{"as_of":"2026-08-14T21:41:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:86d41027ec660ab208a04b79a49e0cfb5dd73e003902d18338bc14add1b373b9","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":5,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":5,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T15:17:20.180625Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-11T09:36:02.073438Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1710.10501","last_updated":"2018-02-01T22:16:56Z","snapshot_observed_at":"2026-08-14T20:19:21.502690Z","submitted_at":"2017-10-28T17:25:23Z","title":"Learning to diagnose from scratch by exploiting dependencies among labels","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.10501","snapshot_observed_at":"2026-08-10T15:17:20.180625Z","title":"Learning to diagnose from scratch by exploiting dependencies among labels","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2501.14279","last_updated":"2025-01-24T06:50:21Z","snapshot_observed_at":"2026-08-14T05:14:41.669555Z","submitted_at":"2025-01-24T06:50:21Z","title":"Deep Learning-Powered Classification of Thoracic Diseases in Chest X-Rays","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T15:17:20.180625Z"},"links":{"cited_paper":"/paper/1710.10501","citing_paper":"/paper/2501.14279"},"observation_digest":"sha256:479587154359436a369520fa3256fb770e40aeb0d53cf2754af66b4c3fd1a8e7","observation_id":"1f9e7125-e2fc-4a02-988c-73a4d9d2d5fe","resolution":{"observed_at":"2026-08-10T15:17:20.180625Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1710.10501","last_updated":"2018-02-01T22:16:56Z","snapshot_observed_at":"2026-08-14T20:19:21.502690Z","submitted_at":"2017-10-28T17:25:23Z","title":"Learning to diagnose from scratch by exploiting dependencies among labels","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.10501","snapshot_observed_at":"2026-08-07T12:46:17.346353Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.23595","last_updated":"2025-05-29T16:08:26Z","snapshot_observed_at":"2026-08-09T13:19:19.275352Z","submitted_at":"2025-05-29T16:08:26Z","title":"DeepChest: Dynamic Gradient-Free Task Weighting for Effective Multi-Task Learning in Chest X-ray Classification","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T12:46:17.346353Z"},"links":{"cited_paper":"/paper/1710.10501","citing_paper":"/paper/2505.23595"},"observation_digest":"sha256:ff44fdd45a354e1d46afd98ee839067b83da750df1f1a11dc73ec75214767d28","observation_id":"4d02c912-9fbd-4b75-b6f9-1f173e3d685c","resolution":{"observed_at":"2026-08-07T12:46:17.346353Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1710.10501","last_updated":"2018-02-01T22:16:56Z","snapshot_observed_at":"2026-08-14T20:19:21.502690Z","submitted_at":"2017-10-28T17:25:23Z","title":"Learning to diagnose from scratch by exploiting dependencies among labels","version":2},"cited_work":{"arxiv_id":"1710.10501","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1710.10501","snapshot_observed_at":"2026-07-04T22:31:07.931311Z","title":"Learning to Diagnose from Scratch by Exploiting Dependencies among Labels","venue":null,"work_id":"d40ad32f-29ea-4041-a981-ccee2b6c52a0","year":2017},"citing_paper":{"arxiv_id":"2604.12305","last_updated":"2026-04-14T05:27:39Z","snapshot_observed_at":"2026-08-13T03:33:38.258092Z","submitted_at":"2026-04-14T05:27:39Z","title":"CBAM-Enhanced DenseNet121 for Multi-Class Chest X-Ray Classification with Grad-CAM Explainability","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-10T15:56:38.384530Z"},"links":{"cited_paper":"/paper/1710.10501","citing_paper":"/paper/2604.12305"},"observation_digest":"sha256:3cfa1dc5cb2716b58e704580746a0b5c184646d183f6bc766546f14045c03bf4","observation_id":"b2fbb56b-8f74-4216-bfe1-a7ac09934487","resolution":{"observed_at":"2026-07-04T22:31:07.931311Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1710.10501","last_updated":"2018-02-01T22:16:56Z","snapshot_observed_at":"2026-08-14T20:19:21.502690Z","submitted_at":"2017-10-28T17:25:23Z","title":"Learning to diagnose from scratch by exploiting dependencies among labels","version":2},"cited_work":{"arxiv_id":"1710.10501","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1710.10501","snapshot_observed_at":"2026-07-04T22:31:07.931311Z","title":"Learning to Diagnose from Scratch by Exploiting Dependencies among Labels","venue":null,"work_id":"d40ad32f-29ea-4041-a981-ccee2b6c52a0","year":2017},"citing_paper":{"arxiv_id":"2605.02328","last_updated":"2026-05-04T08:28:03Z","snapshot_observed_at":"2026-08-13T08:24:03.621159Z","submitted_at":"2026-05-04T08:28:03Z","title":"Improving Imbalanced Multi-Label Chest X-Ray Diagnosis via CBAM-Enhanced CNN Backbones","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-08T19:34:02.421331Z"},"links":{"cited_paper":"/paper/1710.10501","citing_paper":"/paper/2605.02328"},"observation_digest":"sha256:3ad7eb4c3d17b1bd35bc828100b7d5435e2f23bffc9172e349486640921fd9da","observation_id":"38825703-dc9e-4805-8a5d-79268c2dc712","resolution":{"observed_at":"2026-07-04T22:31:07.931311Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1710.10501","last_updated":"2018-02-01T22:16:56Z","snapshot_observed_at":"2026-08-14T20:19:21.502690Z","submitted_at":"2017-10-28T17:25:23Z","title":"Learning to diagnose from scratch by exploiting dependencies among labels","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.10501","snapshot_observed_at":"2026-07-12T00:27:00.531128Z","title":"arXiv preprint arXiv:1710.10501 (2017)","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.03715","last_updated":"2026-07-09T07:33:43Z","snapshot_observed_at":"2026-08-09T13:20:13.203037Z","submitted_at":"2026-07-04T05:41:58Z","title":"Leveraging Pathology Co-occurrence for Test-Time Adaptation in Chest X-Ray Diagnosis","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-07-12T00:27:00.531128Z"},"links":{"cited_paper":"/paper/1710.10501","citing_paper":"/paper/2607.03715"},"observation_digest":"sha256:6026f3080f13b7902249af4eece4d3569566aeadd3ef301c7a85a5af46e5512c","observation_id":"0f42aa87-e256-4f34-8259-85f7051e6248","resolution":{"observed_at":"2026-07-12T00:27:00.531128Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1710.10501/citation-record","integrity":"/paper/1710.10501/integrity","json":"/paper/1710.10501/citation-record.json","paper":"/paper/1710.10501"},"outbound":[],"paper":{"arxiv_id":"1710.10501","last_updated":"2018-02-01T22:16:56Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-14T20:19:21.502690Z","submitted_at":"2017-10-28T17:25:23Z","title":"Learning to diagnose from scratch by exploiting dependencies among labels"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:1710.10501."}