{"as_of":"2026-08-15T11:22:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:728c10d9fa96d69b93742f6f470a16d3c48d7b1490379bcf777ce42a587b00ec","coverage":[{"denominator":19,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":19,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T05:10:00.566141Z","state":"measured"},{"denominator":19,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":19,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+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/1909.06012/citation-record","integrity":"/paper/1909.06012/integrity","json":"/paper/1909.06012/citation-record.json","paper":"/paper/1909.06012"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T05:10:00.784392Z","title":"In: Proc","venue":null,"work_id":"ed475e66-1cad-4a7e-9344-f8eaf00a543b","year":2018},"citing_paper":{"arxiv_id":"1909.06012","last_updated":"2019-09-04T15:03:08Z","snapshot_observed_at":"2026-08-14T05:04:03.908012Z","submitted_at":"2019-09-04T15:03:08Z","title":"3D U$^2$-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-14T05:10:00.495472Z"},"links":{"citing_paper":"/paper/1909.06012"},"observation_digest":"sha256:4dc14daaa94e980cd9f7081fc850d3a75739272439fc49f5a7429d409298415f","observation_id":"4c0ec1aa-df86-4986-8ec6-d856288a6950","resolution":{"observed_at":"2026-08-14T05:10:00.787775Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1701.07275","last_updated":"2017-01-25T12:07:15Z","snapshot_observed_at":"2026-08-14T22:55:01.399977Z","submitted_at":"2017-01-25T12:07:15Z","title":"Universal representations:The missing link between faces, text, planktons, and cat breeds","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1701.07275","snapshot_observed_at":"2026-08-14T05:10:00.500400Z","title":"arXiv:1701.07275 (2017)","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1909.06012","last_updated":"2019-09-04T15:03:08Z","snapshot_observed_at":"2026-08-14T05:04:03.908012Z","submitted_at":"2019-09-04T15:03:08Z","title":"3D U$^2$-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-14T05:10:00.500400Z"},"links":{"cited_paper":"/paper/1701.07275","citing_paper":"/paper/1909.06012"},"observation_digest":"sha256:41715a81fdcc8bfc54d1d76c74da9e5892e0eb2407be8265024992848c2ddf18","observation_id":"72a949f3-6c37-4980-9c8f-06f3d7e7c907","resolution":{"observed_at":"2026-08-14T05:10:00.500400Z","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":"2026-08-14T05:10:00.775018Z","title":"In: Proc","venue":null,"work_id":"186ae980-500f-421b-a48e-20a9f4874f39","year":2017},"citing_paper":{"arxiv_id":"1909.06012","last_updated":"2019-09-04T15:03:08Z","snapshot_observed_at":"2026-08-14T05:04:03.908012Z","submitted_at":"2019-09-04T15:03:08Z","title":"3D U$^2$-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-14T05:10:00.504723Z"},"links":{"citing_paper":"/paper/1909.06012"},"observation_digest":"sha256:cbfba6efc47bd1f16c705871173820987dbfeb510d5d7dd9862b466b99a63e9d","observation_id":"fd88a915-11a7-4811-8130-bf21ce7ee862","resolution":{"observed_at":"2026-08-14T05:10:00.778612Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-14T05:10:00.764755Z","title":"In: Proc","venue":null,"work_id":"e186235f-2905-41a9-8b7c-18a6828e036b","year":2016},"citing_paper":{"arxiv_id":"1909.06012","last_updated":"2019-09-04T15:03:08Z","snapshot_observed_at":"2026-08-14T05:04:03.908012Z","submitted_at":"2019-09-04T15:03:08Z","title":"3D U$^2$-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-14T05:10:00.508170Z"},"links":{"citing_paper":"/paper/1909.06012"},"observation_digest":"sha256:c1b3c074caff864e4c1383c21f4a5c63b3e671672077fdd4b36181826fa61aa8","observation_id":"dd490a9f-af62-40ad-bcb9-ae333d5233a2","resolution":{"observed_at":"2026-08-14T05:10:00.768663Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1902.00927","last_updated":"2019-02-19T18:45:10Z","snapshot_observed_at":"2026-08-14T17:21:42.394746Z","submitted_at":"2019-02-03T16:58:19Z","title":"Depthwise Convolution is All You Need for Learning Multiple Visual Domains","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1902.00927","snapshot_observed_at":"2026-08-14T05:10:00.513041Z","title":"arXiv:1902.00927 (2019)","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"1909.06012","last_updated":"2019-09-04T15:03:08Z","snapshot_observed_at":"2026-08-14T05:04:03.908012Z","submitted_at":"2019-09-04T15:03:08Z","title":"3D U$^2$-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-14T05:10:00.513041Z"},"links":{"cited_paper":"/paper/1902.00927","citing_paper":"/paper/1909.06012"},"observation_digest":"sha256:89193bd0ade2ed5c0b302a6bec243b8171eb399dec43ff0df09fe29f22b01914","observation_id":"17db0031-c8e3-4276-a83a-032bdf4f4405","resolution":{"observed_at":"2026-08-14T05:10:00.513041Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"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":"2026-08-14T05:10:00.517106Z","title":"arXiv:1809.10486 (2018)","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1909.06012","last_updated":"2019-09-04T15:03:08Z","snapshot_observed_at":"2026-08-14T05:04:03.908012Z","submitted_at":"2019-09-04T15:03:08Z","title":"3D U$^2$-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-14T05:10:00.517106Z"},"links":{"cited_paper":"/paper/1809.10486","citing_paper":"/paper/1909.06012"},"observation_digest":"sha256:4b18a56db90ea5889c49036f92eda21eb0b018232e9f4187a14c0a87063c3989","observation_id":"f7777bad-04f3-4f98-9063-8e91b79b7f29","resolution":{"observed_at":"2026-08-14T05:10:00.517106Z","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":"2026-08-14T05:10:00.753755Z","title":"In: Proc","venue":null,"work_id":"92a50455-8b2d-4e0e-a976-20bd2d2f3c4d","year":2018},"citing_paper":{"arxiv_id":"1909.06012","last_updated":"2019-09-04T15:03:08Z","snapshot_observed_at":"2026-08-14T05:04:03.908012Z","submitted_at":"2019-09-04T15:03:08Z","title":"3D U$^2$-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-14T05:10:00.521194Z"},"links":{"citing_paper":"/paper/1909.06012"},"observation_digest":"sha256:509e6813b0c3a78190208eb3fd95d11a8c7265c1c5a74ddfec049f40044a3c9b","observation_id":"2a782d0a-8890-4a2c-8ced-d267e46fb2c5","resolution":{"observed_at":"2026-08-14T05:10:00.758184Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1701.03056","last_updated":"2017-07-25T10:22:17Z","snapshot_observed_at":"2026-08-14T21:21:36.555762Z","submitted_at":"2017-01-11T16:50:30Z","title":"CNN-based Segmentation of Medical Imaging Data","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1701.03056","snapshot_observed_at":"2026-08-14T05:10:00.524527Z","title":"arXiv:1701.03056 (2017)","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1909.06012","last_updated":"2019-09-04T15:03:08Z","snapshot_observed_at":"2026-08-14T05:04:03.908012Z","submitted_at":"2019-09-04T15:03:08Z","title":"3D U$^2$-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-14T05:10:00.524527Z"},"links":{"cited_paper":"/paper/1701.03056","citing_paper":"/paper/1909.06012"},"observation_digest":"sha256:54c48114c736a2c74e5d8167bda00a7e092fe3f729c36d7ab2bcde68b7c78dab","observation_id":"4403ddea-80de-4cf0-ab0c-dac2e159daf1","resolution":{"observed_at":"2026-08-14T05:10:00.524527Z","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":"2026-08-14T05:10:00.743813Z","title":"In: Proc","venue":null,"work_id":"0c7f2cd6-5dbb-4d87-92e4-12dafcfd6620","year":2013},"citing_paper":{"arxiv_id":"1909.06012","last_updated":"2019-09-04T15:03:08Z","snapshot_observed_at":"2026-08-14T05:04:03.908012Z","submitted_at":"2019-09-04T15:03:08Z","title":"3D U$^2$-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-14T05:10:00.528396Z"},"links":{"citing_paper":"/paper/1909.06012"},"observation_digest":"sha256:2d41c6255857a3ec666b1449d3b1c85c70c3def9fce6488cf27b6a5e3ea6cf26","observation_id":"74c2913f-a753-4228-9ebc-19bea72c80e9","resolution":{"observed_at":"2026-08-14T05:10:00.747265Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-14T05:10:00.734003Z","title":"In: Proc","venue":null,"work_id":"c9251add-6758-41b2-a5e5-e8178da17ec7","year":2017},"citing_paper":{"arxiv_id":"1909.06012","last_updated":"2019-09-04T15:03:08Z","snapshot_observed_at":"2026-08-14T05:04:03.908012Z","submitted_at":"2019-09-04T15:03:08Z","title":"3D U$^2$-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-14T05:10:00.531782Z"},"links":{"citing_paper":"/paper/1909.06012"},"observation_digest":"sha256:9a2c63c41ad75665814df2c4c1f9f4a81cf1d97601f8a5a663ee4e893c443cf8","observation_id":"286b6e26-48d7-4a52-afef-af1da6d5f72e","resolution":{"observed_at":"2026-08-14T05:10:00.737357Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-14T05:10:00.723529Z","title":"In: Proc","venue":null,"work_id":"666619de-e2ec-42d1-aef9-62acaf674638","year":2016},"citing_paper":{"arxiv_id":"1909.06012","last_updated":"2019-09-04T15:03:08Z","snapshot_observed_at":"2026-08-14T05:04:03.908012Z","submitted_at":"2019-09-04T15:03:08Z","title":"3D U$^2$-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-14T05:10:00.535310Z"},"links":{"citing_paper":"/paper/1909.06012"},"observation_digest":"sha256:af6013c050285eed0db7558a6e2ed17058e431a03d908510db9e5b4eee3e1972","observation_id":"ab31930e-e64f-45c8-acf4-94174dd3f814","resolution":{"observed_at":"2026-08-14T05:10:00.727426Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-14T05:10:00.712169Z","title":"In: Proc","venue":null,"work_id":"5c9f29de-efb7-4a6f-b855-21f21b32aac4","year":2016},"citing_paper":{"arxiv_id":"1909.06012","last_updated":"2019-09-04T15:03:08Z","snapshot_observed_at":"2026-08-14T05:04:03.908012Z","submitted_at":"2019-09-04T15:03:08Z","title":"3D U$^2$-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-14T05:10:00.538799Z"},"links":{"citing_paper":"/paper/1909.06012"},"observation_digest":"sha256:57a80ebbf3d3c29c8f17a59a757aa77747f3fb3e74ecbb2d1e3818d8d6e09ab6","observation_id":"382856c0-684b-4ee1-be1c-c2fefa7aa2d8","resolution":{"observed_at":"2026-08-14T05:10:00.716024Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-14T05:10:00.701318Z","title":"In: Proc","venue":null,"work_id":"a42494d4-15cd-45bc-81d4-1f06392d6583","year":2017},"citing_paper":{"arxiv_id":"1909.06012","last_updated":"2019-09-04T15:03:08Z","snapshot_observed_at":"2026-08-14T05:04:03.908012Z","submitted_at":"2019-09-04T15:03:08Z","title":"3D U$^2$-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-14T05:10:00.542704Z"},"links":{"citing_paper":"/paper/1909.06012"},"observation_digest":"sha256:b6108c749842d2735d32bbc2c97dba3091c97c710ae14781744ad7a9939f7aed","observation_id":"7b2b5411-4191-47fe-b7c2-6f6af83b696e","resolution":{"observed_at":"2026-08-14T05:10:00.705064Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-14T05:10:00.689423Z","title":"In: Proc","venue":null,"work_id":"eda9b741-8efc-4f4d-a1e3-07cf6993fb58","year":2018},"citing_paper":{"arxiv_id":"1909.06012","last_updated":"2019-09-04T15:03:08Z","snapshot_observed_at":"2026-08-14T05:04:03.908012Z","submitted_at":"2019-09-04T15:03:08Z","title":"3D U$^2$-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-14T05:10:00.547808Z"},"links":{"citing_paper":"/paper/1909.06012"},"observation_digest":"sha256:f87c6d8107b4fab6448322b4298b4f70447c81cc2e1d8d34e93008960ca6df8b","observation_id":"3be8c3a5-60f0-41a2-bfd0-0ca0cbf8ee15","resolution":{"observed_at":"2026-08-14T05:10:00.693945Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-14T05:10:00.678109Z","title":"In: Proc","venue":null,"work_id":"ad62b3bb-b31c-487b-8105-be4b2d6a49f2","year":2015},"citing_paper":{"arxiv_id":"1909.06012","last_updated":"2019-09-04T15:03:08Z","snapshot_observed_at":"2026-08-14T05:04:03.908012Z","submitted_at":"2019-09-04T15:03:08Z","title":"3D U$^2$-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-14T05:10:00.551267Z"},"links":{"citing_paper":"/paper/1909.06012"},"observation_digest":"sha256:71ef25924217810583b706f1780fb9aa4dd43e5fca2492985e9dc5eff536ecb9","observation_id":"44b0d68e-60bb-425c-91c9-63c74a789163","resolution":{"observed_at":"2026-08-14T05:10:00.682022Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-14T05:10:00.667526Z","title":"In: Proc","venue":null,"work_id":"248730a7-1a2b-4a3f-9c85-dcdc34cbc68a","year":2015},"citing_paper":{"arxiv_id":"1909.06012","last_updated":"2019-09-04T15:03:08Z","snapshot_observed_at":"2026-08-14T05:04:03.908012Z","submitted_at":"2019-09-04T15:03:08Z","title":"3D U$^2$-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-14T05:10:00.554760Z"},"links":{"citing_paper":"/paper/1909.06012"},"observation_digest":"sha256:a617624ae7dbd2de552ac578cab4af48bcedbc05f7a1219438e2b524372fb41e","observation_id":"390f332c-25cb-42f3-b572-c73ab7005ac8","resolution":{"observed_at":"2026-08-14T05:10:00.671047Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1704.06382","last_updated":"2017-04-21T03:05:15Z","snapshot_observed_at":"2026-08-14T21:05:40.389483Z","submitted_at":"2017-04-21T03:05:15Z","title":"Hierarchical 3D fully convolutional networks for multi-organ segmentation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1704.06382","snapshot_observed_at":"2026-08-14T05:10:00.558090Z","title":"arXiv:1704.06382 (2017)","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1909.06012","last_updated":"2019-09-04T15:03:08Z","snapshot_observed_at":"2026-08-14T05:04:03.908012Z","submitted_at":"2019-09-04T15:03:08Z","title":"3D U$^2$-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-14T05:10:00.558090Z"},"links":{"cited_paper":"/paper/1704.06382","citing_paper":"/paper/1909.06012"},"observation_digest":"sha256:0e1dd9c0d7f638d6372dad773511e75b9ec42a3951e06dab54844aa1d7771c44","observation_id":"09a5fa10-cd1b-42a8-b1af-80ac2a16ffac","resolution":{"observed_at":"2026-08-14T05:10:00.558090Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1808.01944","last_updated":"2018-09-27T09:27:11Z","snapshot_observed_at":"2026-08-14T18:44:06.276442Z","submitted_at":"2018-08-06T14:51:33Z","title":"V-FCNN: Volumetric Fully Convolution Neural Network For Automatic Atrial Segmentation","version":2},"cited_work":{"arxiv_id":"1808.01944","doi":null,"metadata_source":"pith","pith_arxiv_id":"1808.01944","snapshot_observed_at":"2026-08-14T05:10:00.606531Z","title":"V-FCNN: Volumetric Fully Convolution Neural Network For Automatic Atrial Segmentation","venue":"stat.ML","work_id":"622b788e-65dc-467e-9798-63c5cbe30909","year":2018},"citing_paper":{"arxiv_id":"1909.06012","last_updated":"2019-09-04T15:03:08Z","snapshot_observed_at":"2026-08-14T05:04:03.908012Z","submitted_at":"2019-09-04T15:03:08Z","title":"3D U$^2$-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-14T05:10:00.562308Z"},"links":{"cited_paper":"/paper/1808.01944","citing_paper":"/paper/1909.06012"},"observation_digest":"sha256:0ed642c126ad667b754d96b70ec02f489edf3a162a9579c64a61b7e60ef18bf6","observation_id":"16fd3fea-5b7e-4fd4-8115-d80f01efe159","resolution":{"observed_at":"2026-08-14T05:10:00.613130Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1902.09063","last_updated":"2019-02-25T02:34:48Z","snapshot_observed_at":"2026-08-14T17:12:00.846822Z","submitted_at":"2019-02-25T02:34:48Z","title":"A large annotated medical image dataset for the development and evaluation of segmentation algorithms","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1902.09063","snapshot_observed_at":"2026-08-14T05:10:00.566141Z","title":"arXiv:1902.09063 (2019)","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"1909.06012","last_updated":"2019-09-04T15:03:08Z","snapshot_observed_at":"2026-08-14T05:04:03.908012Z","submitted_at":"2019-09-04T15:03:08Z","title":"3D U$^2$-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-14T05:10:00.566141Z"},"links":{"cited_paper":"/paper/1902.09063","citing_paper":"/paper/1909.06012"},"observation_digest":"sha256:9878db2783146f0567c578ab7fbeec1c81f2516022fac2c6d29f73de6ba3dd45","observation_id":"2d9ca8f9-f207-4e18-b257-0d2def245429","resolution":{"observed_at":"2026-08-14T05:10:00.566141Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"1909.06012","last_updated":"2019-09-04T15:03:08Z","latest_version":1,"primary_category":"eess.IV","snapshot_observed_at":"2026-08-14T05:04:03.908012Z","submitted_at":"2019-09-04T15:03:08Z","title":"3D U$^2$-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation"},"reference_resolution":{"displayed":19,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":6,"verified_exact":0,"verified_fuzzy":12},"total_outbound_references":19},"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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 0 inbound Pith citation observations for arXiv:1909.06012."}