{"as_of":"2026-08-07T19:35:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:9b3515d16b32851c2cc5af0bedb4ac2a7e27f4a1fd82b1557179d84f1bcc80ad","coverage":[{"denominator":16,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":16,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T14:23:20.216555Z","state":"measured"},{"denominator":16,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":16,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+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/2509.02592/citation-record","integrity":"/paper/2509.02592/integrity","json":"/paper/2509.02592/citation-record.json","paper":"/paper/2509.02592"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:23:18.529895Z","title":"BMC Bioinformatics 14(1), 106 (Dec 2013)","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2509.02592","last_updated":"2025-08-29T05:57:17Z","snapshot_observed_at":"2026-08-06T20:48:50.643505Z","submitted_at":"2025-08-29T05:57:17Z","title":"Beyond Synthetic Augmentation: Group-Aware Threshold Calibration for Robust Balanced Accuracy in Imbalanced Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-05T14:23:18.529895Z"},"links":{"citing_paper":"/paper/2509.02592"},"observation_digest":"sha256:26321b66c00b64915f4c42dad19e4f85eef82fcc4e5a0911fe64dc95702d6136","observation_id":"bbd4e0cf-2f21-404b-9e70-f293fe3b6ee8","resolution":{"observed_at":"2026-08-05T14:23:18.529895Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1106.1813","last_updated":"2011-06-09T13:53:42Z","snapshot_observed_at":"2026-08-02T09:14:51.557501Z","submitted_at":"2011-06-09T13:53:42Z","title":"SMOTE: Synthetic Minority Over-sampling Technique","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1106.1813","snapshot_observed_at":"2026-08-05T14:23:18.630025Z","title":"Journal of Artificial Intelligence Research16, 321–357 (Jun 2002)","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2509.02592","last_updated":"2025-08-29T05:57:17Z","snapshot_observed_at":"2026-08-06T20:48:50.643505Z","submitted_at":"2025-08-29T05:57:17Z","title":"Beyond Synthetic Augmentation: Group-Aware Threshold Calibration for Robust Balanced Accuracy in Imbalanced Learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-05T14:23:18.630025Z"},"links":{"cited_paper":"/paper/1106.1813","citing_paper":"/paper/2509.02592"},"observation_digest":"sha256:e5baf2aee926181dccc46a23a9da3ecc51f529de7dd55503e6978a1b768adfd3","observation_id":"78552f40-8e8a-44a5-8634-733d3e289383","resolution":{"observed_at":"2026-08-05T14:23:18.630025Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1701.08230","last_updated":"2017-06-10T00:01:23Z","snapshot_observed_at":"2026-07-06T05:27:54.149181Z","submitted_at":"2017-01-28T00:42:00Z","title":"Algorithmic decision making and the cost of fairness","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1701.08230","snapshot_observed_at":"2026-08-05T14:23:18.757038Z","title":"https://doi.org/10.48550/arXiv.1701","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.02592","last_updated":"2025-08-29T05:57:17Z","snapshot_observed_at":"2026-08-06T20:48:50.643505Z","submitted_at":"2025-08-29T05:57:17Z","title":"Beyond Synthetic Augmentation: Group-Aware Threshold Calibration for Robust Balanced Accuracy in Imbalanced Learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-05T14:23:18.757038Z"},"links":{"cited_paper":"/paper/1701.08230","citing_paper":"/paper/2509.02592"},"observation_digest":"sha256:b8995e7f5b92a2ed407c6967da055f0b0e653c1cacbd20d0512e90fe857ce2cc","observation_id":"5643878f-2c4a-4702-84ab-eff977c02aca","resolution":{"observed_at":"2026-08-05T14:23:18.757038Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.13710","last_updated":"2024-11-05T13:59:31Z","snapshot_observed_at":"2026-07-06T18:48:37.723242Z","submitted_at":"2024-06-30T16:41:28Z","title":"OxonFair: A Flexible Toolkit for Algorithmic Fairness","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.13710","snapshot_observed_at":"2026-08-05T14:23:18.839898Z","title":"https://doi.org/10.48550/arXiv.2407","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.02592","last_updated":"2025-08-29T05:57:17Z","snapshot_observed_at":"2026-08-06T20:48:50.643505Z","submitted_at":"2025-08-29T05:57:17Z","title":"Beyond Synthetic Augmentation: Group-Aware Threshold Calibration for Robust Balanced Accuracy in Imbalanced Learning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-05T14:23:18.839898Z"},"links":{"cited_paper":"/paper/2407.13710","citing_paper":"/paper/2509.02592"},"observation_digest":"sha256:cbe4fee5d993a34735ae72f179f5ce5c4408492b7a47846adc0aefa0ee089a0f","observation_id":"49ddde33-5be9-4b96-80a3-67bbf208f1de","resolution":{"observed_at":"2026-08-05T14:23:18.839898Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2008.09202","last_updated":"2020-08-20T20:33:56Z","snapshot_observed_at":"2026-07-06T09:48:57.151465Z","submitted_at":"2020-08-20T20:33:56Z","title":"Conditional Wasserstein GAN-based Oversampling of Tabular Data for Imbalanced Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2008.09202","snapshot_observed_at":"2026-08-05T14:23:18.948443Z","title":"https://doi.org/10.48550/ arXiv.2008.09202, http://arxiv.org/abs/2008.09202, arXiv:2008.09202 [cs]","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.02592","last_updated":"2025-08-29T05:57:17Z","snapshot_observed_at":"2026-08-06T20:48:50.643505Z","submitted_at":"2025-08-29T05:57:17Z","title":"Beyond Synthetic Augmentation: Group-Aware Threshold Calibration for Robust Balanced Accuracy in Imbalanced Learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-05T14:23:18.948443Z"},"links":{"cited_paper":"/paper/2008.09202","citing_paper":"/paper/2509.02592"},"observation_digest":"sha256:80eb442900a429c774eee4a2f5edd81466113343661b80d164abd8fcf95c8364","observation_id":"ca96d674-ab87-43a7-bc84-6c99388d4f7d","resolution":{"observed_at":"2026-08-05T14:23:18.948443Z","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":"2026-08-05T14:23:19.011009Z","title":"Journal of Chemical Information and Modeling61(6), 2623–2640 (Jun 2021)","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.02592","last_updated":"2025-08-29T05:57:17Z","snapshot_observed_at":"2026-08-06T20:48:50.643505Z","submitted_at":"2025-08-29T05:57:17Z","title":"Beyond Synthetic Augmentation: Group-Aware Threshold Calibration for Robust Balanced Accuracy in Imbalanced Learning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-05T14:23:19.011009Z"},"links":{"citing_paper":"/paper/2509.02592"},"observation_digest":"sha256:da0e9a590023f70208e27f63f7e6987063c934a4f0dee016b1785b4108b9cdb0","observation_id":"9d53d3d2-713c-4c7a-a2c1-106ef298c4e9","resolution":{"observed_at":"2026-08-05T14:23:19.011009Z","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":"document/5128907","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:23:21.248716Z","title":"IEEE Transactions on Knowledge and Data Engineering21(9), 1263–1284 (Sep 2009)","venue":null,"work_id":"643adfb6-128e-44a3-bcbb-2d2139804d53","year":2009},"citing_paper":{"arxiv_id":"2509.02592","last_updated":"2025-08-29T05:57:17Z","snapshot_observed_at":"2026-08-06T20:48:50.643505Z","submitted_at":"2025-08-29T05:57:17Z","title":"Beyond Synthetic Augmentation: Group-Aware Threshold Calibration for Robust Balanced Accuracy in Imbalanced Learning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-05T14:23:19.141517Z"},"links":{"citing_paper":"/paper/2509.02592"},"observation_digest":"sha256:27202b1a9fc10ccdc12ea500f35d0f147666aeca66191cd0337cf026053d4ab9","observation_id":"43a9bd4e-c385-4c01-9973-5c324dba7f5f","resolution":{"observed_at":"2026-08-05T14:23:21.278722Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1610.02413","last_updated":"2016-10-07T20:16:29Z","snapshot_observed_at":"2026-07-06T05:13:49.420787Z","submitted_at":"2016-10-07T20:16:29Z","title":"Equality of Opportunity in Supervised Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1610.02413","snapshot_observed_at":"2026-08-05T14:23:19.232603Z","title":"https://doi.org/10.48550/arXiv.1610.02413, http://arxiv.org/abs/1610","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2509.02592","last_updated":"2025-08-29T05:57:17Z","snapshot_observed_at":"2026-08-06T20:48:50.643505Z","submitted_at":"2025-08-29T05:57:17Z","title":"Beyond Synthetic Augmentation: Group-Aware Threshold Calibration for Robust Balanced Accuracy in Imbalanced Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-05T14:23:19.232603Z"},"links":{"cited_paper":"/paper/1610.02413","citing_paper":"/paper/2509.02592"},"observation_digest":"sha256:ea81cfc935f02accf774090316891f6d6c2e245187c9fb2201ef9b2f24827861","observation_id":"ff52c57d-e757-435a-9d1b-cd7cc3a7fbf7","resolution":{"observed_at":"2026-08-05T14:23:19.232603Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2202.03579","last_updated":"2022-06-08T14:01:43Z","snapshot_observed_at":"2026-08-07T05:10:41.239420Z","submitted_at":"2022-02-04T15:11:11Z","title":"Stop Oversampling for Class Imbalance Learning: A Critical Review","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2202.03579","snapshot_observed_at":"2026-08-05T14:23:19.403682Z","title":"https://doi","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.02592","last_updated":"2025-08-29T05:57:17Z","snapshot_observed_at":"2026-08-06T20:48:50.643505Z","submitted_at":"2025-08-29T05:57:17Z","title":"Beyond Synthetic Augmentation: Group-Aware Threshold Calibration for Robust Balanced Accuracy in Imbalanced Learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-05T14:23:19.403682Z"},"links":{"cited_paper":"/paper/2202.03579","citing_paper":"/paper/2509.02592"},"observation_digest":"sha256:5371255d7ab04751c3fa2985314934013ab75062c533e59bcabe1c5f05237b58","observation_id":"d01bd10d-8b70-4af0-b458-32eba27386b3","resolution":{"observed_at":"2026-08-05T14:23:19.403682Z","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":"2019.10566","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:23:21.017447Z","title":"Applied Soft Computing83, 105662 (Oct 2019)","venue":null,"work_id":"42dd9604-6c54-4e37-86c2-f06dc0b2d045","year":2019},"citing_paper":{"arxiv_id":"2509.02592","last_updated":"2025-08-29T05:57:17Z","snapshot_observed_at":"2026-08-06T20:48:50.643505Z","submitted_at":"2025-08-29T05:57:17Z","title":"Beyond Synthetic Augmentation: Group-Aware Threshold Calibration for Robust Balanced Accuracy in Imbalanced Learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-05T14:23:19.467636Z"},"links":{"citing_paper":"/paper/2509.02592"},"observation_digest":"sha256:4fc57a3dd79d1ca8d6ce185e64bf2199bf457f6fe8ecf5d909c0ec3938124cfc","observation_id":"7dc22a35-a775-4aec-8027-ef83d6eb690c","resolution":{"observed_at":"2026-08-05T14:23:21.110078Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2107.09044","last_updated":"2021-09-27T09:09:53Z","snapshot_observed_at":"2026-07-06T11:30:29.656834Z","submitted_at":"2021-07-19T17:52:32Z","title":"Just Train Twice: Improving Group Robustness without Training Group Information","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.09044","snapshot_observed_at":"2026-08-05T14:23:19.585745Z","title":"https://doi.org/10.48550/arXiv.2107.09044, http: //arxiv.org/abs/2107.09044, arXiv:2107.09044 [cs]","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.02592","last_updated":"2025-08-29T05:57:17Z","snapshot_observed_at":"2026-08-06T20:48:50.643505Z","submitted_at":"2025-08-29T05:57:17Z","title":"Beyond Synthetic Augmentation: Group-Aware Threshold Calibration for Robust Balanced Accuracy in Imbalanced Learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-05T14:23:19.585745Z"},"links":{"cited_paper":"/paper/2107.09044","citing_paper":"/paper/2509.02592"},"observation_digest":"sha256:aee084ef04726fdfd7ef93ddbc076dffa7372d9c33f00a3a5627f9b8ccc5d521","observation_id":"13a2914e-61b0-4077-9b86-de6ce891f100","resolution":{"observed_at":"2026-08-05T14:23:19.585745Z","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":"2026-08-05T14:23:19.705256Z","title":"Pattern Recognition 91, 216–231 (Jul 2019)","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2509.02592","last_updated":"2025-08-29T05:57:17Z","snapshot_observed_at":"2026-08-06T20:48:50.643505Z","submitted_at":"2025-08-29T05:57:17Z","title":"Beyond Synthetic Augmentation: Group-Aware Threshold Calibration for Robust Balanced Accuracy in Imbalanced Learning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-05T14:23:19.705256Z"},"links":{"citing_paper":"/paper/2509.02592"},"observation_digest":"sha256:5a276f28d45a675eaadb6a712e7bd48aa2a1aab016f919331646d72424951988","observation_id":"14469329-22cd-472e-a802-5a52e1d61425","resolution":{"observed_at":"2026-08-05T14:23:19.705256Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1911.08731","last_updated":"2020-04-02T05:40:29Z","snapshot_observed_at":"2026-08-02T19:28:48.954133Z","submitted_at":"2019-11-20T06:43:41Z","title":"Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.08731","snapshot_observed_at":"2026-08-05T14:23:19.839324Z","title":"https://doi.org/10.48550/arXiv.1911.08731, http:// arxiv.org/abs/1911.08731, arXiv:1911.08731 [cs]","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.02592","last_updated":"2025-08-29T05:57:17Z","snapshot_observed_at":"2026-08-06T20:48:50.643505Z","submitted_at":"2025-08-29T05:57:17Z","title":"Beyond Synthetic Augmentation: Group-Aware Threshold Calibration for Robust Balanced Accuracy in Imbalanced Learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-05T14:23:19.839324Z"},"links":{"cited_paper":"/paper/1911.08731","citing_paper":"/paper/2509.02592"},"observation_digest":"sha256:a6fc8c3243ab63a9884dc6905545c156e5d59080daafe47529946597e6bf879e","observation_id":"b45a7bea-abba-4063-b7e6-3733fa5473da","resolution":{"observed_at":"2026-08-05T14:23:19.839324Z","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":"2018.28667","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:23:20.820637Z","title":"IEEE Computational Intelligence Magazine 13(4), 59–76 (Nov 2018)","venue":null,"work_id":"263d7abd-31a1-4ea8-92ab-2fbf65d9420d","year":2018},"citing_paper":{"arxiv_id":"2509.02592","last_updated":"2025-08-29T05:57:17Z","snapshot_observed_at":"2026-08-06T20:48:50.643505Z","submitted_at":"2025-08-29T05:57:17Z","title":"Beyond Synthetic Augmentation: Group-Aware Threshold Calibration for Robust Balanced Accuracy in Imbalanced Learning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-05T14:23:19.943581Z"},"links":{"citing_paper":"/paper/2509.02592"},"observation_digest":"sha256:ab99218d2e8a4c996ded6dc6348a69e45e3a9832e833bdcbe28272e6d0471c90","observation_id":"0cc4d388-f986-4106-8344-4c74c7a90415","resolution":{"observed_at":"2026-08-05T14:23:20.882192Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"document/6137280","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:23:20.580906Z","title":"In: 2011 IEEE 11th International Conference on Data Mining","venue":null,"work_id":"d6442264-38a3-4c31-96ea-b4aa415c4c54","year":2011},"citing_paper":{"arxiv_id":"2509.02592","last_updated":"2025-08-29T05:57:17Z","snapshot_observed_at":"2026-08-06T20:48:50.643505Z","submitted_at":"2025-08-29T05:57:17Z","title":"Beyond Synthetic Augmentation: Group-Aware Threshold Calibration for Robust Balanced Accuracy in Imbalanced Learning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-05T14:23:20.051816Z"},"links":{"citing_paper":"/paper/2509.02592"},"observation_digest":"sha256:9f4cd38d64811b417aa7790979b6d6cb883bf1a9efe926d89ef32820cbb3b1d6","observation_id":"a26fe75d-cda4-4e0d-bcf2-4cc0be4bf78d","resolution":{"observed_at":"2026-08-05T14:23:20.646836Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1907.00503","last_updated":"2019-10-28T02:13:06Z","snapshot_observed_at":"2026-07-06T08:03:56.215960Z","submitted_at":"2019-07-01T00:11:32Z","title":"Modeling Tabular data using Conditional GAN","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1907.00503","snapshot_observed_at":"2026-08-05T14:23:20.216555Z","title":"https://doi.org/10.48550/arXiv.1907","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2509.02592","last_updated":"2025-08-29T05:57:17Z","snapshot_observed_at":"2026-08-06T20:48:50.643505Z","submitted_at":"2025-08-29T05:57:17Z","title":"Beyond Synthetic Augmentation: Group-Aware Threshold Calibration for Robust Balanced Accuracy in Imbalanced Learning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T14:23:20.216555Z"},"links":{"cited_paper":"/paper/1907.00503","citing_paper":"/paper/2509.02592"},"observation_digest":"sha256:0a1dd49d9bcd89bb50ac33c1bee89ada93f8c5c106c49ee3d70d6aeea5f01679","observation_id":"a2c534f4-6fd9-487b-aadd-577881027b0c","resolution":{"observed_at":"2026-08-05T14:23:20.216555Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2509.02592","last_updated":"2025-08-29T05:57:17Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-06T20:48:50.643505Z","submitted_at":"2025-08-29T05:57:17Z","title":"Beyond Synthetic Augmentation: Group-Aware Threshold Calibration for Robust Balanced Accuracy in Imbalanced Learning"},"reference_resolution":{"displayed":16,"state_counts":{"malformed_identifier":0,"metadata_mismatch":2,"parse_uncertain":0,"unresolved":12,"verified_exact":2,"verified_fuzzy":0},"total_outbound_references":16},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 0 inbound Pith citation observations for arXiv:2509.02592."}