring and Analysis of Track and Field Athletes Based on Edge Computing and Deep Reinforcement Learning Algorithm
Reference 63
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T23:06:27.571858Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fdebe6f3-aabc-40ad-8aa7-40fb824dcf0f · inbound
Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis cites this paper.
Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis Real-time Monitoring and Analysis of Track and Field Athletes Based on Edge Computing and Deep Reinforcement Learning Algorithm
Reference 64
- Resolution
- verified exact
- local_arxiv, observed 2026-08-11T21:57:28.760980Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.