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Paper Citation Record · LEDGER

A Comprehensive Survey on Machine Learning Driven Material Defect Detection

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2406.07880.

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

pith.paper-citation-record.v1
2406.07880 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T18:07:57.499127Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-17T00:21:23.691088Z

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 7a73e6dc-6ec3-45e8-9923-65d03388080f · inbound

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review cites this paper.

Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review A Comprehensive Survey on Machine Learning Driven Material Defect Detection

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-10T18:27:38.945598Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T18:27:38.945598Z digest=sha256:5ca183c1c8d394840d5aff08d1e1f68111c4d218fe8c4e7099aa77922aaf7f97

Observation a7dff37b-a814-48a9-abae-f585b1edf44f · inbound

Legal Document Summarization: Enhancing Judicial Efficiency through Automation Detection cites this paper.

Legal Document Summarization: Enhancing Judicial Efficiency through Automation Detection A Comprehensive Survey on Machine Learning Driven Material Defect Detection

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-15T18:07:57.499127Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T18:07:57.499127Z digest=sha256:0978e727b8cd01cb83d9889e06e0f6c775b5a7310e1cd62452613e3aba47371b

Observation 7049008b-f9e1-4782-bbc7-2ef1512f85ed · inbound

Automated Annotation of Shearographic Measurements Enabling Weakly Supervised Defect Detection cites this paper.

Automated Annotation of Shearographic Measurements Enabling Weakly Supervised Defect Detection A Comprehensive Survey on Machine Learning Driven Material Defect Detection

Reference 3

Resolution
verified exact
arxiv_id, observed 2026-05-17T00:21:23.693211Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-17T00:19:31.834944Z digest=sha256:bb31ee4ce16ebd58e9d4e82b526fb8dea3dfe3c241dfcb6280c0e51d176c4d69