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Paper Citation Record · LEDGER

YOLOv1 to YOLOv10: A comprehensive review of YOLO variants and their application in the agricultural domain

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2406.10139.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2406.10139 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T10:46:46.398527Z

measured 1 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

51
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation aa233475-9fc4-4106-8c44-08af0deb7d56 · inbound

YOLOv8 to YOLO11: A Comprehensive Architecture In-depth Comparative Review cites this paper.

YOLOv8 to YOLO11: A Comprehensive Architecture In-depth Comparative Review YOLOv1 to YOLOv10: A comprehensive review of YOLO variants and their application in the agricultural domain

Reference 7

Resolution
verified exact
arxiv_id, observed 2026-05-23T05:12:34.662353Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-23T05:08:02.223581Z digest=sha256:7efbfbc4f9c5bf20f4a32764b991a66a0d46e4eb3bdd13d4e83e1f9419a3ed40

Observation b3d8e78e-6d89-47a1-b785-a38066b2be61 · inbound

Enhancing Quantum-ready QUBO-based Suppression for Object Detection with Appearance and Confidence Features cites this paper.

Enhancing Quantum-ready QUBO-based Suppression for Object Detection with Appearance and Confidence Features YOLOv1 to YOLOv10: A comprehensive review of YOLO variants and their application in the agricultural domain

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-09T10:46:46.398527Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T10:46:46.398527Z digest=sha256:9e730e259f4bdcb1acaa44b6dfb892b4e0e64c183b567dd0a57ebb24a0d3ee91

Observation c28383d0-50eb-4340-a723-351b3f1e868a · inbound

YOLOv4: A Breakthrough in Real-Time Object Detection cites this paper.

YOLOv4: A Breakthrough in Real-Time Object Detection YOLOv1 to YOLOv10: A comprehensive review of YOLO variants and their application in the agricultural domain

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-08T23:20:54.185822Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T23:20:54.185822Z digest=sha256:41ea0a63cb2fdf67102d5a9af1cb8773ffce9f999e3b338d097becc05c3a060e

Observation 7c974cfa-a230-4a1b-9d00-b6edee8b2d83 · inbound

A Low-Cost Machine Learning Approach for Timber Diameter Estimation cites this paper.

A Low-Cost Machine Learning Approach for Timber Diameter Estimation YOLOv1 to YOLOv10: A comprehensive review of YOLO variants and their application in the agricultural domain

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-06T14:57:55.473731Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T14:57:55.473731Z digest=sha256:313a448fcaaa1e94341922c04de6ef69e773cbabe58b6e5ec8af91f846339e86

Observation cbd2ce1f-1511-49e1-9f2a-fa5b8283d99c · inbound

A geometric and deep learning reproducible pipeline for monitoring floating anthropogenic debris in urban rivers using in situ cameras cites this paper.

A geometric and deep learning reproducible pipeline for monitoring floating anthropogenic debris in urban rivers using in situ cameras YOLOv1 to YOLOv10: A comprehensive review of YOLO variants and their application in the agricultural domain

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-04T07:54:02.180148Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T07:54:02.180148Z digest=sha256:3ee0eadff25f094f39f2e13a052f68b8d4d71551cf2d67cb668800873dd03086