Paper Citation Record · LEDGER

InspectionV3: Enhancing Tobacco Quality Assessment with Deep Convolutional Neural Networks for Automated Workshop Management

As of 18 August 2026, this Paper Citation Record lists 7 of 7 outbound references and 0 inbound Pith citation observations for arXiv:2505.16485.

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

pith.paper-citation-record.v1
2505.16485 v1

Coverage vector

measured 7 of 7 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:02:25.132205Z

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

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 415bb107-1819-4cfc-ab62-1c6b9f9139a2 · outbound

This paper cites Chang, W.

InspectionV3: Enhancing Tobacco Quality Assessment with Deep Convolutional Neural Networks for Automated Workshop Management Chang, W

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:02:25.566747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 517584f6-8f15-429a-abec-2a097e40004f · outbound

This paper cites Modeling of Asynchronous Mode –dependent Delays in Stochastic Markovian Jumping Modes Based on Static Neural Networks for Robotic Manipulators.

InspectionV3: Enhancing Tobacco Quality Assessment with Deep Convolutional Neural Networks for Automated Workshop Management Modeling of Asynchronous Mode –dependent Delays in Stochastic Markovian Jumping Modes Based on Static Neural Networks for Robotic Manipulators

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:02:25.713407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4dcf144d-0f89-4161-81c8-1213607c7239 · outbound

This paper cites Building on prior lightweight CNN model combined with LSTM -AM framework to guide fault detection in fixed -wing UAVs.

InspectionV3: Enhancing Tobacco Quality Assessment with Deep Convolutional Neural Networks for Automated Workshop Management Building on prior lightweight CNN model combined with LSTM -AM framework to guide fault detection in fixed -wing UAVs

Reference 1290

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:02:25.341943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f06d909f-3bf5-45b2-b167-a22675b34373 · outbound

This paper cites Journal of medical Internet research, 20(7), p.e236.

InspectionV3: Enhancing Tobacco Quality Assessment with Deep Convolutional Neural Networks for Automated Workshop Management Journal of medical Internet research, 20(7), p.e236

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:02:26.283228Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ff2c62fd-0477-4ec8-9d95-98412366586b · outbound

This paper cites Microprocessors and Microsystems, 80, p.103615.

InspectionV3: Enhancing Tobacco Quality Assessment with Deep Convolutional Neural Networks for Automated Workshop Management Microprocessors and Microsystems, 80, p.103615

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:02:26.137349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b95d8012-f0d2-4ced-944f-68f9b0114d92 · outbound

This paper cites A data-driven approach for intrusion and anomaly detection using automated machine learning for the Internet of Things.

InspectionV3: Enhancing Tobacco Quality Assessment with Deep Convolutional Neural Networks for Automated Workshop Management A data-driven approach for intrusion and anomaly detection using automated machine learning for the Internet of Things

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:02:25.896341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8ce45a7b-0ef1-4b2f-b61d-f1de9708b3bc · outbound

This paper cites Deep learning using chest radiographs to identify high -risk smokers for lung cancer screening computed tomography: development and validation of a prediction model.

InspectionV3: Enhancing Tobacco Quality Assessment with Deep Convolutional Neural Networks for Automated Workshop Management Deep learning using chest radiographs to identify high -risk smokers for lung cancer screening computed tomography: development and validation of a prediction model

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:02:26.442684Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

No inbound Pith citation observations are available.