{"as_of":"2026-08-22T00:47:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:503a94bc8537b766d3d821177994405dfb5c59dd7fdb2cb666fc4d0b05d936b9","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":4,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":4,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-21T06:32:19.484+00:00","state":"measured"},{"denominator":4,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T11:59:48.655497Z","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":"2026-05-23T18:03:18.034947Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2211.11514","last_updated":"2022-11-21T14:57:04Z","snapshot_observed_at":"2026-08-20T19:33:36.257482Z","submitted_at":"2022-11-21T14:57:04Z","title":"ProSFDA: Prompt Learning based Source-free Domain Adaptation for Medical Image Segmentation","version":1},"cited_work":{"arxiv_id":"2211.11514","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2211.11514","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"499cb2a3-885a-4bec-af85-7b153574ca83","year":2022},"citing_paper":{"arxiv_id":"2411.05824","last_updated":"2026-04-18T08:52:52Z","snapshot_observed_at":"2026-08-15T20:08:45.245888Z","submitted_at":"2024-11-05T08:01:16Z","title":"Navigating Distribution Shifts in Medical Image Analysis: A Survey","version":3},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-05-23T18:02:49.355045Z"},"links":{"cited_paper":"/paper/2211.11514","citing_paper":"/paper/2411.05824"},"observation_digest":"sha256:438ab31458c3a2f87f7b62be6bc505602d798c86e5f8b1243e37e48f70b0c6c5","observation_id":"89629919-1f19-431c-92f1-de331ef15ee2","resolution":{"observed_at":"2026-05-23T18:03:18.038761Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2211.11514","last_updated":"2022-11-21T14:57:04Z","snapshot_observed_at":"2026-08-20T19:33:36.257482Z","submitted_at":"2022-11-21T14:57:04Z","title":"ProSFDA: Prompt Learning based Source-free Domain Adaptation for Medical Image Segmentation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.11514","snapshot_observed_at":"2026-08-10T15:38:03.413019Z","title":"Prosfda: prompt learning based source-free domain adaptation for medical image segmentation,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.13787","last_updated":"2025-01-23T16:04:23Z","snapshot_observed_at":"2026-08-19T04:05:25.863503Z","submitted_at":"2025-01-23T16:04:23Z","title":"Parameter-Efficient Fine-Tuning for Foundation Models","version":1},"reference_index":182,"source":"pdf_text","source_observed_at":"2026-08-10T15:38:03.413019Z"},"links":{"cited_paper":"/paper/2211.11514","citing_paper":"/paper/2501.13787"},"observation_digest":"sha256:b8d171dd6de7f0993e100b312344482460d7de009919a41de386a09156d6c94d","observation_id":"e9d775e4-2812-4ab6-9950-d5fcc0554ba7","resolution":{"observed_at":"2026-08-10T15:38:03.413019Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.11514","last_updated":"2022-11-21T14:57:04Z","snapshot_observed_at":"2026-08-20T19:33:36.257482Z","submitted_at":"2022-11-21T14:57:04Z","title":"ProSFDA: Prompt Learning based Source-free Domain Adaptation for Medical Image Segmentation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.11514","snapshot_observed_at":"2026-08-16T11:59:48.655497Z","title":"Prosfda: Prompt learning based source-free domain adaptation for medical image segmentation","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2504.14117","last_updated":"2025-04-19T00:33:16Z","snapshot_observed_at":"2026-08-20T16:10:58.025400Z","submitted_at":"2025-04-19T00:33:16Z","title":"PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models","version":1},"reference_index":252,"source":"pdf_text","source_observed_at":"2026-08-16T11:59:48.655497Z"},"links":{"cited_paper":"/paper/2211.11514","citing_paper":"/paper/2504.14117"},"observation_digest":"sha256:b797b37d574f0d73a18cc956415abca003c269219f3a42e06281b21df39d412f","observation_id":"4f886bb3-1359-4074-97e1-47f124c1f428","resolution":{"observed_at":"2026-08-16T11:59:48.655497Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.11514","last_updated":"2022-11-21T14:57:04Z","snapshot_observed_at":"2026-08-20T19:33:36.257482Z","submitted_at":"2022-11-21T14:57:04Z","title":"ProSFDA: Prompt Learning based Source-free Domain Adaptation for Medical Image Segmentation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.11514","snapshot_observed_at":"2026-08-15T21:26:17.743589Z","title":"Prosfda: Prompt learning based source-freedomainadaptationformedicalimagesegmentation.arXiv preprint arXiv:2211.11514","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.09927","last_updated":"2025-05-15T03:24:54Z","snapshot_observed_at":"2026-08-21T23:43:44.838132Z","submitted_at":"2025-05-15T03:24:54Z","title":"DDFP: Data-dependent Frequency Prompt for Source Free Domain Adaptation of Medical Image Segmentation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-15T21:26:17.743589Z"},"links":{"cited_paper":"/paper/2211.11514","citing_paper":"/paper/2505.09927"},"observation_digest":"sha256:ef51a1156e383d18871ad9b432c900b7d507764a778933e75a54ee0be5776a0d","observation_id":"de195481-2916-4ea3-a90f-a8df481e09a1","resolution":{"observed_at":"2026-08-15T21:26:17.743589Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2211.11514/citation-record","integrity":"/paper/2211.11514/integrity","json":"/paper/2211.11514/citation-record.json","paper":"/paper/2211.11514"},"outbound":[],"paper":{"arxiv_id":"2211.11514","last_updated":"2022-11-21T14:57:04Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-20T19:33:36.257482Z","submitted_at":"2022-11-21T14:57:04Z","title":"ProSFDA: Prompt Learning based Source-free Domain Adaptation for Medical Image Segmentation"},"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":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2211.11514."}