{"as_of":"2026-08-13T22:41:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6f42cca21be4c5b1c7fda5bac0fe786e8a14db08d2c1b83c7a79dd35d4107416","coverage":[{"denominator":33,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":33,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-25T06:58:18.999912Z","state":"measured"},{"denominator":33,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":33,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+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/2601.14180/citation-record","integrity":"/paper/2601.14180/integrity","json":"/paper/2601.14180/citation-record.json","paper":"/paper/2601.14180"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Chang, P.-H","venue":null,"work_id":"7a1ad352-bef1-4605-a1fb-776078acb7ee","year":2022},"citing_paper":{"arxiv_id":"2601.14180","last_updated":"2026-05-22T15:31:34Z","snapshot_observed_at":"2026-08-11T13:13:19.870497Z","submitted_at":"2026-01-20T17:35:02Z","title":"Progressive $\\mathcal{J}$-Invariant Self-supervised Learning for Low-Dose CT Denoising","version":4},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-25T06:58:18.999912Z"},"links":{"citing_paper":"/paper/2601.14180"},"observation_digest":"sha256:60656d3329cb5cd6772d38a4e47762c20fe5a5a6790df13efe6a6019f65c86c9","observation_id":"6e7e3c2a-b47a-447a-bcaf-a1dad6f6c041","resolution":{"observed_at":"2026-05-25T07:06:43.075754Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"sheng Chao, C.-Y","venue":null,"work_id":"3f4c74e1-0fde-40a8-b652-3b901e4811cb","year":2023},"citing_paper":{"arxiv_id":"2601.14180","last_updated":"2026-05-22T15:31:34Z","snapshot_observed_at":"2026-08-11T13:13:19.870497Z","submitted_at":"2026-01-20T17:35:02Z","title":"Progressive $\\mathcal{J}$-Invariant Self-supervised Learning for Low-Dose CT Denoising","version":4},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-25T06:58:18.999912Z"},"links":{"citing_paper":"/paper/2601.14180"},"observation_digest":"sha256:a404e583b782ad7c02d5fc9e02b2d29263433ffd8ee4943ef2f5dc97d729494c","observation_id":"f9b40c0f-dcd2-4809-ab5c-6b3d2500278c","resolution":{"observed_at":"2026-05-25T07:06:43.079372Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"e038b293-14e4-4d7c-b532-3381059f904e","year":2022},"citing_paper":{"arxiv_id":"2601.14180","last_updated":"2026-05-22T15:31:34Z","snapshot_observed_at":"2026-08-11T13:13:19.870497Z","submitted_at":"2026-01-20T17:35:02Z","title":"Progressive $\\mathcal{J}$-Invariant Self-supervised Learning for Low-Dose CT Denoising","version":4},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-25T06:58:18.999912Z"},"links":{"citing_paper":"/paper/2601.14180"},"observation_digest":"sha256:0ea89d9f62650551af35d20052774d785cfd2fe24e2cf6beecc64a8d0424726e","observation_id":"0fdc6998-0fbb-4631-b327-2d9562e75e53","resolution":{"observed_at":"2026-05-25T07:06:43.082923Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"c95c91d7-e56f-4c4b-aade-645454757a37","year":2023},"citing_paper":{"arxiv_id":"2601.14180","last_updated":"2026-05-22T15:31:34Z","snapshot_observed_at":"2026-08-11T13:13:19.870497Z","submitted_at":"2026-01-20T17:35:02Z","title":"Progressive $\\mathcal{J}$-Invariant Self-supervised Learning for Low-Dose CT Denoising","version":4},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-25T06:58:18.999912Z"},"links":{"citing_paper":"/paper/2601.14180"},"observation_digest":"sha256:120da437639304baabe45c1b453a05eccc9f34732bd0f9054ec6bee899348632","observation_id":"3ea38ab0-010f-4897-b595-350492a56310","resolution":{"observed_at":"2026-05-25T07:06:43.086329Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/s00330-015-4192-3","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Huber, J","venue":"European Radiology","work_id":"2d7db6e2-150a-4175-a63f-4d2dee299bb6","year":2016},"citing_paper":{"arxiv_id":"2601.14180","last_updated":"2026-05-22T15:31:34Z","snapshot_observed_at":"2026-08-11T13:13:19.870497Z","submitted_at":"2026-01-20T17:35:02Z","title":"Progressive $\\mathcal{J}$-Invariant Self-supervised Learning for Low-Dose CT Denoising","version":4},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-25T06:58:18.999912Z"},"links":{"citing_paper":"/paper/2601.14180"},"observation_digest":"sha256:8f061393e1407c6645c20c247862c322ea8a6fb5f675819093b238d7e0acafec","observation_id":"58a7bf6d-4d91-4068-b57e-00193e4e8702","resolution":{"observed_at":"2026-05-25T07:00:25.959308Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[{"edge_observation":{"observed_at":"2026-07-11T03:19:09.421391+00:00","source":"paper_reference_links","state":"open"},"event_date":"2016-04-05","event_type":"correction","notice_doi":"10.1007/s00330-016-4325-3","provenance":{"observed_at":"2026-07-11T03:09:06.814484+00:00","source":"crossref","source_record_id":"10.1007/s00330-016-4325-3->10.1007/s00330-015-4192-3:correction"}}],"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":null,"cited_work":{"arxiv_id":"3003.00177","doi":"10.1183/13993003.00177-2020","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Kim, H.-R","venue":"European Respiratory Journal","work_id":"9d3f3c60-c953-4b0e-85c4-deb04627eebc","year":2020},"citing_paper":{"arxiv_id":"2601.14180","last_updated":"2026-05-22T15:31:34Z","snapshot_observed_at":"2026-08-11T13:13:19.870497Z","submitted_at":"2026-01-20T17:35:02Z","title":"Progressive $\\mathcal{J}$-Invariant Self-supervised Learning for Low-Dose CT Denoising","version":4},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-25T06:58:18.999912Z"},"links":{"citing_paper":"/paper/2601.14180"},"observation_digest":"sha256:001178f6217936f25e0bb5ca80dcb226baa9d4db9db8059ca476a99aa9c5b8ee","observation_id":"75c1919a-1cb1-4231-8517-a2f90aebc5f4","resolution":{"observed_at":"2026-05-25T07:00:25.967421Z","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Ronneberger, P","venue":null,"work_id":"9ce10787-9175-4102-90d3-2380d4dceb57","year":2015},"citing_paper":{"arxiv_id":"2601.14180","last_updated":"2026-05-22T15:31:34Z","snapshot_observed_at":"2026-08-11T13:13:19.870497Z","submitted_at":"2026-01-20T17:35:02Z","title":"Progressive $\\mathcal{J}$-Invariant Self-supervised Learning for Low-Dose CT Denoising","version":4},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-25T06:58:18.999912Z"},"links":{"citing_paper":"/paper/2601.14180"},"observation_digest":"sha256:96597d9fa0bfdcf5bbab5f3c7e565bacc70cd53d2de77b41cbb519c780a8094b","observation_id":"4d5bb843-3078-45da-9a85-e2beb1edc5f4","resolution":{"observed_at":"2026-05-25T07:06:43.065725Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"ed077dc0-c243-4420-b435-08dd2a51ec8f","year":2017},"citing_paper":{"arxiv_id":"2601.14180","last_updated":"2026-05-22T15:31:34Z","snapshot_observed_at":"2026-08-11T13:13:19.870497Z","submitted_at":"2026-01-20T17:35:02Z","title":"Progressive $\\mathcal{J}$-Invariant Self-supervised Learning for Low-Dose CT Denoising","version":4},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-25T06:58:18.999912Z"},"links":{"citing_paper":"/paper/2601.14180"},"observation_digest":"sha256:dfe6add5984dc2f5dd86a9a39af5f30b507105a1f2f0cafdde852bdb5dfaaf15","observation_id":"f3fe0e32-4c17-4c1e-9836-6ada4db3eb8c","resolution":{"observed_at":"2026-05-25T07:06:43.068944Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"2223–2232","venue":null,"work_id":"2890d32a-275d-4643-9ff8-f64863d1d2f0","year":2017},"citing_paper":{"arxiv_id":"2601.14180","last_updated":"2026-05-22T15:31:34Z","snapshot_observed_at":"2026-08-11T13:13:19.870497Z","submitted_at":"2026-01-20T17:35:02Z","title":"Progressive $\\mathcal{J}$-Invariant Self-supervised Learning for Low-Dose CT Denoising","version":4},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-25T06:58:18.999912Z"},"links":{"citing_paper":"/paper/2601.14180"},"observation_digest":"sha256:ded5788cc8a6dcf16fcc68779a26a31a06aa6d75590e79dbc111a4db78f972f9","observation_id":"0522a0d2-980d-4878-aab9-7575b2a78cac","resolution":{"observed_at":"2026-05-25T07:06:43.072137Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"6d977592-6ed3-47cd-a6d6-3dc496603bf3","year":2019},"citing_paper":{"arxiv_id":"2601.14180","last_updated":"2026-05-22T15:31:34Z","snapshot_observed_at":"2026-08-11T13:13:19.870497Z","submitted_at":"2026-01-20T17:35:02Z","title":"Progressive $\\mathcal{J}$-Invariant Self-supervised Learning for Low-Dose CT Denoising","version":4},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-25T06:58:18.999912Z"},"links":{"citing_paper":"/paper/2601.14180"},"observation_digest":"sha256:154ebe77887eab7e3843c1a47b552882aabf4f5230440350e9c6c037560de388","observation_id":"fc952d4b-f75a-4b5e-9f38-2ba3c161e46b","resolution":{"observed_at":"2026-05-25T07:06:43.047631Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"86edc0cf-30b6-4aed-9fb1-6713c3542b9f","year":2023},"citing_paper":{"arxiv_id":"2601.14180","last_updated":"2026-05-22T15:31:34Z","snapshot_observed_at":"2026-08-11T13:13:19.870497Z","submitted_at":"2026-01-20T17:35:02Z","title":"Progressive $\\mathcal{J}$-Invariant Self-supervised Learning for Low-Dose CT Denoising","version":4},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-25T06:58:18.999912Z"},"links":{"citing_paper":"/paper/2601.14180"},"observation_digest":"sha256:178604b49a9b54384ee8422a8d30274319012114061aafb77fcdb3da4d402d14","observation_id":"1a8e9dbc-8f3c-403b-8df6-4e43837ca08a","resolution":{"observed_at":"2026-05-25T07:06:43.062270Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"2a4edc60-aace-43a0-b86c-b401d4b914c5","year":2023},"citing_paper":{"arxiv_id":"2601.14180","last_updated":"2026-05-22T15:31:34Z","snapshot_observed_at":"2026-08-11T13:13:19.870497Z","submitted_at":"2026-01-20T17:35:02Z","title":"Progressive $\\mathcal{J}$-Invariant Self-supervised Learning for Low-Dose CT Denoising","version":4},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-25T06:58:18.999912Z"},"links":{"citing_paper":"/paper/2601.14180"},"observation_digest":"sha256:09b070f77a617c6b99c718021b3deedf19ccda17724d717523af7a2f901e3bce","observation_id":"27995e4a-01e0-4e42-926b-40d491b6bdce","resolution":{"observed_at":"2026-05-25T07:06:43.051876Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"1696a098-8a99-41ef-ab62-1a82a55899d3","year":2024},"citing_paper":{"arxiv_id":"2601.14180","last_updated":"2026-05-22T15:31:34Z","snapshot_observed_at":"2026-08-11T13:13:19.870497Z","submitted_at":"2026-01-20T17:35:02Z","title":"Progressive $\\mathcal{J}$-Invariant Self-supervised Learning for Low-Dose CT Denoising","version":4},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-25T06:58:18.999912Z"},"links":{"citing_paper":"/paper/2601.14180"},"observation_digest":"sha256:7deb1132451da6ebd577dcb46db94c427a3457861f845414c5d19d18aad6869c","observation_id":"b19392da-c166-42ea-a72e-5bacb47728bc","resolution":{"observed_at":"2026-05-25T07:06:43.058990Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"0c3bf5aa-2100-4d67-ba13-45f97293ad3c","year":2022},"citing_paper":{"arxiv_id":"2601.14180","last_updated":"2026-05-22T15:31:34Z","snapshot_observed_at":"2026-08-11T13:13:19.870497Z","submitted_at":"2026-01-20T17:35:02Z","title":"Progressive $\\mathcal{J}$-Invariant Self-supervised Learning for Low-Dose CT Denoising","version":4},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-25T06:58:18.999912Z"},"links":{"citing_paper":"/paper/2601.14180"},"observation_digest":"sha256:f75497b59da273b6bb8b1be4491cd03a1a8e18452636b082130f1a6e57732a26","observation_id":"b52d9da8-97c1-4f63-8009-7c4b8dd772ea","resolution":{"observed_at":"2026-05-25T07:06:43.055769Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Dabov, A","venue":null,"work_id":"e96aa5fe-73d7-4e36-856e-67df8a243733","year":2007},"citing_paper":{"arxiv_id":"2601.14180","last_updated":"2026-05-22T15:31:34Z","snapshot_observed_at":"2026-08-11T13:13:19.870497Z","submitted_at":"2026-01-20T17:35:02Z","title":"Progressive $\\mathcal{J}$-Invariant Self-supervised Learning for Low-Dose CT Denoising","version":4},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-25T06:58:18.999912Z"},"links":{"citing_paper":"/paper/2601.14180"},"observation_digest":"sha256:6944d88ab84ae7948177aca64ff5ed94496e5442cbaeb5edea9eea98aa671d4d","observation_id":"627b317c-bdad-4d2b-ab0a-f34265446c84","resolution":{"observed_at":"2026-05-25T07:06:43.092565Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"fae226d8-301b-47cb-8727-60d8adf60707","year":2020},"citing_paper":{"arxiv_id":"2601.14180","last_updated":"2026-05-22T15:31:34Z","snapshot_observed_at":"2026-08-11T13:13:19.870497Z","submitted_at":"2026-01-20T17:35:02Z","title":"Progressive $\\mathcal{J}$-Invariant Self-supervised Learning for Low-Dose CT Denoising","version":4},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-25T06:58:18.999912Z"},"links":{"citing_paper":"/paper/2601.14180"},"observation_digest":"sha256:f7488b503f6a326074fff8492165f3e40d6ac95f4dbd07ab931ede5998c6335f","observation_id":"855e5b49-0bf8-4c6b-9961-01dfa5035d50","resolution":{"observed_at":"2026-05-25T07:06:43.100121Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2506.22012","last_updated":"2025-06-27T08:24:55Z","snapshot_observed_at":"2026-08-12T09:59:04.395407Z","submitted_at":"2025-06-27T08:24:55Z","title":"Noise-Inspired Diffusion Model for Generalizable Low-Dose CT Reconstruction","version":1},"cited_work":{"arxiv_id":"2506.22012","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.22012","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"a031c6ca-4bd2-424f-9ff9-93494babc7f5","year":2025},"citing_paper":{"arxiv_id":"2601.14180","last_updated":"2026-05-22T15:31:34Z","snapshot_observed_at":"2026-08-11T13:13:19.870497Z","submitted_at":"2026-01-20T17:35:02Z","title":"Progressive $\\mathcal{J}$-Invariant Self-supervised Learning for Low-Dose CT Denoising","version":4},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-25T06:58:18.999912Z"},"links":{"cited_paper":"/paper/2506.22012","citing_paper":"/paper/2601.14180"},"observation_digest":"sha256:916c0c513723c1d6ff4010d8d3e3e35335e3a7d3597a153cdd559b5a8ff6ccf9","observation_id":"8159c093-5db4-4989-a194-f2005d26fcf9","resolution":{"observed_at":"2026-05-25T07:00:27.017949Z","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":"2405.17167","last_updated":"2024-05-27T13:44:53Z","snapshot_observed_at":"2026-08-13T22:05:26.454835Z","submitted_at":"2024-05-27T13:44:53Z","title":"Partitioned Hankel-based Diffusion Models for Few-shot Low-dose CT Reconstruction","version":1},"cited_work":{"arxiv_id":"2405.17167","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2405.17167","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Zhang, B","venue":null,"work_id":"24dde599-4f86-42cb-880c-173bc5051f86","year":2024},"citing_paper":{"arxiv_id":"2601.14180","last_updated":"2026-05-22T15:31:34Z","snapshot_observed_at":"2026-08-11T13:13:19.870497Z","submitted_at":"2026-01-20T17:35:02Z","title":"Progressive $\\mathcal{J}$-Invariant Self-supervised Learning for Low-Dose CT Denoising","version":4},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-25T06:58:18.999912Z"},"links":{"cited_paper":"/paper/2405.17167","citing_paper":"/paper/2601.14180"},"observation_digest":"sha256:d47bf9aec8e6b17bff68beb270f0f6a0073d1e0e5e99049c2efd4f2c2e3265cd","observation_id":"8d94acf4-2fdc-4579-b27c-0e98dd6b4922","resolution":{"observed_at":"2026-05-25T07:00:27.025421Z","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"a4ca37b4-58a5-4e1d-8a36-4f6722f64d16","year":2025},"citing_paper":{"arxiv_id":"2601.14180","last_updated":"2026-05-22T15:31:34Z","snapshot_observed_at":"2026-08-11T13:13:19.870497Z","submitted_at":"2026-01-20T17:35:02Z","title":"Progressive $\\mathcal{J}$-Invariant Self-supervised Learning for Low-Dose CT Denoising","version":4},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-25T06:58:18.999912Z"},"links":{"citing_paper":"/paper/2601.14180"},"observation_digest":"sha256:26af4d80fa0a0d39dab7913ee27fddb0296b92ed3b0a81ada1971b096c4e8751","observation_id":"37892dea-0a22-497b-b971-d7a6ce30d5c6","resolution":{"observed_at":"2026-05-25T07:06:43.089473Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"1803.04189","last_updated":"2018-10-29T10:29:23Z","snapshot_observed_at":"2026-07-06T06:27:46.580255Z","submitted_at":"2018-03-12T11:07:58Z","title":"Noise2Noise: Learning Image Restoration without Clean Data","version":3},"cited_work":{"arxiv_id":"1803.04189","doi":null,"metadata_source":"pith","pith_arxiv_id":"1803.04189","snapshot_observed_at":"2026-07-04T14:49:55.543993Z","title":"Noise2Noise: Learning Image Restoration without Clean Data","venue":"cs.CV","work_id":"f74309ce-dfec-493a-8d1a-426f42c14736","year":2018},"citing_paper":{"arxiv_id":"2601.14180","last_updated":"2026-05-22T15:31:34Z","snapshot_observed_at":"2026-08-11T13:13:19.870497Z","submitted_at":"2026-01-20T17:35:02Z","title":"Progressive $\\mathcal{J}$-Invariant Self-supervised Learning for Low-Dose CT Denoising","version":4},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-25T06:58:18.999912Z"},"links":{"cited_paper":"/paper/1803.04189","citing_paper":"/paper/2601.14180"},"observation_digest":"sha256:337656c79f885a385a8fb6bd5c6163d2116fb803d9d03b1cd1fe317d022c855d","observation_id":"28f7a5cf-0052-4260-b9e1-4eb35487aae0","resolution":{"observed_at":"2026-05-25T07:00:27.030662Z","resolver_source":"local_arxiv","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Batson, L","venue":null,"work_id":"815103a4-6b30-4ac1-9dc5-83d45f2dcc78","year":2019},"citing_paper":{"arxiv_id":"2601.14180","last_updated":"2026-05-22T15:31:34Z","snapshot_observed_at":"2026-08-11T13:13:19.870497Z","submitted_at":"2026-01-20T17:35:02Z","title":"Progressive $\\mathcal{J}$-Invariant Self-supervised Learning for Low-Dose CT Denoising","version":4},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-25T06:58:18.999912Z"},"links":{"citing_paper":"/paper/2601.14180"},"observation_digest":"sha256:4f2b9c5672aa3bc4fba672823fd239d015cbdb6e9f4dd288386314eedea1f5d9","observation_id":"983c0b25-60c8-422f-8cc7-e4726e1ef2a6","resolution":{"observed_at":"2026-05-25T07:06:43.106046Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Krull, T.-O","venue":null,"work_id":"261a35c2-e4a2-4e51-b3fd-0621827da266","year":2019},"citing_paper":{"arxiv_id":"2601.14180","last_updated":"2026-05-22T15:31:34Z","snapshot_observed_at":"2026-08-11T13:13:19.870497Z","submitted_at":"2026-01-20T17:35:02Z","title":"Progressive $\\mathcal{J}$-Invariant Self-supervised Learning for Low-Dose CT Denoising","version":4},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-25T06:58:18.999912Z"},"links":{"citing_paper":"/paper/2601.14180"},"observation_digest":"sha256:f395699e8196af9c273905844b9ad2f06c5caa5a9e15bfdeb4572ca41a9c9aa1","observation_id":"32699b6c-3889-4194-9320-d242ee9d28e7","resolution":{"observed_at":"2026-05-25T07:06:43.136858Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"649389a8-4549-4cd5-88ff-3f5f32b151a4","year":2020},"citing_paper":{"arxiv_id":"2601.14180","last_updated":"2026-05-22T15:31:34Z","snapshot_observed_at":"2026-08-11T13:13:19.870497Z","submitted_at":"2026-01-20T17:35:02Z","title":"Progressive $\\mathcal{J}$-Invariant Self-supervised Learning for Low-Dose CT Denoising","version":4},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-25T06:58:18.999912Z"},"links":{"citing_paper":"/paper/2601.14180"},"observation_digest":"sha256:849a0f1cc52f576470090b51c26ead5263a5a3010faadc009fa20bd6d41742ab","observation_id":"e7d543a2-068e-4e36-a1b0-8801140b3450","resolution":{"observed_at":"2026-05-25T07:06:43.109295Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"08549949-a1e8-4cfb-9664-68847c32bab7","year":2020},"citing_paper":{"arxiv_id":"2601.14180","last_updated":"2026-05-22T15:31:34Z","snapshot_observed_at":"2026-08-11T13:13:19.870497Z","submitted_at":"2026-01-20T17:35:02Z","title":"Progressive $\\mathcal{J}$-Invariant Self-supervised Learning for Low-Dose CT Denoising","version":4},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-25T06:58:18.999912Z"},"links":{"citing_paper":"/paper/2601.14180"},"observation_digest":"sha256:cdc4ed33a2070a5b941b0bc74703b9d6d8b78c61a145775c235d8558073d1359","observation_id":"b65b13ab-2a19-491e-8765-f4ed68c2b496","resolution":{"observed_at":"2026-05-25T07:06:43.119672Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"e7a08f6b-411c-48fd-9637-ed4b767b369f","year":2021},"citing_paper":{"arxiv_id":"2601.14180","last_updated":"2026-05-22T15:31:34Z","snapshot_observed_at":"2026-08-11T13:13:19.870497Z","submitted_at":"2026-01-20T17:35:02Z","title":"Progressive $\\mathcal{J}$-Invariant Self-supervised Learning for Low-Dose CT Denoising","version":4},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-25T06:58:18.999912Z"},"links":{"citing_paper":"/paper/2601.14180"},"observation_digest":"sha256:6c28bbf37e04cace06ff162eac151f858f3dad1a17c64f274aaf06f830256173","observation_id":"08af1c86-6c82-4808-9cde-f8157f13dff5","resolution":{"observed_at":"2026-05-25T07:06:43.133163Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"57e2cff5-5651-4d24-a919-77ea1105ade8","year":2022},"citing_paper":{"arxiv_id":"2601.14180","last_updated":"2026-05-22T15:31:34Z","snapshot_observed_at":"2026-08-11T13:13:19.870497Z","submitted_at":"2026-01-20T17:35:02Z","title":"Progressive $\\mathcal{J}$-Invariant Self-supervised Learning for Low-Dose CT Denoising","version":4},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-25T06:58:18.999912Z"},"links":{"citing_paper":"/paper/2601.14180"},"observation_digest":"sha256:f7701049f7caa4a50731f94e35b2e43cd195beb81d9e2fc58651970fa3c4d223","observation_id":"74ebab85-9d9e-4b5d-9ce5-400896fff30b","resolution":{"observed_at":"2026-05-25T07:06:43.126053Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"f6d9aa3c-0c3b-4525-8416-a69a5170f0fb","year":2020},"citing_paper":{"arxiv_id":"2601.14180","last_updated":"2026-05-22T15:31:34Z","snapshot_observed_at":"2026-08-11T13:13:19.870497Z","submitted_at":"2026-01-20T17:35:02Z","title":"Progressive $\\mathcal{J}$-Invariant Self-supervised Learning for Low-Dose CT Denoising","version":4},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-25T06:58:18.999912Z"},"links":{"citing_paper":"/paper/2601.14180"},"observation_digest":"sha256:65ef47b5264c763e5c439ea5f697e39df2a1d0ed5fb4fea2bea9f259c1540c58","observation_id":"6cd9befe-9a89-48fb-8359-c17dc08c5584","resolution":{"observed_at":"2026-05-25T07:06:43.103116Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"27bdbc24-9b36-46cf-985b-330c4812dde3","year":2025},"citing_paper":{"arxiv_id":"2601.14180","last_updated":"2026-05-22T15:31:34Z","snapshot_observed_at":"2026-08-11T13:13:19.870497Z","submitted_at":"2026-01-20T17:35:02Z","title":"Progressive $\\mathcal{J}$-Invariant Self-supervised Learning for Low-Dose CT Denoising","version":4},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-25T06:58:18.999912Z"},"links":{"citing_paper":"/paper/2601.14180"},"observation_digest":"sha256:dfdefa8d03959b6166d8c432eacd04e1d65013a7404cdd7a23fbc87189d1fb39","observation_id":"265286b1-7041-4bf8-b9e0-9d7a85b20890","resolution":{"observed_at":"2026-05-25T07:06:43.112926Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Laine, T","venue":null,"work_id":"458448a4-33a3-4030-b548-e389eec66d7d","year":2019},"citing_paper":{"arxiv_id":"2601.14180","last_updated":"2026-05-22T15:31:34Z","snapshot_observed_at":"2026-08-11T13:13:19.870497Z","submitted_at":"2026-01-20T17:35:02Z","title":"Progressive $\\mathcal{J}$-Invariant Self-supervised Learning for Low-Dose CT Denoising","version":4},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-25T06:58:18.999912Z"},"links":{"citing_paper":"/paper/2601.14180"},"observation_digest":"sha256:ec7691e3c1af62c8ed8b889165cc24c9905ced00cbad3a38e1078be16c8e45f2","observation_id":"05fd8b36-ec6e-465e-8724-e976c7a10568","resolution":{"observed_at":"2026-05-25T07:06:43.122796Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"19946628-375a-4d09-b3d5-c5de4684a52e","year":2023},"citing_paper":{"arxiv_id":"2601.14180","last_updated":"2026-05-22T15:31:34Z","snapshot_observed_at":"2026-08-11T13:13:19.870497Z","submitted_at":"2026-01-20T17:35:02Z","title":"Progressive $\\mathcal{J}$-Invariant Self-supervised Learning for Low-Dose CT Denoising","version":4},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-25T06:58:18.999912Z"},"links":{"citing_paper":"/paper/2601.14180"},"observation_digest":"sha256:e6de1c1f706a5b78b2f610de483677a7f4521df82f06210f08e01227d5b5f8f5","observation_id":"c65329a8-119e-4c85-8021-9581f16fea86","resolution":{"observed_at":"2026-05-25T07:06:43.096273Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"5f9283e4-01a4-4a64-a9dc-22cd0bef5c4d","year":1990},"citing_paper":{"arxiv_id":"2601.14180","last_updated":"2026-05-22T15:31:34Z","snapshot_observed_at":"2026-08-11T13:13:19.870497Z","submitted_at":"2026-01-20T17:35:02Z","title":"Progressive $\\mathcal{J}$-Invariant Self-supervised Learning for Low-Dose CT Denoising","version":4},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-25T06:58:18.999912Z"},"links":{"citing_paper":"/paper/2601.14180"},"observation_digest":"sha256:143916303585e27ac4ca77fe18b4fafc45e0520a58a06ae7353e8c1fc43fd9f1","observation_id":"a0265f1a-bb0b-4a84-8246-dbcda06ad1b7","resolution":{"observed_at":"2026-05-25T07:06:43.116502Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"6f669255-3a4c-444b-814a-72d85a11064d","year":2021},"citing_paper":{"arxiv_id":"2601.14180","last_updated":"2026-05-22T15:31:34Z","snapshot_observed_at":"2026-08-11T13:13:19.870497Z","submitted_at":"2026-01-20T17:35:02Z","title":"Progressive $\\mathcal{J}$-Invariant Self-supervised Learning for Low-Dose CT Denoising","version":4},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-25T06:58:18.999912Z"},"links":{"citing_paper":"/paper/2601.14180"},"observation_digest":"sha256:63c3667ba0eba0324ee83ba8c9998339be0d0b7d54e59bc6e2c8c6e5aa3a014a","observation_id":"341707b2-5bca-4f6d-9aa1-98ddebeaecbf","resolution":{"observed_at":"2026-05-25T07:06:43.140059Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Huang, S","venue":null,"work_id":"760a405e-14ee-4ced-92a1-6c1f3fc1abc3","year":2021},"citing_paper":{"arxiv_id":"2601.14180","last_updated":"2026-05-22T15:31:34Z","snapshot_observed_at":"2026-08-11T13:13:19.870497Z","submitted_at":"2026-01-20T17:35:02Z","title":"Progressive $\\mathcal{J}$-Invariant Self-supervised Learning for Low-Dose CT Denoising","version":4},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-25T06:58:18.999912Z"},"links":{"citing_paper":"/paper/2601.14180"},"observation_digest":"sha256:d6fd5a5ce5e21364bf11b7c454dd3ad5fa5d4e27b352c10de2827b1615cf9f5c","observation_id":"1c2edba5-d923-43cf-aba0-62886b538512","resolution":{"observed_at":"2026-05-25T07:06:43.129257Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}}],"paper":{"arxiv_id":"2601.14180","last_updated":"2026-05-22T15:31:34Z","latest_version":4,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-11T13:13:19.870497Z","submitted_at":"2026-01-20T17:35:02Z","title":"Progressive $\\mathcal{J}$-Invariant Self-supervised Learning for Low-Dose CT Denoising"},"reference_resolution":{"displayed":33,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":19,"verified_exact":5,"verified_fuzzy":9},"total_outbound_references":33},"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 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2601.14180."}