{"as_of":"2026-08-20T10:51:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d3e26268c84b4cdaa869135e6be4651a55389780ca738d8d709d0168865d49d8","coverage":[{"denominator":3,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-02T04:53:22.001699Z","state":"measured"},{"denominator":3,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":3,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+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/2607.00370/citation-record","integrity":"/paper/2607.00370/integrity","json":"/paper/2607.00370/citation-record.json","paper":"/paper/2607.00370"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1186/1748-717x-8-84","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"SAM-UNETR: Clinically Significant Prostate Cancer Segmentation Using Transfer Learning From Large Model,","venue":"Radiation Oncology","work_id":"d2b8553e-3ff7-4026-b182-1cbc142a01ed","year":2023},"citing_paper":{"arxiv_id":"2607.00370","last_updated":"2026-07-01T03:10:14Z","snapshot_observed_at":"2026-08-18T15:44:57.708584Z","submitted_at":"2026-07-01T03:10:14Z","title":"Enhancing Prostate Cancer Segmentation for Multi-Domain Generalization using a novel Parallel-Route Coherent Mixup Regularization Training","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-07-02T04:53:22.001699Z"},"links":{"citing_paper":"/paper/2607.00370"},"observation_digest":"sha256:974bae8b7e655caef3c64751d3e140cf5ab99c99a91905e9d01e1a3383610c93","observation_id":"f755f3d2-5aac-485d-8868-f813bf55c0f9","resolution":{"observed_at":"2026-07-02T04:56:38.135898Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"7964.100017","doi":"10.4172/2167-7964.1000170","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Deep learning-based dominant index lesion segmentation for MR-guided radiation therapy of prostate cancer,","venue":"OMICS Journal of Radiology","work_id":"413d92df-1e75-4d1d-a3cc-8f1f1e78f3b9","year":2023},"citing_paper":{"arxiv_id":"2607.00370","last_updated":"2026-07-01T03:10:14Z","snapshot_observed_at":"2026-08-18T15:44:57.708584Z","submitted_at":"2026-07-01T03:10:14Z","title":"Enhancing Prostate Cancer Segmentation for Multi-Domain Generalization using a novel Parallel-Route Coherent Mixup Regularization Training","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-07-02T04:53:22.001699Z"},"links":{"citing_paper":"/paper/2607.00370"},"observation_digest":"sha256:1b6fc0780ef2f4d151c865625acbb746113e853598cfa16fb70e4a23583bc51d","observation_id":"d9cc5060-6ed5-481a-be15-ac86a73ca806","resolution":{"observed_at":"2026-07-02T04:56:38.123522Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2020.297359","doi":"10.1109/tmi.2020.2973595","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Zhang, X","venue":"IEEE Transactions on Medical Imaging","work_id":"b2fdfcfd-82b9-4b80-ad27-95a38ffa07df","year":2020},"citing_paper":{"arxiv_id":"2607.00370","last_updated":"2026-07-01T03:10:14Z","snapshot_observed_at":"2026-08-18T15:44:57.708584Z","submitted_at":"2026-07-01T03:10:14Z","title":"Enhancing Prostate Cancer Segmentation for Multi-Domain Generalization using a novel Parallel-Route Coherent Mixup Regularization Training","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-07-02T04:53:22.001699Z"},"links":{"citing_paper":"/paper/2607.00370"},"observation_digest":"sha256:5659730d97b8f84ba2a1fe8585515f0fca61cbd1b937600373857a0cb51938ec","observation_id":"6e6d5ec4-a94d-41a3-a80c-8276595e4cb1","resolution":{"observed_at":"2026-07-02T04:56:38.132715Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2607.00370","last_updated":"2026-07-01T03:10:14Z","latest_version":1,"primary_category":"eess.IV","snapshot_observed_at":"2026-08-18T15:44:57.708584Z","submitted_at":"2026-07-01T03:10:14Z","title":"Enhancing Prostate Cancer Segmentation for Multi-Domain Generalization using a novel Parallel-Route Coherent Mixup Regularization Training"},"reference_resolution":{"displayed":3,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":0,"verified_exact":2,"verified_fuzzy":0},"total_outbound_references":3},"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":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 20 August 2026, this Paper Citation Record lists 3 of 3 outbound references and 0 inbound Pith citation observations for arXiv:2607.00370."}