{"as_of":"2026-08-20T14:35:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0f11c9e4af935523efb28a2728873cad1b37cbdb969aa6c4bd11fa9e3e100bd2","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":"2026-08-20T06:33:59.587034+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":"2026-08-16T00:48:19.522232Z","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":"2026-05-23T19:43:23.340711Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2408.06170","last_updated":"2025-01-14T01:27:36Z","snapshot_observed_at":"2026-08-20T11:40:53.926813Z","submitted_at":"2024-08-12T14:16:10Z","title":"Zero-shot 3D Segmentation of Abdominal Organs in CT Scans Using Segment Anything Model 2: Adapting Video Tracking Capabilities for 3D Medical Imaging","version":4},"cited_work":{"arxiv_id":"2408.06170","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2408.06170","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2408.06170 (2024)","venue":null,"work_id":"2bbf6004-91ef-4eec-90c1-e87d9f31ccfe","year":2024},"citing_paper":{"arxiv_id":"2410.04960","last_updated":"2026-06-04T11:59:03Z","snapshot_observed_at":"2026-08-16T13:11:56.955238Z","submitted_at":"2024-10-07T11:59:54Z","title":"On Efficient Variants of Segment Anything Model: A Survey","version":5},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-23T19:42:24.122342Z"},"links":{"cited_paper":"/paper/2408.06170","citing_paper":"/paper/2410.04960"},"observation_digest":"sha256:35b0c85d0aafe415bd7e0fadaaff861edcc210c90b39914d30bb1e002809e08c","observation_id":"363c13bb-4456-47dc-8f96-007b4b2868c4","resolution":{"observed_at":"2026-05-23T19:43:23.343691Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.06170","last_updated":"2025-01-14T01:27:36Z","snapshot_observed_at":"2026-08-20T11:40:53.926813Z","submitted_at":"2024-08-12T14:16:10Z","title":"Zero-shot 3D Segmentation of Abdominal Organs in CT Scans Using Segment Anything Model 2: Adapting Video Tracking Capabilities for 3D Medical Imaging","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.06170","snapshot_observed_at":"2026-08-16T00:48:19.522232Z","title":"Zero- shot 3d segmentation of abdominal organs in ct scans us- ing segment anything model 2: Adapting video track- ing capabilities for 3d medical imaging","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.02753","last_updated":"2025-05-05T16:05:37Z","snapshot_observed_at":"2026-08-19T21:28:00.475501Z","submitted_at":"2025-05-05T16:05:37Z","title":"Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-16T00:48:19.522232Z"},"links":{"cited_paper":"/paper/2408.06170","citing_paper":"/paper/2505.02753"},"observation_digest":"sha256:5d2420d6fef89373f72b3d6b2f719ac6557a5db31b49e02f8d959bc22f4910c5","observation_id":"4e690a48-421b-4d96-9612-9c3f018df3dd","resolution":{"observed_at":"2026-08-16T00:48:19.522232Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2408.06170/citation-record","integrity":"/paper/2408.06170/integrity","json":"/paper/2408.06170/citation-record.json","paper":"/paper/2408.06170"},"outbound":[],"paper":{"arxiv_id":"2408.06170","last_updated":"2025-01-14T01:27:36Z","latest_version":4,"primary_category":"eess.IV","snapshot_observed_at":"2026-08-20T11:40:53.926813Z","submitted_at":"2024-08-12T14:16:10Z","title":"Zero-shot 3D Segmentation of Abdominal Organs in CT Scans Using Segment Anything Model 2: Adapting Video Tracking Capabilities for 3D Medical Imaging"},"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":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2408.06170."}