{"as_of":"2026-08-19T10:33:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:2f110b01507b59ff8def7f26bfc5db0137f7c34c491647124e3fc5f4e6f72d63","coverage":[{"denominator":32,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":32,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T19:33:05.197740Z","state":"measured"},{"denominator":32,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":32,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-19T06:32:44.657259+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2412.06610/citation-record","integrity":"/paper/2412.06610/integrity","json":"/paper/2412.06610/citation-record.json","paper":"/paper/2412.06610"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:33:05.605484Z","title":"In: Andrearczyk, V., Oreiller, V., Hatt, M., Depeursinge, A","venue":null,"work_id":"bd8dae56-806a-466f-9ec5-d60074f796a4","year":2023},"citing_paper":{"arxiv_id":"2412.06610","last_updated":"2024-12-09T16:01:54Z","snapshot_observed_at":"2026-08-15T12:50:13.515311Z","submitted_at":"2024-12-09T16:01:54Z","title":"MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T19:33:05.099595Z"},"links":{"citing_paper":"/paper/2412.06610"},"observation_digest":"sha256:ccf7755c9a505fe7eb2c92b201a0eefe9a636fcb583e3352aec4da1505c3bfe6","observation_id":"987b96ec-5baa-4c60-9521-be2249eae88f","resolution":{"observed_at":"2026-08-11T19:33:05.608644Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:33:05.102640Z","title":"In: 3D head and neck tumor segmentation in PET/CT challenge, pp","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.06610","last_updated":"2024-12-09T16:01:54Z","snapshot_observed_at":"2026-08-15T12:50:13.515311Z","submitted_at":"2024-12-09T16:01:54Z","title":"MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T19:33:05.102640Z"},"links":{"citing_paper":"/paper/2412.06610"},"observation_digest":"sha256:d30c14e0f5dd4ead647595e1f2b3e5f1f08187c763e0d3e9346674c201b21ae9","observation_id":"aac6bc04-1854-4ef3-b4cd-8869e1bb5b07","resolution":{"observed_at":"2026-08-11T19:33:05.102640Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:33:05.105468Z","title":"In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.06610","last_updated":"2024-12-09T16:01:54Z","snapshot_observed_at":"2026-08-15T12:50:13.515311Z","submitted_at":"2024-12-09T16:01:54Z","title":"MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T19:33:05.105468Z"},"links":{"citing_paper":"/paper/2412.06610"},"observation_digest":"sha256:d3684cf0e0bd8409d15ba71f7e76313ebdeebd640873afa38a24c8e4a5f4e20c","observation_id":"451092b3-aed0-463c-9868-1fb76fe40811","resolution":{"observed_at":"2026-08-11T19:33:05.105468Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:33:05.590945Z","title":"In: Head and Neck Tumor Segmentation: First Challenge, HECKTOR 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings 1","venue":null,"work_id":"66598aa5-7c79-437d-b607-91a66287439a","year":2021},"citing_paper":{"arxiv_id":"2412.06610","last_updated":"2024-12-09T16:01:54Z","snapshot_observed_at":"2026-08-15T12:50:13.515311Z","submitted_at":"2024-12-09T16:01:54Z","title":"MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T19:33:05.108770Z"},"links":{"citing_paper":"/paper/2412.06610"},"observation_digest":"sha256:7b46b56b5cadf2de3239a7c7c1a6c5d5809561762bdb23ff68f4ce0a6ab1a2cd","observation_id":"6e744be4-cacc-4393-8bbf-12dd188de27b","resolution":{"observed_at":"2026-08-11T19:33:05.593836Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.3390/bioengineering10020181","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:33:05.261862Z","title":"Bio- engineering 10(2) (2023)","venue":null,"work_id":"14fa2001-fbd1-4a50-9584-29cdf67a210f","year":2023},"citing_paper":{"arxiv_id":"2412.06610","last_updated":"2024-12-09T16:01:54Z","snapshot_observed_at":"2026-08-15T12:50:13.515311Z","submitted_at":"2024-12-09T16:01:54Z","title":"MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T19:33:05.113643Z"},"links":{"citing_paper":"/paper/2412.06610"},"observation_digest":"sha256:d85a5834233242447da7b4f1790aad841fbf20ce97d2e4159d502c6eec82c5cb","observation_id":"9a490d2d-a13c-42f2-a697-f123a07d9145","resolution":{"observed_at":"2026-08-11T19:33:05.265250Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:33:05.580812Z","title":"Radiation Oncology15, 1–9 (2020)","venue":null,"work_id":"353b2fb2-67ca-4211-a93b-513fcd21699d","year":2020},"citing_paper":{"arxiv_id":"2412.06610","last_updated":"2024-12-09T16:01:54Z","snapshot_observed_at":"2026-08-15T12:50:13.515311Z","submitted_at":"2024-12-09T16:01:54Z","title":"MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T19:33:05.116829Z"},"links":{"citing_paper":"/paper/2412.06610"},"observation_digest":"sha256:fea730f911b418faaea047df715375dfcc6938e346ac17b856e5aad25d98a18b","observation_id":"fbeaf8e8-9d29-405b-bb4f-40734f337c9b","resolution":{"observed_at":"2026-08-11T19:33:05.584471Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:33:05.570533Z","title":"In: 3D Head and Neck Tumor Segmentation in PET/CT Challenge, pp","venue":null,"work_id":"df1def99-7e7f-4ff2-88ce-9a9dcd148308","year":2022},"citing_paper":{"arxiv_id":"2412.06610","last_updated":"2024-12-09T16:01:54Z","snapshot_observed_at":"2026-08-15T12:50:13.515311Z","submitted_at":"2024-12-09T16:01:54Z","title":"MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T19:33:05.120201Z"},"links":{"citing_paper":"/paper/2412.06610"},"observation_digest":"sha256:b2284d90c69e1c1c4625c068dec08f6346f5c0959e19bdc9ea412f2fc8f4b99e","observation_id":"03bdcba9-b364-49f8-8e36-01f5d86f240d","resolution":{"observed_at":"2026-08-11T19:33:05.574209Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:33:05.122817Z","title":"In: Medical Image ComputingandComputer-AssistedIntervention–MICCAI2016:19thInternational Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part II 19","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.06610","last_updated":"2024-12-09T16:01:54Z","snapshot_observed_at":"2026-08-15T12:50:13.515311Z","submitted_at":"2024-12-09T16:01:54Z","title":"MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T19:33:05.122817Z"},"links":{"citing_paper":"/paper/2412.06610"},"observation_digest":"sha256:c41089515f76b4152e079d73af54f55b56ec6c881f4d3e3dc6986ec522316b1d","observation_id":"8710618e-5ce8-4622-9920-66ba25c6cef5","resolution":{"observed_at":"2026-08-11T19:33:05.122817Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2003.06505","last_updated":"2020-03-13T23:10:39Z","snapshot_observed_at":"2026-08-13T20:52:10.714829Z","submitted_at":"2020-03-13T23:10:39Z","title":"AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2003.06505","snapshot_observed_at":"2026-08-11T19:33:05.125602Z","title":"arXiv preprint arXiv:2003.06505 (2020)","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.06610","last_updated":"2024-12-09T16:01:54Z","snapshot_observed_at":"2026-08-15T12:50:13.515311Z","submitted_at":"2024-12-09T16:01:54Z","title":"MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T19:33:05.125602Z"},"links":{"cited_paper":"/paper/2003.06505","citing_paper":"/paper/2412.06610"},"observation_digest":"sha256:541e7523f6b0b331112acfdd59b386ab93b4ae19fb317acb6ff20b27500a3cd6","observation_id":"6cf1ec37-220e-4e06-bf83-6fba71f02fe3","resolution":{"observed_at":"2026-08-11T19:33:05.125602Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:33:05.555426Z","title":"In: Head and Neck Tumor Segmentation: First Challenge, HECKTOR 2020, Held in Con- junction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings 1","venue":null,"work_id":"fb9ef3f2-6437-4b8e-b3c6-5cb1a8532bcb","year":2021},"citing_paper":{"arxiv_id":"2412.06610","last_updated":"2024-12-09T16:01:54Z","snapshot_observed_at":"2026-08-15T12:50:13.515311Z","submitted_at":"2024-12-09T16:01:54Z","title":"MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T19:33:05.128675Z"},"links":{"citing_paper":"/paper/2412.06610"},"observation_digest":"sha256:4e425b2307016c0576a397ee85911324b69d9b0a8c0fdedf6f008e1e3af2406a","observation_id":"14d56e47-523c-4168-93f3-44d4dc077c62","resolution":{"observed_at":"2026-08-11T19:33:05.559235Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:33:05.131367Z","title":"In: International MICCAI brainlesion workshop","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.06610","last_updated":"2024-12-09T16:01:54Z","snapshot_observed_at":"2026-08-15T12:50:13.515311Z","submitted_at":"2024-12-09T16:01:54Z","title":"MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T19:33:05.131367Z"},"links":{"citing_paper":"/paper/2412.06610"},"observation_digest":"sha256:6abe676364be84ebd406b48423dfe7f7a4dee5d49c8feb9c5cf87c990e898e92","observation_id":"f83e7928-5bf1-4866-bd1e-8d2370780ec8","resolution":{"observed_at":"2026-08-11T19:33:05.131367Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:33:05.539607Z","title":"Contrast media & molecular imaging2018(1), 8923028 (2018)","venue":null,"work_id":"1fa8649c-2c71-45f7-b529-1240846bf478","year":2018},"citing_paper":{"arxiv_id":"2412.06610","last_updated":"2024-12-09T16:01:54Z","snapshot_observed_at":"2026-08-15T12:50:13.515311Z","submitted_at":"2024-12-09T16:01:54Z","title":"MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T19:33:05.134443Z"},"links":{"citing_paper":"/paper/2412.06610"},"observation_digest":"sha256:5cbf710cb07dad29f643e552d4bd5cc0a269e9241cd0e9e8337ce18d643876a1","observation_id":"6ba1053a-75f9-4027-8cce-1d55f37e897d","resolution":{"observed_at":"2026-08-11T19:33:05.543201Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:33:05.529299Z","title":"Medical physics44(2), 547–557 (2017)","venue":null,"work_id":"6a591016-d9f7-4c10-9bad-49e8b9bb9b17","year":2017},"citing_paper":{"arxiv_id":"2412.06610","last_updated":"2024-12-09T16:01:54Z","snapshot_observed_at":"2026-08-15T12:50:13.515311Z","submitted_at":"2024-12-09T16:01:54Z","title":"MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T19:33:05.137772Z"},"links":{"citing_paper":"/paper/2412.06610"},"observation_digest":"sha256:b9c809c0b150f27f618228cadafed083d79b1ea13d6bc8ada984824704152d1d","observation_id":"1dcf55db-19c0-4a72-9953-35bfff0ba222","resolution":{"observed_at":"2026-08-11T19:33:05.532686Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1809.10486","last_updated":"2018-09-27T12:25:52Z","snapshot_observed_at":"2026-08-14T18:22:47.133660Z","submitted_at":"2018-09-27T12:25:52Z","title":"nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1809.10486","snapshot_observed_at":"2026-08-11T19:33:05.140831Z","title":"org/abs/1809.10486","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.06610","last_updated":"2024-12-09T16:01:54Z","snapshot_observed_at":"2026-08-15T12:50:13.515311Z","submitted_at":"2024-12-09T16:01:54Z","title":"MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T19:33:05.140831Z"},"links":{"cited_paper":"/paper/1809.10486","citing_paper":"/paper/2412.06610"},"observation_digest":"sha256:f86b710eb8b433d4d5d7a35741ce0b17ba8ab24f4544f778a5cf0f6cc0a34bf7","observation_id":"64883e9c-4ff0-4539-9c94-a98bb934dd9b","resolution":{"observed_at":"2026-08-11T19:33:05.140831Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.09556","last_updated":"2024-07-25T14:42:11Z","snapshot_observed_at":"2026-08-18T02:28:44.071406Z","submitted_at":"2024-04-15T08:19:08Z","title":"nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.09556","snapshot_observed_at":"2026-08-11T19:33:05.144510Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.06610","last_updated":"2024-12-09T16:01:54Z","snapshot_observed_at":"2026-08-15T12:50:13.515311Z","submitted_at":"2024-12-09T16:01:54Z","title":"MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T19:33:05.144510Z"},"links":{"cited_paper":"/paper/2404.09556","citing_paper":"/paper/2412.06610"},"observation_digest":"sha256:1dbe190746b6f11684f7004ba76656b96e1db81ef478038c4c678844e71ded7a","observation_id":"903fb60a-3bdd-4fcf-a9cc-146e05e8d608","resolution":{"observed_at":"2026-08-11T19:33:05.144510Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:33:05.147850Z","title":"In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.06610","last_updated":"2024-12-09T16:01:54Z","snapshot_observed_at":"2026-08-15T12:50:13.515311Z","submitted_at":"2024-12-09T16:01:54Z","title":"MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T19:33:05.147850Z"},"links":{"citing_paper":"/paper/2412.06610"},"observation_digest":"sha256:22e0d7627668f79a4864ffd867cc33053020c07eb9ced281556529270c4ceac2","observation_id":"bf9e6290-89d9-44df-ae4a-ea3abccdc53d","resolution":{"observed_at":"2026-08-11T19:33:05.147850Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:33:05.151251Z","title":"In: Proceedings of the IEEE/CVF International Conference on Computer Vision","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.06610","last_updated":"2024-12-09T16:01:54Z","snapshot_observed_at":"2026-08-15T12:50:13.515311Z","submitted_at":"2024-12-09T16:01:54Z","title":"MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T19:33:05.151251Z"},"links":{"citing_paper":"/paper/2412.06610"},"observation_digest":"sha256:13afd256639b3fd1714f519a6b5ac08afb59f95ba89d0728cb043b979fc909d3","observation_id":"aa1bad81-040b-4016-90ae-f12d1a9f045c","resolution":{"observed_at":"2026-08-11T19:33:05.151251Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:33:05.154697Z","title":"Nature Communications15(1), 654 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.06610","last_updated":"2024-12-09T16:01:54Z","snapshot_observed_at":"2026-08-15T12:50:13.515311Z","submitted_at":"2024-12-09T16:01:54Z","title":"MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T19:33:05.154697Z"},"links":{"citing_paper":"/paper/2412.06610"},"observation_digest":"sha256:23716de6fdc27d7a939aad13e14a47c2390e3ef39b989f7ea8ab97e413d05871","observation_id":"05dfbada-5c46-412e-9c4e-1718258207bc","resolution":{"observed_at":"2026-08-11T19:33:05.154697Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:33:05.157914Z","title":"In: 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.06610","last_updated":"2024-12-09T16:01:54Z","snapshot_observed_at":"2026-08-15T12:50:13.515311Z","submitted_at":"2024-12-09T16:01:54Z","title":"MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T19:33:05.157914Z"},"links":{"citing_paper":"/paper/2412.06610"},"observation_digest":"sha256:886887fbd83d55175db7c45005d9f7aeace99a11226a1b0390869e1a1d078254","observation_id":"e6c89ef9-7026-430a-90f6-7a2bad775977","resolution":{"observed_at":"2026-08-11T19:33:05.157914Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1908.00841","last_updated":"2019-08-02T13:24:16Z","snapshot_observed_at":"2026-08-14T15:28:39.990623Z","submitted_at":"2019-08-02T13:24:16Z","title":"Deep learning for automatic tumour segmentation in PET/CT images of patients with head and neck cancers","version":1},"cited_work":{"arxiv_id":"1908.00841","doi":null,"metadata_source":"pith","pith_arxiv_id":"1908.00841","snapshot_observed_at":"2026-08-11T19:33:05.372434Z","title":"Deep learning for automatic tumour segmentation in PET/CT images of patients with head and neck cancers","venue":"eess.IV","work_id":"0f2d6c10-f925-4d65-af4f-53fb53e45be1","year":2019},"citing_paper":{"arxiv_id":"2412.06610","last_updated":"2024-12-09T16:01:54Z","snapshot_observed_at":"2026-08-15T12:50:13.515311Z","submitted_at":"2024-12-09T16:01:54Z","title":"MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T19:33:05.161301Z"},"links":{"cited_paper":"/paper/1908.00841","citing_paper":"/paper/2412.06610"},"observation_digest":"sha256:736b0db9bb1d64dc8b772142b971cc695f31e6061e13528b0c2f484bb3467abd","observation_id":"22f7f03a-1f65-4d43-a48e-491121e7b1be","resolution":{"observed_at":"2026-08-11T19:33:05.377609Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.5281/zenodo.14193311","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:33:05.247985Z","title":"https:// doi.org/10.5281/zenodo.14193311","venue":null,"work_id":"4f1392ab-3113-4ae2-936a-dfea3afc9bdc","year":2024},"citing_paper":{"arxiv_id":"2412.06610","last_updated":"2024-12-09T16:01:54Z","snapshot_observed_at":"2026-08-15T12:50:13.515311Z","submitted_at":"2024-12-09T16:01:54Z","title":"MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T19:33:05.165431Z"},"links":{"citing_paper":"/paper/2412.06610"},"observation_digest":"sha256:e5b017b72b2727e67086490a191eb7933141a8722b24c8ec2621111e10b3992f","observation_id":"0e2d4df2-0b2b-4350-98f4-2b5aa0959318","resolution":{"observed_at":"2026-08-11T19:33:05.251049Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.5281/zenodo.12542217","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:33:05.239028Z","title":"https://doi.org/10.5281/zenodo.12542217","venue":null,"work_id":"fdccbfa1-4547-4184-80cc-fae6a701c8a7","year":2024},"citing_paper":{"arxiv_id":"2412.06610","last_updated":"2024-12-09T16:01:54Z","snapshot_observed_at":"2026-08-15T12:50:13.515311Z","submitted_at":"2024-12-09T16:01:54Z","title":"MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T19:33:05.168599Z"},"links":{"citing_paper":"/paper/2412.06610"},"observation_digest":"sha256:8a97bb50bfb40a4da7b0554db56b16a41848afb7fcb0e7dca48fcc972a8bb318","observation_id":"c09d57a2-6a46-41ba-a1bd-965006972a50","resolution":{"observed_at":"2026-08-11T19:33:05.242502Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:33:05.505218Z","title":"In: Andrearczyk, V., Oreiller, V., Hatt, M., De- peursinge, A","venue":null,"work_id":"9e83fc11-5d18-47e8-8237-1e3c5033174d","year":2022},"citing_paper":{"arxiv_id":"2412.06610","last_updated":"2024-12-09T16:01:54Z","snapshot_observed_at":"2026-08-15T12:50:13.515311Z","submitted_at":"2024-12-09T16:01:54Z","title":"MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T19:33:05.171371Z"},"links":{"citing_paper":"/paper/2412.06610"},"observation_digest":"sha256:4ea942db4b1f99f56de49f7b8923e3471b33de62d0d7863f7d940c272e272328","observation_id":"a08946e4-8c82-4efa-8aec-1c3d7b9e8179","resolution":{"observed_at":"2026-08-11T19:33:05.508125Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:33:05.174015Z","title":"Physics and Imaging in Radia- tion Oncology 32, 100655 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.06610","last_updated":"2024-12-09T16:01:54Z","snapshot_observed_at":"2026-08-15T12:50:13.515311Z","submitted_at":"2024-12-09T16:01:54Z","title":"MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T19:33:05.174015Z"},"links":{"citing_paper":"/paper/2412.06610"},"observation_digest":"sha256:821102cc8096d08c8924356118dafe7e581d82ddc8517b87f37b744883e91a29","observation_id":"ecdf286b-4de0-479a-944c-1dd3beb95c9c","resolution":{"observed_at":"2026-08-11T19:33:05.174015Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.17454","last_updated":"2024-02-27T12:26:45Z","snapshot_observed_at":"2026-08-16T14:14:55.877395Z","submitted_at":"2024-02-27T12:26:45Z","title":"Segment anything model for head and neck tumor segmentation with CT, PET and MRI multi-modality images","version":1},"cited_work":{"arxiv_id":"2402.17454","doi":null,"metadata_source":"pith","pith_arxiv_id":"2402.17454","snapshot_observed_at":"2026-08-11T19:33:05.290743Z","title":"Segment anything model for head and neck tumor segmentation with CT, PET and MRI multi-modality images","venue":"physics.med-ph","work_id":"e4062672-0f2d-4dfb-9394-24e666296bb8","year":2024},"citing_paper":{"arxiv_id":"2412.06610","last_updated":"2024-12-09T16:01:54Z","snapshot_observed_at":"2026-08-15T12:50:13.515311Z","submitted_at":"2024-12-09T16:01:54Z","title":"MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T19:33:05.176716Z"},"links":{"cited_paper":"/paper/2402.17454","citing_paper":"/paper/2412.06610"},"observation_digest":"sha256:52548b5f310c6526095adfb396ac4196dbd2e7600ce263040823714f43a147fc","observation_id":"2d716a75-fdc8-4216-8191-82d37bb61e1a","resolution":{"observed_at":"2026-08-11T19:33:05.294539Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1505.04597","last_updated":"2015-05-18T11:28:37Z","snapshot_observed_at":"2026-08-15T21:30:31.645090Z","submitted_at":"2015-05-18T11:28:37Z","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1505.04597","snapshot_observed_at":"2026-08-11T19:33:05.179840Z","title":"CoRRabs/1505.04597 (2015), http://arxiv.org/abs/ 1505.04597","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2412.06610","last_updated":"2024-12-09T16:01:54Z","snapshot_observed_at":"2026-08-15T12:50:13.515311Z","submitted_at":"2024-12-09T16:01:54Z","title":"MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T19:33:05.179840Z"},"links":{"cited_paper":"/paper/1505.04597","citing_paper":"/paper/2412.06610"},"observation_digest":"sha256:23eebe90612bbcdb2b5898b522e0ae1440997a9e4dc0a308368908ddb71259c6","observation_id":"16c57470-d01b-4357-b7b5-af371399ff21","resolution":{"observed_at":"2026-08-11T19:33:05.179840Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:33:05.183238Z","title":"CA: a cancer journal for clinicians 71(3), 209–249 (2021)","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.06610","last_updated":"2024-12-09T16:01:54Z","snapshot_observed_at":"2026-08-15T12:50:13.515311Z","submitted_at":"2024-12-09T16:01:54Z","title":"MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T19:33:05.183238Z"},"links":{"citing_paper":"/paper/2412.06610"},"observation_digest":"sha256:65ae9dc42d612ada61d6899431af9888819725037282573290d2dc4f68326212","observation_id":"c311d396-a792-4ba4-97b7-8dcf2b4190d7","resolution":{"observed_at":"2026-08-11T19:33:05.183238Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.16233","last_updated":"2024-04-30T21:09:27Z","snapshot_observed_at":"2026-08-17T16:52:03.076906Z","submitted_at":"2024-04-24T22:28:12Z","title":"AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.16233","snapshot_observed_at":"2026-08-11T19:33:05.186187Z","title":"arXiv preprint arXiv:2404.16233 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.06610","last_updated":"2024-12-09T16:01:54Z","snapshot_observed_at":"2026-08-15T12:50:13.515311Z","submitted_at":"2024-12-09T16:01:54Z","title":"MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-11T19:33:05.186187Z"},"links":{"cited_paper":"/paper/2404.16233","citing_paper":"/paper/2412.06610"},"observation_digest":"sha256:131cdd2bbe073fb81e57f87745c44164ad4729501603d218a506a4a03fe9dbb0","observation_id":"6a32847e-2798-4191-a056-511ed1648157","resolution":{"observed_at":"2026-08-11T19:33:05.186187Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:33:05.189555Z","title":"Radiotherapy MRI-based HNC segmentation by 15-fold cross-validation ensemble 13 and Oncology 112(3), 317–320 (Sep 2014).https://doi.org/10.1016/j.radonc","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2412.06610","last_updated":"2024-12-09T16:01:54Z","snapshot_observed_at":"2026-08-15T12:50:13.515311Z","submitted_at":"2024-12-09T16:01:54Z","title":"MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-11T19:33:05.189555Z"},"links":{"citing_paper":"/paper/2412.06610"},"observation_digest":"sha256:0d236caca156d6dbe0548d8872a1f7856d084587e8b00e6bca83e89a6983f85b","observation_id":"3467157a-389c-44c0-8d45-4311c4e390a5","resolution":{"observed_at":"2026-08-11T19:33:05.189555Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.ctro.2021.10.003","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:33:05.222254Z","title":"Clinical and Translational Radi- ationOncology 32,6–14(2022)","venue":null,"work_id":"7a4c5ea4-414a-4cae-b715-f5ada7e3ed2f","year":2022},"citing_paper":{"arxiv_id":"2412.06610","last_updated":"2024-12-09T16:01:54Z","snapshot_observed_at":"2026-08-15T12:50:13.515311Z","submitted_at":"2024-12-09T16:01:54Z","title":"MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-11T19:33:05.192389Z"},"links":{"citing_paper":"/paper/2412.06610"},"observation_digest":"sha256:b460880965aaac44d37b780b8fecda4906579f13dda64750a13e731b62659d72","observation_id":"d5c187ad-dbb6-4b5f-a761-d263faa9ce80","resolution":{"observed_at":"2026-08-11T19:33:05.227281Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:33:05.491033Z","title":"In: 3D Head and Neck Tumor Segmentation in PET/CT Challenge, pp","venue":null,"work_id":"24d91349-0bc6-4492-99f6-dc3200e2fa83","year":2021},"citing_paper":{"arxiv_id":"2412.06610","last_updated":"2024-12-09T16:01:54Z","snapshot_observed_at":"2026-08-15T12:50:13.515311Z","submitted_at":"2024-12-09T16:01:54Z","title":"MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-11T19:33:05.195314Z"},"links":{"citing_paper":"/paper/2412.06610"},"observation_digest":"sha256:c778870568e0c352ec62c3288a79c949858aac6c3263bd585f7c8c150ee5d226","observation_id":"7cffb491-cdba-4726-92e2-fa76ef847efa","resolution":{"observed_at":"2026-08-11T19:33:05.494213Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:33:05.482165Z","title":"In: Head and Neck Tumor Segmentation: First Challenge, HECKTOR 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings 1","venue":null,"work_id":"4ecf9898-600c-4f90-9adc-e5a786a0e829","year":2021},"citing_paper":{"arxiv_id":"2412.06610","last_updated":"2024-12-09T16:01:54Z","snapshot_observed_at":"2026-08-15T12:50:13.515311Z","submitted_at":"2024-12-09T16:01:54Z","title":"MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-11T19:33:05.197740Z"},"links":{"citing_paper":"/paper/2412.06610"},"observation_digest":"sha256:38fafe6c900b8c9d900138ac8c737990591ebf8990c6ea0af3d455e6461f95aa","observation_id":"d05cb0d9-32ee-40da-94ab-6ac7289154ef","resolution":{"observed_at":"2026-08-11T19:33:05.485342Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2412.06610","last_updated":"2024-12-09T16:01:54Z","latest_version":1,"primary_category":"physics.med-ph","snapshot_observed_at":"2026-08-15T12:50:13.515311Z","submitted_at":"2024-12-09T16:01:54Z","title":"MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble"},"reference_resolution":{"displayed":32,"state_counts":{"malformed_identifier":2,"metadata_mismatch":2,"parse_uncertain":0,"unresolved":14,"verified_exact":4,"verified_fuzzy":10},"total_outbound_references":32},"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":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2412.06610."}