{"as_of":"2026-08-13T08:01:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b1b36627f414cc50f0e609d4a1d1befd2154c9312a598410daca3f22f48238c3","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":5,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":5,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T23:41:46.963824Z","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":"2026-07-03T20:28:55.438105Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2301.04465","last_updated":"2023-05-26T15:14:45Z","snapshot_observed_at":"2026-08-13T04:49:42.166259Z","submitted_at":"2023-01-11T13:48:37Z","title":"Co-training with High-Confidence Pseudo Labels for Semi-supervised Medical Image Segmentation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.04465","snapshot_observed_at":"2026-08-11T23:41:46.963824Z","title":"Co-training with high-confidence pseudo labels for semi-supervised medical image segmentation,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.02314","last_updated":"2025-01-31T09:29:44Z","snapshot_observed_at":"2026-08-11T23:33:42.902364Z","submitted_at":"2024-12-03T09:31:16Z","title":"Low-Contrast-Enhanced Contrastive Learning for Semi-Supervised Endoscopic Image Segmentation","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T23:41:46.963824Z"},"links":{"cited_paper":"/paper/2301.04465","citing_paper":"/paper/2412.02314"},"observation_digest":"sha256:3f8eb106bb0d34d951c348058a85c7b6f82e54cee628c5b1d072d0d5df19d219","observation_id":"6e84f93a-b372-4ff7-84aa-286ed643f83d","resolution":{"observed_at":"2026-08-11T23:41:46.963824Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2301.04465","last_updated":"2023-05-26T15:14:45Z","snapshot_observed_at":"2026-08-13T04:49:42.166259Z","submitted_at":"2023-01-11T13:48:37Z","title":"Co-training with High-Confidence Pseudo Labels for Semi-supervised Medical Image Segmentation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.04465","snapshot_observed_at":"2026-08-11T12:54:14.199319Z","title":"Co- training with high-confidence pseudo labels for semi-supervised medical image segmentation,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.13742","last_updated":"2024-12-18T11:19:23Z","snapshot_observed_at":"2026-08-12T06:48:27.404554Z","submitted_at":"2024-12-18T11:19:23Z","title":"Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T12:54:14.199319Z"},"links":{"cited_paper":"/paper/2301.04465","citing_paper":"/paper/2412.13742"},"observation_digest":"sha256:dc46f54611fea61e6f635b536ada03aea62e4ad42093131f8bc756ac3580b135","observation_id":"9aa517df-6a5b-4401-ba3f-07f96252747b","resolution":{"observed_at":"2026-08-11T12:54:14.199319Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2301.04465","last_updated":"2023-05-26T15:14:45Z","snapshot_observed_at":"2026-08-13T04:49:42.166259Z","submitted_at":"2023-01-11T13:48:37Z","title":"Co-training with High-Confidence Pseudo Labels for Semi-supervised Medical Image Segmentation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.04465","snapshot_observed_at":"2026-08-11T11:31:29.972183Z","title":"Co-training with high-confidence pseudo labels for semi-supervised medical image segmentation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.15380","last_updated":"2024-12-19T20:16:58Z","snapshot_observed_at":"2026-08-12T17:37:48.618506Z","submitted_at":"2024-12-19T20:16:58Z","title":"Uncertainty-Guided Cross Attention Ensemble Mean Teacher for Semi-supervised Medical Image Segmentation","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-11T11:31:29.972183Z"},"links":{"cited_paper":"/paper/2301.04465","citing_paper":"/paper/2412.15380"},"observation_digest":"sha256:1dad17ec176b3f22bf2b0c8b6d4671b898cff4e868ae55816d05a18015202f29","observation_id":"50a51ff0-f0c0-454a-b8cf-83577191e1f5","resolution":{"observed_at":"2026-08-11T11:31:29.972183Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2301.04465","last_updated":"2023-05-26T15:14:45Z","snapshot_observed_at":"2026-08-13T04:49:42.166259Z","submitted_at":"2023-01-11T13:48:37Z","title":"Co-training with High-Confidence Pseudo Labels for Semi-supervised Medical Image Segmentation","version":3},"cited_work":{"arxiv_id":"2301.04465","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2301.04465","snapshot_observed_at":"2026-07-03T20:28:55.438105Z","title":"Co-training with high- confidence pseudo labels for semi-supervised medical image segmentation","venue":null,"work_id":"0fc4f4c3-a89f-4fd5-8cd8-d41e1ae5a4d3","year":2023},"citing_paper":{"arxiv_id":"2604.09169","last_updated":"2026-04-10T09:53:33Z","snapshot_observed_at":"2026-08-13T07:27:16.584339Z","submitted_at":"2026-04-10T09:53:33Z","title":"UniSemAlign: Text-Prototype Alignment with a Foundation Encoder for Semi-Supervised Histopathology Segmentation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-10T17:14:00.689520Z"},"links":{"cited_paper":"/paper/2301.04465","citing_paper":"/paper/2604.09169"},"observation_digest":"sha256:89d265ff4ef0c456a193b1be8ffd39ff7af01420d5b38139025377585406fe24","observation_id":"9d1c53ec-8dcd-4c79-b0da-a4beeb1e5bed","resolution":{"observed_at":"2026-05-11T07:20:57.908357Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2301.04465","last_updated":"2023-05-26T15:14:45Z","snapshot_observed_at":"2026-08-13T04:49:42.166259Z","submitted_at":"2023-01-11T13:48:37Z","title":"Co-training with High-Confidence Pseudo Labels for Semi-supervised Medical Image Segmentation","version":3},"cited_work":{"arxiv_id":"2301.04465","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2301.04465","snapshot_observed_at":"2026-07-03T20:28:55.438105Z","title":"Co-training with high- confidence pseudo labels for semi-supervised medical image segmentation","venue":null,"work_id":"0fc4f4c3-a89f-4fd5-8cd8-d41e1ae5a4d3","year":2023},"citing_paper":{"arxiv_id":"2606.17958","last_updated":"2026-06-16T14:10:19Z","snapshot_observed_at":"2026-08-06T14:31:41.908061Z","submitted_at":"2026-06-16T14:10:19Z","title":"Beyond Visual Cues: CoT-Enhanced Reasoning for Semi-supervised Medical Image Segmentation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-06-27T01:22:05.399683Z"},"links":{"cited_paper":"/paper/2301.04465","citing_paper":"/paper/2606.17958"},"observation_digest":"sha256:7bb1da3c6b81bfaa133bc054c16fd0ab326bf5f5e38dbb1880366557be6fbfe4","observation_id":"3b5e44e5-5183-4670-860b-92a43370a032","resolution":{"observed_at":"2026-07-03T20:28:55.439387Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2301.04465/citation-record","integrity":"/paper/2301.04465/integrity","json":"/paper/2301.04465/citation-record.json","paper":"/paper/2301.04465"},"outbound":[],"paper":{"arxiv_id":"2301.04465","last_updated":"2023-05-26T15:14:45Z","latest_version":3,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-13T04:49:42.166259Z","submitted_at":"2023-01-11T13:48:37Z","title":"Co-training with High-Confidence Pseudo Labels for Semi-supervised 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":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2301.04465."}