{"as_of":"2026-08-18T19:15:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:be3c7703b3053a33f93ff96051a640265c896db313ed9d07e799f7b00915cbdb","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":2,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":2,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T22:47:42.019318Z","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":"2026-05-12T08:01:28.549141Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1807.11805","last_updated":"2018-08-08T09:29:37Z","snapshot_observed_at":"2026-08-18T09:11:19.435473Z","submitted_at":"2018-07-31T13:24:31Z","title":"Disaster Monitoring using Unmanned Aerial Vehicles and Deep Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1807.11805","snapshot_observed_at":"2026-08-10T22:47:42.019318Z","title":"Real -time detection of apple leaf diseases using deep learning approach based on improved convolutional neural networks","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2501.00669","last_updated":"2024-12-31T22:56:19Z","snapshot_observed_at":"2026-08-17T23:44:10.089906Z","submitted_at":"2024-12-31T22:56:19Z","title":"Leaf diseases detection using deep learning methods","version":1},"reference_index":3446,"source":"pdf_text","source_observed_at":"2026-08-10T22:47:42.019318Z"},"links":{"cited_paper":"/paper/1807.11805","citing_paper":"/paper/2501.00669"},"observation_digest":"sha256:67987a25ec22fce639749de89af85ee593206d0fb10a3fd555bd1d4155a49542","observation_id":"cf57b514-e825-4df5-b019-9d5da6d515f1","resolution":{"observed_at":"2026-08-10T22:47:42.019318Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1807.11805","last_updated":"2018-08-08T09:29:37Z","snapshot_observed_at":"2026-08-18T09:11:19.435473Z","submitted_at":"2018-07-31T13:24:31Z","title":"Disaster Monitoring using Unmanned Aerial Vehicles and Deep Learning","version":2},"cited_work":{"arxiv_id":"1807.11805","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1807.11805","snapshot_observed_at":"2026-07-04T22:58:23.694656Z","title":"Kamilaris and F","venue":null,"work_id":"0ffdf924-4130-4ddc-8f99-ac03023068f9","year":2018},"citing_paper":{"arxiv_id":"2605.08196","last_updated":"2026-05-05T19:05:21Z","snapshot_observed_at":"2026-08-04T09:34:37.316100Z","submitted_at":"2026-05-05T19:05:21Z","title":"Survey on Disaster Management Datasets for Remote Sensing Based Emergency Applications","version":1},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-05-12T01:24:46.294409Z"},"links":{"cited_paper":"/paper/1807.11805","citing_paper":"/paper/2605.08196"},"observation_digest":"sha256:abded9083636668607a3ed0647f16d6cd70a78dd4abb46926cd915b3063a9553","observation_id":"d7bba23f-b3b9-41e6-a8b0-cc7c7451d6fd","resolution":{"observed_at":"2026-07-04T22:58:23.694656Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/1807.11805/citation-record","integrity":"/paper/1807.11805/integrity","json":"/paper/1807.11805/citation-record.json","paper":"/paper/1807.11805"},"outbound":[],"paper":{"arxiv_id":"1807.11805","last_updated":"2018-08-08T09:29:37Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-18T09:11:19.435473Z","submitted_at":"2018-07-31T13:24:31Z","title":"Disaster Monitoring using Unmanned Aerial Vehicles and Deep Learning"},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1807.11805."}