Paper Citation Record · LEDGER

Synthetic Fungi Datasets: A Time-Aligned Approach

As of 11 August 2026, this Paper Citation Record lists 7 of 7 outbound references and 1 inbound Pith citation observation for arXiv:2501.02855.

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

pith.paper-citation-record.v1
2501.02855 v1

Coverage vector

measured 7 of 7 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:05:43.686100Z

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T10:10:57.650687Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T10:10:57.675344Z

Reference resolution

7 of 7 outbound references displayed

verified exact0
verified fuzzy7
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 821f0b2e-d2c5-416d-9ddb-b7c0a2af2fb6 · outbound

This paper cites A fungus spores dataset and a convolutional neural network based approach for fungus detection.

Synthetic Fungi Datasets: A Time-Aligned Approach A fungus spores dataset and a convolutional neural network based approach for fungus detection

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:05:43.850666Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d4e262ba-1699-4312-bda1-11107836690b · outbound

This paper cites Automatic fungi recognition: deep learning meets mycology.

Synthetic Fungi Datasets: A Time-Aligned Approach Automatic fungi recognition: deep learning meets mycology

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:05:43.828909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a23e02b0-4c6f-4769-9f73-b8405130be45 · outbound

This paper cites Plant disease detection by imaging sensors--parallels and specific demands for precision agriculture and plant phenotyping.

Synthetic Fungi Datasets: A Time-Aligned Approach Plant disease detection by imaging sensors--parallels and specific demands for precision agriculture and plant phenotyping

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:05:43.808164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f596c5a7-6f73-4982-9226-fc86f27aab83 · outbound

This paper cites Automated image-based analysis of spatio-temporal fungal dynamics.

Synthetic Fungi Datasets: A Time-Aligned Approach Automated image-based analysis of spatio-temporal fungal dynamics

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:05:43.789861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b097efd8-2483-4b08-9fc7-edbb308c8e30 · outbound

This paper cites Analysis of spatio-temporal fungal growth dynamics under different environmental conditions.

Synthetic Fungi Datasets: A Time-Aligned Approach Analysis of spatio-temporal fungal growth dynamics under different environmental conditions

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:05:43.772431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 2ef80489-dc91-474b-a04e-1b35d10a73ba · outbound

This paper cites Estimation of fungal biomass using multiphase artificial neural network based dynamic soft sensor.

Synthetic Fungi Datasets: A Time-Aligned Approach Estimation of fungal biomass using multiphase artificial neural network based dynamic soft sensor

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:05:43.754023Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 9b19685b-1a28-44e0-ad18-4856b045208a · outbound

This paper cites Deep learning approach to describe and classify fungi microscopic images.

Synthetic Fungi Datasets: A Time-Aligned Approach Deep learning approach to describe and classify fungi microscopic images

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:05:43.734864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Pith citing papers

Observation 1f1b6b55-5eea-4796-bb2b-4c01b29d5ef3 · inbound

CLIPTime: Time-Aware Multimodal Representation Learning from Images and Text cites this paper.

CLIPTime: Time-Aware Multimodal Representation Learning from Images and Text Synthetic Fungi Datasets: A Time-Aligned Approach

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-08-06T10:10:57.680562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.