{"as_of":"2026-08-09T02:07:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:17ff1b984367356feb5463b7e035dcf4e999c2a38d9c28d72970a20a0505c222","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":"2026-08-08T06:32:00.761636+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":"2026-07-31T18:28:55.336300Z","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":"2026-05-19T02:06:58.769990Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2502.01785","last_updated":"2025-02-03T19:56:16Z","snapshot_observed_at":"2026-07-06T20:30:36.418341Z","submitted_at":"2025-02-03T19:56:16Z","title":"AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis","version":1},"cited_work":{"arxiv_id":"2502.01785","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2502.01785","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Aquaticclip: A vision-language foundation model for underwater scene analysis","venue":null,"work_id":"6cda2bd1-bb67-4ac4-8403-158922565a02","year":2025},"citing_paper":{"arxiv_id":"2507.22101","last_updated":"2026-05-05T13:51:40Z","snapshot_observed_at":"2026-07-06T22:04:49.159747Z","submitted_at":"2025-07-29T17:59:48Z","title":"AI in Agriculture: A Survey of Deep Learning Techniques for Crops, Fisheries and Livestock","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-19T02:03:46.331803Z"},"links":{"cited_paper":"/paper/2502.01785","citing_paper":"/paper/2507.22101"},"observation_digest":"sha256:19965492464ffd946919fd8cb838569dafecbfefabed9e2a5c3b3ed176aa4f38","observation_id":"835b0549-c4ff-4923-99f6-cd84e49684ec","resolution":{"observed_at":"2026-05-19T02:06:58.772657Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.01785","last_updated":"2025-02-03T19:56:16Z","snapshot_observed_at":"2026-07-06T20:30:36.418341Z","submitted_at":"2025-02-03T19:56:16Z","title":"AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis","version":1},"cited_work":{"arxiv_id":"2502.01785","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2502.01785","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Aquaticclip: A vision-language foundation model for underwater scene analysis","venue":null,"work_id":"6cda2bd1-bb67-4ac4-8403-158922565a02","year":2025},"citing_paper":{"arxiv_id":"2604.00313","last_updated":"2026-04-15T20:28:57Z","snapshot_observed_at":"2026-07-06T22:51:22.254518Z","submitted_at":"2026-03-31T23:27:09Z","title":"Label-efficient underwater species classification with logistic regression on frozen foundation model embeddings","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-08T02:19:09.609298Z"},"links":{"cited_paper":"/paper/2502.01785","citing_paper":"/paper/2604.00313"},"observation_digest":"sha256:f86d7158466d9087b5d2aca3292743da3b1d1fef9693c11e2dc4f60165edc096","observation_id":"3e79eda9-a4fc-43ff-abde-a0ccd1ac9cae","resolution":{"observed_at":"2026-05-11T22:51:23.115841Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.01785","last_updated":"2025-02-03T19:56:16Z","snapshot_observed_at":"2026-07-06T20:30:36.418341Z","submitted_at":"2025-02-03T19:56:16Z","title":"AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.01785","snapshot_observed_at":"2026-07-31T18:28:55.336300Z","title":"Aquaticclip: A vision-language foundation modelforunderwatersceneanalysis","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.24313","last_updated":"2026-07-27T11:57:55Z","snapshot_observed_at":"2026-08-07T02:25:03.860012Z","submitted_at":"2026-07-27T11:57:55Z","title":"Energy Constrained Hierarchical Underwater Monitoring via Local Multi-Agent RAG","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-07-31T18:28:55.336300Z"},"links":{"cited_paper":"/paper/2502.01785","citing_paper":"/paper/2607.24313"},"observation_digest":"sha256:caf7dc722fd85c5e266fc40a84214c03d4c89ad23396749a55b781fb509e0787","observation_id":"3f1cd8d7-5200-4278-aef5-7452f7ab6fd0","resolution":{"observed_at":"2026-07-31T18:28:55.336300Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2502.01785/citation-record","integrity":"/paper/2502.01785/integrity","json":"/paper/2502.01785/citation-record.json","paper":"/paper/2502.01785"},"outbound":[],"paper":{"arxiv_id":"2502.01785","last_updated":"2025-02-03T19:56:16Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-07-06T20:30:36.418341Z","submitted_at":"2025-02-03T19:56:16Z","title":"AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis"},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2502.01785."}