{"as_of":"2026-08-17T07:33:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0d6aa7a5e0cc05fe51d287d07df201f4a64cad974fdd91dfcd1cf2616a4430c7","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":1,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":1,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T21:19:21.168874Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-16T12:16:17.039197Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2210.06885","last_updated":"2022-10-13T10:24:16Z","snapshot_observed_at":"2026-08-17T00:21:02.178040Z","submitted_at":"2022-10-13T10:24:16Z","title":"Geometric Active Learning for Segmentation of Large 3D Volumes","version":1},"cited_work":{"arxiv_id":"2210.06885","doi":"10.48550/arxiv.2210.06885","metadata_source":"pith","pith_arxiv_id":"2210.06885","snapshot_observed_at":"2026-08-16T12:16:17.039197Z","title":"Geometric Active Learning for Segmentation of Large 3D Volumes","venue":"cs.CV","work_id":"22184aff-e905-421f-9e44-fd0e408b9470","year":2022},"citing_paper":{"arxiv_id":"2505.10098","last_updated":"2025-05-15T09:00:02Z","snapshot_observed_at":"2026-08-15T21:14:20.347570Z","submitted_at":"2025-05-15T09:00:02Z","title":"Exploring Large Quantities of Secondary Data from High-Resolution Synchrotron X-ray Computed Tomography Scans Using AccuStripes","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-15T21:19:21.168874Z"},"links":{"cited_paper":"/paper/2210.06885","citing_paper":"/paper/2505.10098"},"observation_digest":"sha256:4c2f8b7ddb5995e92cabc7184c000f8d0d8e054ab5b21d68fbc570f6bb8960ca","observation_id":"18f2c1aa-d013-498c-884a-ce297929dc4a","resolution":{"observed_at":"2026-08-15T21:19:21.255391Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2210.06885/citation-record","integrity":"/paper/2210.06885/integrity","json":"/paper/2210.06885/citation-record.json","paper":"/paper/2210.06885"},"outbound":[],"paper":{"arxiv_id":"2210.06885","last_updated":"2022-10-13T10:24:16Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-17T00:21:02.178040Z","submitted_at":"2022-10-13T10:24:16Z","title":"Geometric Active Learning for Segmentation of Large 3D Volumes"},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2210.06885."}