{"as_of":"2026-08-24T04:14:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:12946fc584a40a787d831ad8e6e819fec7950c1f32196dea5737eb025c69d855","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":3,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":3,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-23T06:30:58.430688+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T18:07:57.499127Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-17T00:21:23.691088Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2406.07880","last_updated":"2025-05-02T14:05:41Z","snapshot_observed_at":"2026-08-16T13:43:54.668740Z","submitted_at":"2024-06-12T05:19:55Z","title":"A Comprehensive Survey on Machine Learning Driven Material Defect Detection","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.07880","snapshot_observed_at":"2026-08-10T18:27:38.945598Z","title":"Available: https://arxiv.org/abs/2406.07880","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.11310","last_updated":"2025-01-20T07:24:39Z","snapshot_observed_at":"2026-08-16T06:58:12.505859Z","submitted_at":"2025-01-20T07:24:39Z","title":"Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-10T18:27:38.945598Z"},"links":{"cited_paper":"/paper/2406.07880","citing_paper":"/paper/2501.11310"},"observation_digest":"sha256:5ca183c1c8d394840d5aff08d1e1f68111c4d218fe8c4e7099aa77922aaf7f97","observation_id":"7a73e6dc-6ec3-45e8-9923-65d03388080f","resolution":{"observed_at":"2026-08-10T18:27:38.945598Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.07880","last_updated":"2025-05-02T14:05:41Z","snapshot_observed_at":"2026-08-16T13:43:54.668740Z","submitted_at":"2024-06-12T05:19:55Z","title":"A Comprehensive Survey on Machine Learning Driven Material Defect Detection","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.07880","snapshot_observed_at":"2026-08-15T18:07:57.499127Z","title":"A comprehensive survey on machine learning driven material defect detection: Challenges, solutions, and future prospects,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.18952","last_updated":"2025-07-25T04:39:33Z","snapshot_observed_at":"2026-08-18T17:32:58.181708Z","submitted_at":"2025-07-25T04:39:33Z","title":"Legal Document Summarization: Enhancing Judicial Efficiency through Automation Detection","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-15T18:07:57.499127Z"},"links":{"cited_paper":"/paper/2406.07880","citing_paper":"/paper/2507.18952"},"observation_digest":"sha256:0978e727b8cd01cb83d9889e06e0f6c775b5a7310e1cd62452613e3aba47371b","observation_id":"a7dff37b-a814-48a9-abae-f585b1edf44f","resolution":{"observed_at":"2026-08-15T18:07:57.499127Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.07880","last_updated":"2025-05-02T14:05:41Z","snapshot_observed_at":"2026-08-16T13:43:54.668740Z","submitted_at":"2024-06-12T05:19:55Z","title":"A Comprehensive Survey on Machine Learning Driven Material Defect Detection","version":3},"cited_work":{"arxiv_id":"2406.07880","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.07880","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"A comprehensive survey on machine learning driven material defect detection","venue":null,"work_id":"38b0d0ca-671e-4101-8258-6c09882f6930","year":2024},"citing_paper":{"arxiv_id":"2512.06171","last_updated":"2026-04-23T10:42:33Z","snapshot_observed_at":"2026-08-07T18:51:13.316673Z","submitted_at":"2025-12-05T21:49:58Z","title":"Automated Annotation of Shearographic Measurements Enabling Weakly Supervised Defect Detection","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-17T00:19:31.834944Z"},"links":{"cited_paper":"/paper/2406.07880","citing_paper":"/paper/2512.06171"},"observation_digest":"sha256:bb31ee4ce16ebd58e9d4e82b526fb8dea3dfe3c241dfcb6280c0e51d176c4d69","observation_id":"7049008b-f9e1-4782-bbc7-2ef1512f85ed","resolution":{"observed_at":"2026-05-17T00:21:23.693211Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2406.07880/citation-record","integrity":"/paper/2406.07880/integrity","json":"/paper/2406.07880/citation-record.json","paper":"/paper/2406.07880"},"outbound":[],"paper":{"arxiv_id":"2406.07880","last_updated":"2025-05-02T14:05:41Z","latest_version":3,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-16T13:43:54.668740Z","submitted_at":"2024-06-12T05:19:55Z","title":"A Comprehensive Survey on Machine Learning Driven Material Defect Detection"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"thesis":"As of 24 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2406.07880."}