Paper Citation Record · LEDGER

Leveraging Vision Language Models for Specialized Agricultural Tasks

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2407.19617.

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

pith.paper-citation-record.v1
2407.19617 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 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 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:21:43.288941Z

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

3
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 c1bd91ab-3adb-4d73-9f69-851a67c12fa9 · inbound

Towards Large Reasoning Models for Agriculture cites this paper.

Towards Large Reasoning Models for Agriculture Leveraging Vision Language Models for Specialized Agricultural Tasks

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T14:21:43.288941Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 75735e5a-8b90-49a5-93c3-4b5a39c1b408 · inbound

ThumbnailTruth: A Multi-Modal LLM Approach for Detecting Misleading YouTube Thumbnails Across Diverse Cultural Settings cites this paper.

ThumbnailTruth: A Multi-Modal LLM Approach for Detecting Misleading YouTube Thumbnails Across Diverse Cultural Settings Leveraging Vision Language Models for Specialized Agricultural Tasks

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-05T06:00:51.139713Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a38518e-b28f-45af-bea0-d2d9c39921f7 · inbound

Are vision-language models ready to zero-shot replace supervised classification models in agriculture? cites this paper.

Are vision-language models ready to zero-shot replace supervised classification models in agriculture? Leveraging Vision Language Models for Specialized Agricultural Tasks

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-16T21:18:32.246263Z

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.

Observation 777f014a-4238-4700-ab0b-e7aed2202ba8 · inbound

CropVLM: A Domain-Adapted Vision-Language Model for Open-Set Crop Analysis cites this paper.

CropVLM: A Domain-Adapted Vision-Language Model for Open-Set Crop Analysis Leveraging Vision Language Models for Specialized Agricultural Tasks

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-12T10:46:31.130591Z

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.