{"as_of":"2026-08-10T02:40:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4884a80b18d7ee6a094f36d076b1946f5a786812d797fb4468fc1dcfb0538add","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":"2026-08-09T06:31:02.800959+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":"2026-08-08T17:44:47.812509Z","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":"sha256:41ce517a2752d8034109831dcb711531d68287244eb2d958a1113e232927675a","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":"2026-08-08T17:44:47.812509Z","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":"sha256:5582ddc807c9dea5865fb1ac16e058c842c9b05bc8e3b07c12a27e09189e1619","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":"2026-08-08T17:44:47.812509Z","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:cab8d31b130de36d2a79829a12a076a6f4191f82a79cdc9303788e15286b78b7","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":"2026-08-08T17:44:47.812509Z","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:c50421a9326dd1cb80c23f484b715a83c98f5b1fe796cb01b54f9517332bcd65","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":"2026-08-09T04:15:35.641047Z","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":"2026-08-08T17:44:47.812509Z","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":"sha256:2ad8f872d2976973bbc85c110b08e93e250bb66d7a82f4d2aafcc1301b918432","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":"2026-08-08T17:44:47.812509Z","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":"sha256:012aec5ae5c9a64e95ac5fc846c92297034f87382cc002c59af9c927643a1857","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":"2026-08-08T17:44:47.812509Z","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:df06c4d5a97059410ceaae28aa57affbdee0490898060d9a46b0277d5de604ad","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 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T17:44:47.812509Z","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:cc42e35e23b4492477799fc572177eeeb02f92a27379c0453eac4b46232e7232","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":"2026-08-08T17:44:47.812509Z","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":"sha256:2067c62d117a39930df39dae2d7e61b2fdf2b28e33efa901b77fdda701d5a68a","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":"2026-08-08T17:44:47.812509Z","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":"sha256:68595b09ebf7bdee70fa8476e9040197e525c1ed5185fe086a438d932b5159b5","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 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T17:44:47.812509Z","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:e756c4aaab547be6f58e7b98b066e6db5c0035557af22eb5a29167312fbf2c76","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":"2026-08-08T17:44:47.812509Z","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:ee0a175fc1c8a58b501d26e1afeb2c9021ab14b90411e7bd295465557b44f28b","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":"2026-08-08T17:44:47.812509Z","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":"sha256:2044328e14a993a89c5b4bc3e2473cdde455810b1522ecc6d9c893a47cde4aac","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":"2026-08-08T17:44:47.812509Z","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:bdfc50a10c211786097ff49b06509454ccd81a2929db1993174942c7f7e12ed0","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 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T17:44:47.812509Z","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":"sha256:2c7c9e5c7351f2bdf30fc9309b31a58b4b0a25dac9d91db8cbbd6a61b5a4a9fe","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 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1907.00503","last_updated":"2019-10-28T02:13:06Z","snapshot_observed_at":"2026-08-10T01:12:25.995412Z","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":"2026-08-08T17:44:47.812509Z","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":"sha256:9b26cdcd99e8f3769dedf3332d56d4acbdc37da6832ae89c40ba5aa781fbea99","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":"2026-08-08T17:44:47.812509Z","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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 0 inbound Pith citation observations for arXiv:2509.02592."}