{"as_of":"2026-08-13T00:10:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1fb18a84ec26003ee0b92c020108d558811bd3ea321a034fdbac9824ef3ee8ba","coverage":[{"denominator":27,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":27,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-26T11:34:53.405582Z","state":"measured"},{"denominator":27,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":27,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-12T06:34:41.77262+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/2606.22054/citation-record","integrity":"/paper/2606.22054/integrity","json":"/paper/2606.22054/citation-record.json","paper":"/paper/2606.22054"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T11:34:53.405582Z","title":"Accuracy on the line: on the strong correlation between out-of-distribution and in-distribution generalization,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.22054","last_updated":"2026-06-23T08:01:32Z","snapshot_observed_at":"2026-07-06T23:56:59.635251Z","submitted_at":"2026-06-20T14:10:59Z","title":"Anticipating the Optimism Gap: Predicting Distribution-Shift Degradation of RF-Impairment Detectors from In-Distribution Statistics","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-26T11:34:53.405582Z"},"links":{"citing_paper":"/paper/2606.22054"},"observation_digest":"sha256:962d357c555b07f372d452fcbffb5fbddf3262d4b32cabc4d30d0f9d68529a25","observation_id":"10315cd2-b02a-4bfb-8c7b-f408840f01a8","resolution":{"observed_at":"2026-06-26T11:34:53.405582Z","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":"2026-06-26T11:34:53.405582Z","title":"Agreement-on-the- line: predicting the performance of neural networks under distribution shift,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.22054","last_updated":"2026-06-23T08:01:32Z","snapshot_observed_at":"2026-07-06T23:56:59.635251Z","submitted_at":"2026-06-20T14:10:59Z","title":"Anticipating the Optimism Gap: Predicting Distribution-Shift Degradation of RF-Impairment Detectors from In-Distribution Statistics","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-26T11:34:53.405582Z"},"links":{"citing_paper":"/paper/2606.22054"},"observation_digest":"sha256:bb677d7585eba0fe2ed166fb579385bcea1162bba302b31c8766e820ae6dfaf7","observation_id":"d470950c-58ca-480f-8744-6ed1d6bc2fbf","resolution":{"observed_at":"2026-06-26T11:34:53.405582Z","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":"2026-06-26T11:34:53.405582Z","title":"Assessing generalization of SGD via disagreement,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.22054","last_updated":"2026-06-23T08:01:32Z","snapshot_observed_at":"2026-07-06T23:56:59.635251Z","submitted_at":"2026-06-20T14:10:59Z","title":"Anticipating the Optimism Gap: Predicting Distribution-Shift Degradation of RF-Impairment Detectors from In-Distribution Statistics","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-26T11:34:53.405582Z"},"links":{"citing_paper":"/paper/2606.22054"},"observation_digest":"sha256:ee07881b01812a9c3ca6d03dd4280a409b894c971455297484252ebbc48d0745","observation_id":"3a0ac248-503c-48af-9a36-39a68ab71595","resolution":{"observed_at":"2026-06-26T11:34:53.405582Z","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":"2026-06-26T11:34:53.405582Z","title":"Leveraging unlabeled data to predict out-of-distribution performance,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.22054","last_updated":"2026-06-23T08:01:32Z","snapshot_observed_at":"2026-07-06T23:56:59.635251Z","submitted_at":"2026-06-20T14:10:59Z","title":"Anticipating the Optimism Gap: Predicting Distribution-Shift Degradation of RF-Impairment Detectors from In-Distribution Statistics","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-26T11:34:53.405582Z"},"links":{"citing_paper":"/paper/2606.22054"},"observation_digest":"sha256:0b8f3b23dfe28caf645df8a925756972b90deeb0c899e1854a9df8abda6de5e2","observation_id":"62963a91-43fb-4e76-a5d3-ba8674f5a42f","resolution":{"observed_at":"2026-06-26T11:34:53.405582Z","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":"2026-06-26T11:34:53.405582Z","title":"Are labels always necessary for classifier accuracy evaluation?","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.22054","last_updated":"2026-06-23T08:01:32Z","snapshot_observed_at":"2026-07-06T23:56:59.635251Z","submitted_at":"2026-06-20T14:10:59Z","title":"Anticipating the Optimism Gap: Predicting Distribution-Shift Degradation of RF-Impairment Detectors from In-Distribution Statistics","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-26T11:34:53.405582Z"},"links":{"citing_paper":"/paper/2606.22054"},"observation_digest":"sha256:4421a869fdde3d8fe713a4a56d5dece7ba9a03d9f8fcc8aef99c18afa8ad928b","observation_id":"96413d9e-107f-47f0-9298-ef3e521e35f1","resolution":{"observed_at":"2026-06-26T11:34:53.405582Z","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":"2026-06-26T11:34:53.405582Z","title":"Predicting with confidence on unseen distributions,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.22054","last_updated":"2026-06-23T08:01:32Z","snapshot_observed_at":"2026-07-06T23:56:59.635251Z","submitted_at":"2026-06-20T14:10:59Z","title":"Anticipating the Optimism Gap: Predicting Distribution-Shift Degradation of RF-Impairment Detectors from In-Distribution Statistics","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-26T11:34:53.405582Z"},"links":{"citing_paper":"/paper/2606.22054"},"observation_digest":"sha256:ba0dab63e8ddb31340fbeea572ab5afd5a1d2c865804560166fcd22f70d19747","observation_id":"b76caeb7-b1bd-49eb-96d9-a19ea5278d83","resolution":{"observed_at":"2026-06-26T11:34:53.405582Z","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":"2026-06-26T11:34:53.405582Z","title":"Can you trust your model’s uncertainty? Evaluating predictive uncertainty under dataset shift,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.22054","last_updated":"2026-06-23T08:01:32Z","snapshot_observed_at":"2026-07-06T23:56:59.635251Z","submitted_at":"2026-06-20T14:10:59Z","title":"Anticipating the Optimism Gap: Predicting Distribution-Shift Degradation of RF-Impairment Detectors from In-Distribution Statistics","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-26T11:34:53.405582Z"},"links":{"citing_paper":"/paper/2606.22054"},"observation_digest":"sha256:e93ca3df3569a23beda2a80e9f2059ece11580d3f7fe5b1e05aad88a5c0fe6f4","observation_id":"2dab9b1a-3a0c-4566-be7e-c7cf4b466dea","resolution":{"observed_at":"2026-06-26T11:34:53.405582Z","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":"2026-06-26T11:34:53.405582Z","title":"Do ImageNet classifiers generalize to ImageNet?","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.22054","last_updated":"2026-06-23T08:01:32Z","snapshot_observed_at":"2026-07-06T23:56:59.635251Z","submitted_at":"2026-06-20T14:10:59Z","title":"Anticipating the Optimism Gap: Predicting Distribution-Shift Degradation of RF-Impairment Detectors from In-Distribution Statistics","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-26T11:34:53.405582Z"},"links":{"citing_paper":"/paper/2606.22054"},"observation_digest":"sha256:d3226d9486d65992f143144ae4cf71a318a811db1949dc951ed91af420f88404","observation_id":"6f028f1b-cbc1-41e4-9525-f1305b1fa7ae","resolution":{"observed_at":"2026-06-26T11:34:53.405582Z","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":"2026-06-26T11:34:53.405582Z","title":"Measuring robustness to natural distribution shifts in image classification,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.22054","last_updated":"2026-06-23T08:01:32Z","snapshot_observed_at":"2026-07-06T23:56:59.635251Z","submitted_at":"2026-06-20T14:10:59Z","title":"Anticipating the Optimism Gap: Predicting Distribution-Shift Degradation of RF-Impairment Detectors from In-Distribution Statistics","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-26T11:34:53.405582Z"},"links":{"citing_paper":"/paper/2606.22054"},"observation_digest":"sha256:bdb39ded2936e766ccbf0a117d806305121fec2938cfdc0b77f55e7e4b9584bf","observation_id":"30d867ec-756b-4d45-80ca-1fd93c55cd8d","resolution":{"observed_at":"2026-06-26T11:34:53.405582Z","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":"2026-06-26T11:34:53.405582Z","title":"WILDS: a benchmark of in-the-wild distribution shifts,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.22054","last_updated":"2026-06-23T08:01:32Z","snapshot_observed_at":"2026-07-06T23:56:59.635251Z","submitted_at":"2026-06-20T14:10:59Z","title":"Anticipating the Optimism Gap: Predicting Distribution-Shift Degradation of RF-Impairment Detectors from In-Distribution Statistics","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-26T11:34:53.405582Z"},"links":{"citing_paper":"/paper/2606.22054"},"observation_digest":"sha256:5c34adab7433663e4cc257d97b0d2fb2512007b524cf94580170b302ee4fbe63","observation_id":"37f320ef-90d0-4e85-b88c-ba3381ab599b","resolution":{"observed_at":"2026-06-26T11:34:53.405582Z","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":"2026-06-26T11:34:53.405582Z","title":"Benchmarking neural network robust- ness to common corruptions and perturbations,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.22054","last_updated":"2026-06-23T08:01:32Z","snapshot_observed_at":"2026-07-06T23:56:59.635251Z","submitted_at":"2026-06-20T14:10:59Z","title":"Anticipating the Optimism Gap: Predicting Distribution-Shift Degradation of RF-Impairment Detectors from In-Distribution Statistics","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-26T11:34:53.405582Z"},"links":{"citing_paper":"/paper/2606.22054"},"observation_digest":"sha256:5446bd1c1465ffb893baf033c4c2ef3f5092bca922c2900b236fe84694acfbea","observation_id":"a5c5cebb-50dc-4108-84b0-f6450cd8295d","resolution":{"observed_at":"2026-06-26T11:34:53.405582Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.23775","last_updated":"2025-03-31T06:51:52Z","snapshot_observed_at":"2026-08-12T22:05:05.691555Z","submitted_at":"2025-03-31T06:51:52Z","title":"Evaluation of (Un-)Supervised Machine Learning Methods for GNSS Interference Classification with Real-World Data Discrepancies","version":1},"cited_work":{"arxiv_id":"2503.23775","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2503.23775","snapshot_observed_at":"2026-07-04T08:29:42.081949Z","title":"Evaluation of (un-)supervised ma- chine learning methods for GNSS interference classification with real- world data discrepancies,","venue":null,"work_id":"0897f363-b839-4ed8-ab6d-c786a6db2201","year":2024},"citing_paper":{"arxiv_id":"2606.22054","last_updated":"2026-06-23T08:01:32Z","snapshot_observed_at":"2026-07-06T23:56:59.635251Z","submitted_at":"2026-06-20T14:10:59Z","title":"Anticipating the Optimism Gap: Predicting Distribution-Shift Degradation of RF-Impairment Detectors from In-Distribution Statistics","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-26T11:34:53.405582Z"},"links":{"cited_paper":"/paper/2503.23775","citing_paper":"/paper/2606.22054"},"observation_digest":"sha256:86a020239cdac2fd140b84cbdaee528f61dc972bf3f62a1e18af95f8b701f926","observation_id":"1b4fd2a3-1705-45d5-bf6f-f1af9edf7f51","resolution":{"observed_at":"2026-07-04T08:29:42.083434Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T11:34:53.405582Z","title":"Recent advances on jamming and spoofing detection in GNSS,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.22054","last_updated":"2026-06-23T08:01:32Z","snapshot_observed_at":"2026-07-06T23:56:59.635251Z","submitted_at":"2026-06-20T14:10:59Z","title":"Anticipating the Optimism Gap: Predicting Distribution-Shift Degradation of RF-Impairment Detectors from In-Distribution Statistics","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-06-26T11:34:53.405582Z"},"links":{"citing_paper":"/paper/2606.22054"},"observation_digest":"sha256:d11e28aaf2a210726ce7b4233288405602939f0e4fb8d5adc1b6150b8bbb49fd","observation_id":"81e1d420-95c7-4d1b-9a0b-fac9a58a1136","resolution":{"observed_at":"2026-06-26T11:34:53.405582Z","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":"2026-06-26T11:34:53.405582Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2606.22054","last_updated":"2026-06-23T08:01:32Z","snapshot_observed_at":"2026-07-06T23:56:59.635251Z","submitted_at":"2026-06-20T14:10:59Z","title":"Anticipating the Optimism Gap: Predicting Distribution-Shift Degradation of RF-Impairment Detectors from In-Distribution Statistics","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-06-26T11:34:53.405582Z"},"links":{"citing_paper":"/paper/2606.22054"},"observation_digest":"sha256:63e993a54df6110f0c61499bf3cfd8174f2df94f4f23c35f65646a720d5dc695","observation_id":"a5c865aa-cad9-4e74-970f-1fb34370febd","resolution":{"observed_at":"2026-06-26T11:34:53.405582Z","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":"2026-06-26T11:34:53.405582Z","title":"GNSS spoofing and detection,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2606.22054","last_updated":"2026-06-23T08:01:32Z","snapshot_observed_at":"2026-07-06T23:56:59.635251Z","submitted_at":"2026-06-20T14:10:59Z","title":"Anticipating the Optimism Gap: Predicting Distribution-Shift Degradation of RF-Impairment Detectors from In-Distribution Statistics","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-26T11:34:53.405582Z"},"links":{"citing_paper":"/paper/2606.22054"},"observation_digest":"sha256:58fb2171ac25a2d0b20c8426953e415e4f7966e80d26cacfd5a59fdb7bb1d53a","observation_id":"b575273d-a14b-4332-bfdc-56b91a0ef2f0","resolution":{"observed_at":"2026-06-26T11:34:53.405582Z","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":"2026-06-26T11:34:53.405582Z","title":"Dovis,GNSS Interference Threats and Countermeasures","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2606.22054","last_updated":"2026-06-23T08:01:32Z","snapshot_observed_at":"2026-07-06T23:56:59.635251Z","submitted_at":"2026-06-20T14:10:59Z","title":"Anticipating the Optimism Gap: Predicting Distribution-Shift Degradation of RF-Impairment Detectors from In-Distribution Statistics","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-06-26T11:34:53.405582Z"},"links":{"citing_paper":"/paper/2606.22054"},"observation_digest":"sha256:a9e60d0a5752bbc955c9b352090ef8136be29c5d1a8c16664cd03ba86923d566","observation_id":"8b642727-d542-4444-8ee8-56a7b80c9d16","resolution":{"observed_at":"2026-06-26T11:34:53.405582Z","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":"2026-06-26T11:34:53.405582Z","title":"Who’s afraid of the spoofer? GPS/GNSS spoofing detection via automatic gain control (AGC),","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2606.22054","last_updated":"2026-06-23T08:01:32Z","snapshot_observed_at":"2026-07-06T23:56:59.635251Z","submitted_at":"2026-06-20T14:10:59Z","title":"Anticipating the Optimism Gap: Predicting Distribution-Shift Degradation of RF-Impairment Detectors from In-Distribution Statistics","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-06-26T11:34:53.405582Z"},"links":{"citing_paper":"/paper/2606.22054"},"observation_digest":"sha256:ec5bc7e9953e86db40d5a4f1086f1aabac3c50c27d875f6de0f3cd868667542b","observation_id":"e4f15bbd-cea2-4aa9-808b-2e57f7be2d4a","resolution":{"observed_at":"2026-06-26T11:34:53.405582Z","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":"2026-06-26T11:34:53.405582Z","title":"The meaning and use of the area under a receiver operating characteristic (ROC) curve,","venue":null,"work_id":null,"year":1982},"citing_paper":{"arxiv_id":"2606.22054","last_updated":"2026-06-23T08:01:32Z","snapshot_observed_at":"2026-07-06T23:56:59.635251Z","submitted_at":"2026-06-20T14:10:59Z","title":"Anticipating the Optimism Gap: Predicting Distribution-Shift Degradation of RF-Impairment Detectors from In-Distribution Statistics","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-06-26T11:34:53.405582Z"},"links":{"citing_paper":"/paper/2606.22054"},"observation_digest":"sha256:be94826938da96ba7d32e1a736bbc9f010fd6e985b46c94f0839a12989d00950","observation_id":"e4a54775-6b8d-494f-bf78-62412270a897","resolution":{"observed_at":"2026-06-26T11:34:53.405582Z","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":"2026-06-26T11:34:53.405582Z","title":"The use of the area under the ROC curve in the evaluation of machine learning algorithms,","venue":null,"work_id":null,"year":1997},"citing_paper":{"arxiv_id":"2606.22054","last_updated":"2026-06-23T08:01:32Z","snapshot_observed_at":"2026-07-06T23:56:59.635251Z","submitted_at":"2026-06-20T14:10:59Z","title":"Anticipating the Optimism Gap: Predicting Distribution-Shift Degradation of RF-Impairment Detectors from In-Distribution Statistics","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-06-26T11:34:53.405582Z"},"links":{"citing_paper":"/paper/2606.22054"},"observation_digest":"sha256:0446073a06e9f5322e8735946f2f0689ce3a397e361d18f136b0c43138570187","observation_id":"7c430535-4943-4d62-8a95-1f7dbe9527bb","resolution":{"observed_at":"2026-06-26T11:34:53.405582Z","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":"2026-06-26T11:34:53.405582Z","title":"Comparing the areas under two or more correlated ROC curves: a nonparametric approach,","venue":null,"work_id":null,"year":1988},"citing_paper":{"arxiv_id":"2606.22054","last_updated":"2026-06-23T08:01:32Z","snapshot_observed_at":"2026-07-06T23:56:59.635251Z","submitted_at":"2026-06-20T14:10:59Z","title":"Anticipating the Optimism Gap: Predicting Distribution-Shift Degradation of RF-Impairment Detectors from In-Distribution Statistics","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-06-26T11:34:53.405582Z"},"links":{"citing_paper":"/paper/2606.22054"},"observation_digest":"sha256:fdccaf6d362d2ae8e68c60875f303640d9f628ca90046b650bd75133a81a1083","observation_id":"7057daf8-30ed-4573-b346-3dc657403de4","resolution":{"observed_at":"2026-06-26T11:34:53.405582Z","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":"2026-06-26T11:34:53.405582Z","title":"Fast implementation of DeLong’s algorithm for comparing the areas under correlated ROC curves,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2606.22054","last_updated":"2026-06-23T08:01:32Z","snapshot_observed_at":"2026-07-06T23:56:59.635251Z","submitted_at":"2026-06-20T14:10:59Z","title":"Anticipating the Optimism Gap: Predicting Distribution-Shift Degradation of RF-Impairment Detectors from In-Distribution Statistics","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-06-26T11:34:53.405582Z"},"links":{"citing_paper":"/paper/2606.22054"},"observation_digest":"sha256:8ee0740cd60978866d182b08362ab9255f7475d29b01d7481bfc52989454b9c4","observation_id":"474652b0-c1fb-4dd4-86c7-b6aa3ff0fcea","resolution":{"observed_at":"2026-06-26T11:34:53.405582Z","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":"2026-06-26T11:34:53.405582Z","title":"Efron and R","venue":null,"work_id":null,"year":1993},"citing_paper":{"arxiv_id":"2606.22054","last_updated":"2026-06-23T08:01:32Z","snapshot_observed_at":"2026-07-06T23:56:59.635251Z","submitted_at":"2026-06-20T14:10:59Z","title":"Anticipating the Optimism Gap: Predicting Distribution-Shift Degradation of RF-Impairment Detectors from In-Distribution Statistics","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-06-26T11:34:53.405582Z"},"links":{"citing_paper":"/paper/2606.22054"},"observation_digest":"sha256:5069cc53f06fff3638058cba316208bb4b4fa658fcb488064a981416f80ec5a3","observation_id":"fac36e59-9d71-499b-824c-d1a39e7e93f3","resolution":{"observed_at":"2026-06-26T11:34:53.405582Z","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":"2026-06-26T11:34:53.405582Z","title":"Ridge regression: biased estimation for nonorthogonal problems,","venue":null,"work_id":null,"year":1970},"citing_paper":{"arxiv_id":"2606.22054","last_updated":"2026-06-23T08:01:32Z","snapshot_observed_at":"2026-07-06T23:56:59.635251Z","submitted_at":"2026-06-20T14:10:59Z","title":"Anticipating the Optimism Gap: Predicting Distribution-Shift Degradation of RF-Impairment Detectors from In-Distribution Statistics","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-06-26T11:34:53.405582Z"},"links":{"citing_paper":"/paper/2606.22054"},"observation_digest":"sha256:27e8c020d015745ba20f9ddda8e94bb8f4a70717576626854ea1bdcb3d97db90","observation_id":"762c1698-74e9-4b4d-bfbe-86fe7f4d029a","resolution":{"observed_at":"2026-06-26T11:34:53.405582Z","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":"10.5281/zenodo.20528627","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Kshana: an open, reproducible PNT-resilience simulator,","venue":"Zenodo (CERN European Organization for Nuclear Research)","work_id":"3329313e-feb1-4976-bb49-4721569e5440","year":2026},"citing_paper":{"arxiv_id":"2606.22054","last_updated":"2026-06-23T08:01:32Z","snapshot_observed_at":"2026-07-06T23:56:59.635251Z","submitted_at":"2026-06-20T14:10:59Z","title":"Anticipating the Optimism Gap: Predicting Distribution-Shift Degradation of RF-Impairment Detectors from In-Distribution Statistics","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-06-26T11:34:53.405582Z"},"links":{"citing_paper":"/paper/2606.22054"},"observation_digest":"sha256:541fffc1f3d809b27a04cb16828fc29fb2e6166bd268e335973a79ca2ed3f5ab","observation_id":"b74f1639-4e95-478e-b6b3-834ebf7c6ec7","resolution":{"observed_at":"2026-06-26T11:39:24.633491Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2024.110302","doi":"10.1016/j.dib.2024.110302","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"GNSS interference and spoofing dataset,","venue":"Data in Brief","work_id":"364179a0-4af5-422e-b2ac-e96ad5dac969","year":2024},"citing_paper":{"arxiv_id":"2606.22054","last_updated":"2026-06-23T08:01:32Z","snapshot_observed_at":"2026-07-06T23:56:59.635251Z","submitted_at":"2026-06-20T14:10:59Z","title":"Anticipating the Optimism Gap: Predicting Distribution-Shift Degradation of RF-Impairment Detectors from In-Distribution Statistics","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-06-26T11:34:53.405582Z"},"links":{"citing_paper":"/paper/2606.22054"},"observation_digest":"sha256:f84d8db8947b8994b765ed66c87e5a24866be31d86493fb67d0488ac44be242c","observation_id":"e58b5c69-6b65-466e-9560-0d8df2e4e325","resolution":{"observed_at":"2026-06-26T11:39:24.638182Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.5281/zenodo.15910563","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"GNSS dataset under jam- ming, spoofing, and meaconing conditions (Jammertest 2024),","venue":"Zenodo (CERN European Organization for Nuclear Research)","work_id":"a82f3a6c-b9e1-49ba-847d-fc7e5c714864","year":2024},"citing_paper":{"arxiv_id":"2606.22054","last_updated":"2026-06-23T08:01:32Z","snapshot_observed_at":"2026-07-06T23:56:59.635251Z","submitted_at":"2026-06-20T14:10:59Z","title":"Anticipating the Optimism Gap: Predicting Distribution-Shift Degradation of RF-Impairment Detectors from In-Distribution Statistics","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-06-26T11:34:53.405582Z"},"links":{"citing_paper":"/paper/2606.22054"},"observation_digest":"sha256:a0ea1aa79133aae895f723e9ef0fc4755d960c7026e078a196892ebdc39ff845","observation_id":"8f8cb4bd-bf36-433b-bd31-257344db5bed","resolution":{"observed_at":"2026-06-26T11:39:24.638265Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.7294/se62-7x13","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"SatGrid: realtime genuine and spoofing traces of GPS signals,","venue":"Figshare","work_id":"f9adead5-c36e-4468-9906-636c860f0df4","year":2020},"citing_paper":{"arxiv_id":"2606.22054","last_updated":"2026-06-23T08:01:32Z","snapshot_observed_at":"2026-07-06T23:56:59.635251Z","submitted_at":"2026-06-20T14:10:59Z","title":"Anticipating the Optimism Gap: Predicting Distribution-Shift Degradation of RF-Impairment Detectors from In-Distribution Statistics","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-06-26T11:34:53.405582Z"},"links":{"citing_paper":"/paper/2606.22054"},"observation_digest":"sha256:d4c53b12ed39dd1fa37846e2cf140265480d53c3a0ea8c8eb82ccf9eb37760c4","observation_id":"756a7f61-a174-480e-b768-fa1f587299fa","resolution":{"observed_at":"2026-06-26T11:39:24.639918Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2606.22054","last_updated":"2026-06-23T08:01:32Z","latest_version":2,"primary_category":"eess.SP","snapshot_observed_at":"2026-07-06T23:56:59.635251Z","submitted_at":"2026-06-20T14:10:59Z","title":"Anticipating the Optimism Gap: Predicting Distribution-Shift Degradation of RF-Impairment Detectors from In-Distribution Statistics"},"reference_resolution":{"displayed":27,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":22,"verified_exact":5,"verified_fuzzy":0},"total_outbound_references":27},"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":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:2606.22054."}