{"as_of":"2026-08-10T07:52:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:3995f2e25ff2a3e3a2261ad0d484c40ec598b8d256f22d1e159a22886714dedf","coverage":[{"denominator":50,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":50,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T11:15:04.199928Z","state":"measured"},{"denominator":50,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":50,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+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/2506.03037/citation-record","integrity":"/paper/2506.03037/integrity","json":"/paper/2506.03037/citation-record.json","paper":"/paper/2506.03037"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:15:10.886173Z","title":"Wiley Publications in Statistics, 1954","venue":null,"work_id":"7c49b778-eeaf-4032-8f8d-e5ffff00dd3a","year":1954},"citing_paper":{"arxiv_id":"2506.03037","last_updated":"2025-06-03T16:19:59Z","snapshot_observed_at":"2026-08-07T11:07:40.811572Z","submitted_at":"2025-06-03T16:19:59Z","title":"On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T11:15:00.099695Z"},"links":{"citing_paper":"/paper/2506.03037"},"observation_digest":"sha256:fc6426dda7bd0a5dddf5585c9591a69697a8f604a8113bcc5435786cab8509d0","observation_id":"fc4982a6-640a-4f28-8ee5-beb51215a8f9","resolution":{"observed_at":"2026-08-07T11:15:10.926297Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:15:10.721489Z","title":"John Wiley & Sons, 2009","venue":null,"work_id":"006ddf1d-84fe-47ea-91a2-430aea870580","year":2009},"citing_paper":{"arxiv_id":"2506.03037","last_updated":"2025-06-03T16:19:59Z","snapshot_observed_at":"2026-08-07T11:07:40.811572Z","submitted_at":"2025-06-03T16:19:59Z","title":"On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T11:15:00.144859Z"},"links":{"citing_paper":"/paper/2506.03037"},"observation_digest":"sha256:ce08136a2cd217ab427ed785c378ff0d52210acfac199232ce5a4f65bd91ed5e","observation_id":"403da893-58f6-443f-9f27-d251f4a8c066","resolution":{"observed_at":"2026-08-07T11:15:10.815169Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T11:15:00.216682Z","title":"Strictly proper scoring rules, prediction, and estimation","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2506.03037","last_updated":"2025-06-03T16:19:59Z","snapshot_observed_at":"2026-08-07T11:07:40.811572Z","submitted_at":"2025-06-03T16:19:59Z","title":"On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T11:15:00.216682Z"},"links":{"citing_paper":"/paper/2506.03037"},"observation_digest":"sha256:aa81e0ac558d3db29f2169dc0a37c0a9267926ac1889117e48b578dcfec90922","observation_id":"6f1f7ba9-a846-4e91-9265-0f003689494d","resolution":{"observed_at":"2026-08-07T11:15:00.216682Z","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":"2026-08-07T11:15:00.292884Z","title":"Rajendra Acharya, Vladimir Makarenkov, and Saeid Nahavandi","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.03037","last_updated":"2025-06-03T16:19:59Z","snapshot_observed_at":"2026-08-07T11:07:40.811572Z","submitted_at":"2025-06-03T16:19:59Z","title":"On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T11:15:00.292884Z"},"links":{"citing_paper":"/paper/2506.03037"},"observation_digest":"sha256:aa94e4951c215aaa39b87cf8c86363b8225e6260a0c1d7e10ae59e3fd5ae80aa","observation_id":"fcacf603-d964-4e22-b423-e54945e7348f","resolution":{"observed_at":"2026-08-07T11:15:00.292884Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1612.01474","last_updated":"2017-11-04T01:33:43Z","snapshot_observed_at":"2026-07-06T05:21:24.357721Z","submitted_at":"2016-12-05T18:54:43Z","title":"Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1612.01474","snapshot_observed_at":"2026-08-07T11:15:00.406660Z","title":"Simple and scalable predictive uncertainty estimation using deep ensembles, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.03037","last_updated":"2025-06-03T16:19:59Z","snapshot_observed_at":"2026-08-07T11:07:40.811572Z","submitted_at":"2025-06-03T16:19:59Z","title":"On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T11:15:00.406660Z"},"links":{"cited_paper":"/paper/1612.01474","citing_paper":"/paper/2506.03037"},"observation_digest":"sha256:01c6fd2af527fbc1066e76b441108e3af8136c31d1e334d6e1931f8406151f85","observation_id":"b7b80db3-3cf3-424f-a52f-b1410e39ba63","resolution":{"observed_at":"2026-08-07T11:15:00.406660Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1505.05424","last_updated":"2015-05-21T14:07:23Z","snapshot_observed_at":"2026-08-07T14:53:26.409379Z","submitted_at":"2015-05-20T15:39:48Z","title":"Weight Uncertainty in Neural Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1505.05424","snapshot_observed_at":"2026-08-07T11:15:00.450083Z","title":"Weight uncertainty in neural networks, 2015","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2506.03037","last_updated":"2025-06-03T16:19:59Z","snapshot_observed_at":"2026-08-07T11:07:40.811572Z","submitted_at":"2025-06-03T16:19:59Z","title":"On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T11:15:00.450083Z"},"links":{"cited_paper":"/paper/1505.05424","citing_paper":"/paper/2506.03037"},"observation_digest":"sha256:450a392ea89d1d5fa85539b9b578fb98838671540bff441e48d3502b25b606c9","observation_id":"65326fce-d8fb-445d-aff3-a71f6dabe052","resolution":{"observed_at":"2026-08-07T11:15:00.450083Z","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":"2026-08-07T11:15:00.493620Z","title":"Springer, 2005","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2506.03037","last_updated":"2025-06-03T16:19:59Z","snapshot_observed_at":"2026-08-07T11:07:40.811572Z","submitted_at":"2025-06-03T16:19:59Z","title":"On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T11:15:00.493620Z"},"links":{"citing_paper":"/paper/2506.03037"},"observation_digest":"sha256:e47b0f5495dc661be5e08fb0e6dedd474e4c3063b9dd3e76f1c3e8e3a5b48e8e","observation_id":"55099150-18a9-4193-80ba-10e8e19a270e","resolution":{"observed_at":"2026-08-07T11:15:00.493620Z","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":"2026-08-07T11:15:00.564436Z","title":"The frontier of simulation-based inference","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.03037","last_updated":"2025-06-03T16:19:59Z","snapshot_observed_at":"2026-08-07T11:07:40.811572Z","submitted_at":"2025-06-03T16:19:59Z","title":"On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T11:15:00.564436Z"},"links":{"citing_paper":"/paper/2506.03037"},"observation_digest":"sha256:bf941c7d7926477202554bd48456172bad24f951e9e2b62432761eae8c57959d","observation_id":"9ca8bad9-e131-413f-85c9-72bebc85d39a","resolution":{"observed_at":"2026-08-07T11:15:00.564436Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:15:10.577364Z","title":"Science and statistics.Journal of the American Statistical Association, 71 (356):791–799, 1976","venue":null,"work_id":"b0267685-02c0-44ef-8c5b-94460dee2e4e","year":1976},"citing_paper":{"arxiv_id":"2506.03037","last_updated":"2025-06-03T16:19:59Z","snapshot_observed_at":"2026-08-07T11:07:40.811572Z","submitted_at":"2025-06-03T16:19:59Z","title":"On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T11:15:00.620946Z"},"links":{"citing_paper":"/paper/2506.03037"},"observation_digest":"sha256:7a13ac610f4149fc8b2884828f0e909b4b050938ae3d0b83382bf287f7dae964","observation_id":"33323193-0ea6-43a5-b923-7e1b22f4e46c","resolution":{"observed_at":"2026-08-07T11:15:10.630121Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:15:10.404547Z","title":"Oberkampf and Christopher J","venue":null,"work_id":"aff5cd8e-2005-496c-9e8c-b7baaac11783","year":2010},"citing_paper":{"arxiv_id":"2506.03037","last_updated":"2025-06-03T16:19:59Z","snapshot_observed_at":"2026-08-07T11:07:40.811572Z","submitted_at":"2025-06-03T16:19:59Z","title":"On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T11:15:00.762325Z"},"links":{"citing_paper":"/paper/2506.03037"},"observation_digest":"sha256:f8b2bca27ef291009a9c8b31cf559419dd7b8b545f683c3d6c604e1ad369cf32","observation_id":"2830c1f8-1c37-4a25-bbe6-8820ca13f4d8","resolution":{"observed_at":"2026-08-07T11:15:10.458307Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T11:15:00.823510Z","title":"Cambridge university press, 2014","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2506.03037","last_updated":"2025-06-03T16:19:59Z","snapshot_observed_at":"2026-08-07T11:07:40.811572Z","submitted_at":"2025-06-03T16:19:59Z","title":"On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T11:15:00.823510Z"},"links":{"citing_paper":"/paper/2506.03037"},"observation_digest":"sha256:418365a233bd0a64dea4ae03f12a3642d09e3ec4fb9011bfdbb2a2636419211f","observation_id":"a786edf7-607d-4893-83b4-ae12c1b2f7d8","resolution":{"observed_at":"2026-08-07T11:15:00.823510Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:15:10.245471Z","title":"Statistical inference.Australia: Duxbury/Thomson Learning, 2002","venue":null,"work_id":"daacebca-ba8b-4aea-bd3e-366e2bbfb18e","year":2002},"citing_paper":{"arxiv_id":"2506.03037","last_updated":"2025-06-03T16:19:59Z","snapshot_observed_at":"2026-08-07T11:07:40.811572Z","submitted_at":"2025-06-03T16:19:59Z","title":"On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T11:15:00.904118Z"},"links":{"citing_paper":"/paper/2506.03037"},"observation_digest":"sha256:8a2a6491c2f5816c3cbcc2c33d81b05ed7af175e3f067cbf8db5d998da747a15","observation_id":"e7c77ef0-1b5e-4c64-9273-97c6b5b4af44","resolution":{"observed_at":"2026-08-07T11:15:10.306682Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:15:10.088920Z","title":"Bayesian data analysis, 3rd edn london, 2013","venue":null,"work_id":"52323879-e017-40d3-94ff-78066d052501","year":2013},"citing_paper":{"arxiv_id":"2506.03037","last_updated":"2025-06-03T16:19:59Z","snapshot_observed_at":"2026-08-07T11:07:40.811572Z","submitted_at":"2025-06-03T16:19:59Z","title":"On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T11:15:00.965030Z"},"links":{"citing_paper":"/paper/2506.03037"},"observation_digest":"sha256:ddb5b79f582b50eda3ecb75a19f37e5320faee3930df0af9cd86223004e0b256","observation_id":"a0d45284-eab8-44c4-9583-f2924846c520","resolution":{"observed_at":"2026-08-07T11:15:10.135297Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T11:15:01.072128Z","title":"Conformalized quantile regression","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.03037","last_updated":"2025-06-03T16:19:59Z","snapshot_observed_at":"2026-08-07T11:07:40.811572Z","submitted_at":"2025-06-03T16:19:59Z","title":"On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T11:15:01.072128Z"},"links":{"citing_paper":"/paper/2506.03037"},"observation_digest":"sha256:269aff48f0b710a6b503c42ca8e6af36807b504ad194bfa687cddda5ae6bdc72","observation_id":"eff8ab48-b34a-43cd-b015-eee09b685aff","resolution":{"observed_at":"2026-08-07T11:15:01.072128Z","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":"2026-08-07T11:15:01.121764Z","title":"The limits of distribution-free conditional predictive inference.Information and Inference: A Journal of the IMA, 10(2):455–482, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.03037","last_updated":"2025-06-03T16:19:59Z","snapshot_observed_at":"2026-08-07T11:07:40.811572Z","submitted_at":"2025-06-03T16:19:59Z","title":"On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T11:15:01.121764Z"},"links":{"citing_paper":"/paper/2506.03037"},"observation_digest":"sha256:252efc20dedf48ff3e0246f82350ec411833bf15718e4b9401ca80cea5353d15","observation_id":"477a43a4-7e11-4b68-9093-23210a3779d6","resolution":{"observed_at":"2026-08-07T11:15:01.121764Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:15:09.906537Z","title":"Indirect inference.Journal of applied econometrics, 8(S1):S85–S118, 1993","venue":null,"work_id":"5725daf9-ac83-4010-b6cd-0a8aef79b888","year":1993},"citing_paper":{"arxiv_id":"2506.03037","last_updated":"2025-06-03T16:19:59Z","snapshot_observed_at":"2026-08-07T11:07:40.811572Z","submitted_at":"2025-06-03T16:19:59Z","title":"On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T11:15:01.184341Z"},"links":{"citing_paper":"/paper/2506.03037"},"observation_digest":"sha256:efee706018ccbf896b7d58087726c2f97d4eda05d6a549ed4d1b775c230fc811","observation_id":"ece12f3e-5e7c-4e0f-a7db-1ab9a4716cda","resolution":{"observed_at":"2026-08-07T11:15:09.961135Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:15:09.750152Z","title":"Springer, 1977","venue":null,"work_id":"3acf6f5b-1522-4131-a5a5-a17aa43beb3c","year":1977},"citing_paper":{"arxiv_id":"2506.03037","last_updated":"2025-06-03T16:19:59Z","snapshot_observed_at":"2026-08-07T11:07:40.811572Z","submitted_at":"2025-06-03T16:19:59Z","title":"On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T11:15:01.256537Z"},"links":{"citing_paper":"/paper/2506.03037"},"observation_digest":"sha256:e2da78a513247d88aea9fedf3b733565b1312e4ee0bc2da8e9b2cccabff4d7db","observation_id":"fd34c422-17a9-4402-ba65-c8835e11c835","resolution":{"observed_at":"2026-08-07T11:15:09.804818Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:15:09.612489Z","title":"Springer, 2005","venue":null,"work_id":"b872653a-cf6a-43a0-8a2d-4b2620291cca","year":2005},"citing_paper":{"arxiv_id":"2506.03037","last_updated":"2025-06-03T16:19:59Z","snapshot_observed_at":"2026-08-07T11:07:40.811572Z","submitted_at":"2025-06-03T16:19:59Z","title":"On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T11:15:01.316045Z"},"links":{"citing_paper":"/paper/2506.03037"},"observation_digest":"sha256:a7b98b8b2df991911ebbb6400d9c0b08d2ba1ebf9b111214f200e588d25c9323","observation_id":"854ee82d-f479-4710-81e9-3789837ab595","resolution":{"observed_at":"2026-08-07T11:15:09.687398Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:15:09.457112Z","title":"Fastϵ -free inference of simulation models with bayesian conditional density estimation","venue":null,"work_id":"b0218365-57fc-4647-b9fa-1ceb49f2487e","year":2016},"citing_paper":{"arxiv_id":"2506.03037","last_updated":"2025-06-03T16:19:59Z","snapshot_observed_at":"2026-08-07T11:07:40.811572Z","submitted_at":"2025-06-03T16:19:59Z","title":"On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T11:15:01.408340Z"},"links":{"citing_paper":"/paper/2506.03037"},"observation_digest":"sha256:7e458dbaa9ab5f2008d99716934e335e7ee1393697a22d4a5e09e634e8be5608","observation_id":"e07b9a2e-fb96-4c42-a11a-7e5e5a42aa04","resolution":{"observed_at":"2026-08-07T11:15:09.525707Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:15:09.266246Z","title":"Sequential neural likelihood: Fast likelihood-free inference with autoregressive flows","venue":null,"work_id":"0efe68d2-7be5-491c-b5c3-46646491ae16","year":2019},"citing_paper":{"arxiv_id":"2506.03037","last_updated":"2025-06-03T16:19:59Z","snapshot_observed_at":"2026-08-07T11:07:40.811572Z","submitted_at":"2025-06-03T16:19:59Z","title":"On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T11:15:01.452957Z"},"links":{"citing_paper":"/paper/2506.03037"},"observation_digest":"sha256:58e34c142eb0025904ead9957333ca03d6cd2d340df7de8444ea11013f14c1c4","observation_id":"eda58bab-f948-4a21-98fd-88abaebedfa6","resolution":{"observed_at":"2026-08-07T11:15:09.392338Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1903.04057","last_updated":"2020-06-26T08:15:43Z","snapshot_observed_at":"2026-07-06T07:38:18.008292Z","submitted_at":"2019-03-10T20:51:02Z","title":"Likelihood-free MCMC with Amortized Approximate Ratio Estimators","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1903.04057","snapshot_observed_at":"2026-08-07T11:15:01.542661Z","title":"Likelihood-free mcmc with amortized approximate ratio estimators, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.03037","last_updated":"2025-06-03T16:19:59Z","snapshot_observed_at":"2026-08-07T11:07:40.811572Z","submitted_at":"2025-06-03T16:19:59Z","title":"On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T11:15:01.542661Z"},"links":{"cited_paper":"/paper/1903.04057","citing_paper":"/paper/2506.03037"},"observation_digest":"sha256:9231bb66028ed8a84323879633d63c7412eaf778a44857f3232fab1935dd2c21","observation_id":"aaec77f8-6899-4320-bdea-1b43d224fb9c","resolution":{"observed_at":"2026-08-07T11:15:01.542661Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:15:09.023289Z","title":"Transmission of Justification and Warrant","venue":null,"work_id":"7e5e0a1d-191e-4480-ad15-f83edb017108","year":2023},"citing_paper":{"arxiv_id":"2506.03037","last_updated":"2025-06-03T16:19:59Z","snapshot_observed_at":"2026-08-07T11:07:40.811572Z","submitted_at":"2025-06-03T16:19:59Z","title":"On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T11:15:01.609381Z"},"links":{"citing_paper":"/paper/2506.03037"},"observation_digest":"sha256:f5d3cafafdf235ef10b1904d72086eb322cde02a61d685e81bede51a1de84d8b","observation_id":"af70a0c2-f39e-43c4-976b-ecbac597610e","resolution":{"observed_at":"2026-08-07T11:15:09.164985Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:15:08.755978Z","title":"Cambridge University Press, 2006","venue":null,"work_id":"19ad0e25-c75f-4260-b305-13bdcb0801fb","year":2006},"citing_paper":{"arxiv_id":"2506.03037","last_updated":"2025-06-03T16:19:59Z","snapshot_observed_at":"2026-08-07T11:07:40.811572Z","submitted_at":"2025-06-03T16:19:59Z","title":"On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T11:15:01.883796Z"},"links":{"citing_paper":"/paper/2506.03037"},"observation_digest":"sha256:0cbca7a2e3f202a9e59d387a2c7707be22ba08301affb356ff25e1f80cb6468a","observation_id":"d13f7863-7509-4da1-bef3-3daacfe984ec","resolution":{"observed_at":"2026-08-07T11:15:08.861713Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:15:08.475390Z","title":"Outline of a theory of statistical estimation based on the classical theory of probability.Philosophical Transactions of the Royal Society of London","venue":null,"work_id":"a66904ca-97ea-4cd3-867d-82762a7c24b1","year":1937},"citing_paper":{"arxiv_id":"2506.03037","last_updated":"2025-06-03T16:19:59Z","snapshot_observed_at":"2026-08-07T11:07:40.811572Z","submitted_at":"2025-06-03T16:19:59Z","title":"On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T11:15:01.947849Z"},"links":{"citing_paper":"/paper/2506.03037"},"observation_digest":"sha256:4f1572377e44ab639c42618b47bec87a694026d76591d414336a891afec3d6c3","observation_id":"cb091166-cc93-43d5-bf21-43b75aa55da8","resolution":{"observed_at":"2026-08-07T11:15:08.614397Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:15:08.214603Z","title":"University of Chicago Press, 1996","venue":null,"work_id":"95401c1f-a5b6-4b21-a5e6-8e1cc4116807","year":1996},"citing_paper":{"arxiv_id":"2506.03037","last_updated":"2025-06-03T16:19:59Z","snapshot_observed_at":"2026-08-07T11:07:40.811572Z","submitted_at":"2025-06-03T16:19:59Z","title":"On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T11:15:02.048812Z"},"links":{"citing_paper":"/paper/2506.03037"},"observation_digest":"sha256:8893e9c6b48ac532fe26984ddece4c109e642a75ca3865653e0943b4e480e3c5","observation_id":"590460ff-9673-4765-9207-15c592139858","resolution":{"observed_at":"2026-08-07T11:15:08.371887Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:15:07.994916Z","title":null,"venue":null,"work_id":"2ca0dd84-53a9-4f5a-8049-739700a43592","year":1958},"citing_paper":{"arxiv_id":"2506.03037","last_updated":"2025-06-03T16:19:59Z","snapshot_observed_at":"2026-08-07T11:07:40.811572Z","submitted_at":"2025-06-03T16:19:59Z","title":"On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T11:15:02.147312Z"},"links":{"citing_paper":"/paper/2506.03037"},"observation_digest":"sha256:d7f9a22c96f33db333064371abd8681c007446fcc409112a66fa690065812e8d","observation_id":"ce35e127-9e1a-4178-a740-5262b5beacfc","resolution":{"observed_at":"2026-08-07T11:15:08.116867Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:15:07.762124Z","title":"John Wiley & Sons, 2017","venue":null,"work_id":"e1d5c9a6-6356-4b91-9ba6-a84a7e61948d","year":2017},"citing_paper":{"arxiv_id":"2506.03037","last_updated":"2025-06-03T16:19:59Z","snapshot_observed_at":"2026-08-07T11:07:40.811572Z","submitted_at":"2025-06-03T16:19:59Z","title":"On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T11:15:02.219900Z"},"links":{"citing_paper":"/paper/2506.03037"},"observation_digest":"sha256:915fb9b215bb2c7cf4dd8f534bfceeb5053c0fe763148d8e8e1d6a99aeef0482","observation_id":"8d220400-6bfb-4dcf-9a84-6ebe5d1207c2","resolution":{"observed_at":"2026-08-07T11:15:07.906273Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:15:07.523623Z","title":"OUP Oxford, 2004","venue":null,"work_id":"2b0522da-3c40-45e6-a3fd-bcb853814986","year":2004},"citing_paper":{"arxiv_id":"2506.03037","last_updated":"2025-06-03T16:19:59Z","snapshot_observed_at":"2026-08-07T11:07:40.811572Z","submitted_at":"2025-06-03T16:19:59Z","title":"On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T11:15:02.293652Z"},"links":{"citing_paper":"/paper/2506.03037"},"observation_digest":"sha256:4b59ca6e94bb135fe084c7b6acfd4b7198d1fe5aba2f4f9fe5ec60c72108b16e","observation_id":"2a7912ee-2be4-4c3e-aa2c-7c209f1a1081","resolution":{"observed_at":"2026-08-07T11:15:07.644436Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:15:07.306446Z","title":"On fiducial inference.The Annals of Mathematical Statistics, 32(3):661–676, 1961","venue":null,"work_id":"0e2f5d48-3f58-4f5d-b78a-fb122a5423b9","year":1961},"citing_paper":{"arxiv_id":"2506.03037","last_updated":"2025-06-03T16:19:59Z","snapshot_observed_at":"2026-08-07T11:07:40.811572Z","submitted_at":"2025-06-03T16:19:59Z","title":"On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T11:15:02.386092Z"},"links":{"citing_paper":"/paper/2506.03037"},"observation_digest":"sha256:23b22bdf4b422169206cfe642f3b3b3c10a664c4310698fd27fc5dac0ce14982","observation_id":"287c163e-cea5-423f-b715-4bbff89baa1a","resolution":{"observed_at":"2026-08-07T11:15:07.417976Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:15:07.063755Z","title":"Fiducial inference, then and now","venue":null,"work_id":"69609a49-6bc0-4132-8bce-5a2db6025cbd","year":2024},"citing_paper":{"arxiv_id":"2506.03037","last_updated":"2025-06-03T16:19:59Z","snapshot_observed_at":"2026-08-07T11:07:40.811572Z","submitted_at":"2025-06-03T16:19:59Z","title":"On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T11:15:02.490529Z"},"links":{"citing_paper":"/paper/2506.03037"},"observation_digest":"sha256:386135d1aea58d34ec2faf845baeace480ad74b45427f26812d4a68ce09bd9f2","observation_id":"96f19034-ff9b-4b3b-892f-dd8653ee8e01","resolution":{"observed_at":"2026-08-07T11:15:07.190355Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:15:06.872518Z","title":"Generalized fiducial inference: A review and new results.Journal of the American Statistical Association, 111(515):1346–1361, 2016","venue":null,"work_id":"709ebe87-fe76-446b-828b-bd5b8858cfbd","year":2016},"citing_paper":{"arxiv_id":"2506.03037","last_updated":"2025-06-03T16:19:59Z","snapshot_observed_at":"2026-08-07T11:07:40.811572Z","submitted_at":"2025-06-03T16:19:59Z","title":"On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T11:15:02.590170Z"},"links":{"citing_paper":"/paper/2506.03037"},"observation_digest":"sha256:90ed013674d5a0f397a281ffac4aae71f718b0bbc56305f9e5b251aa3e1d743f","observation_id":"e7ec9364-f03b-4bca-ac41-33aeef146a26","resolution":{"observed_at":"2026-08-07T11:15:06.963219Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:15:06.667568Z","title":"Courier Corporation, 2013","venue":null,"work_id":"8481e60d-7d9e-4603-88d6-724159e97d0c","year":2013},"citing_paper":{"arxiv_id":"2506.03037","last_updated":"2025-06-03T16:19:59Z","snapshot_observed_at":"2026-08-07T11:07:40.811572Z","submitted_at":"2025-06-03T16:19:59Z","title":"On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T11:15:02.665261Z"},"links":{"citing_paper":"/paper/2506.03037"},"observation_digest":"sha256:e27d0ab0898b5449d023ce37e6f4f1668d1a19a34de0a7f32070635686f53026","observation_id":"cc7bbe50-3fd2-42ce-b920-b1c34ffd90d7","resolution":{"observed_at":"2026-08-07T11:15:06.747219Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:15:06.385788Z","title":"Citeseer, 1962","venue":null,"work_id":"a42e1c59-dadb-4240-8fd6-c62fd7b8c051","year":1962},"citing_paper":{"arxiv_id":"2506.03037","last_updated":"2025-06-03T16:19:59Z","snapshot_observed_at":"2026-08-07T11:07:40.811572Z","submitted_at":"2025-06-03T16:19:59Z","title":"On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T11:15:02.769605Z"},"links":{"citing_paper":"/paper/2506.03037"},"observation_digest":"sha256:2dc7e4daf0071307e2f3d794578caa1cba669ae4c0efe5fae40b52d688cb5b49","observation_id":"52aa0265-985e-4fd1-8227-d73f12ecb673","resolution":{"observed_at":"2026-08-07T11:15:06.477710Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T11:15:02.834533Z","title":"Cambridge university press, 2003","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2506.03037","last_updated":"2025-06-03T16:19:59Z","snapshot_observed_at":"2026-08-07T11:07:40.811572Z","submitted_at":"2025-06-03T16:19:59Z","title":"On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T11:15:02.834533Z"},"links":{"citing_paper":"/paper/2506.03037"},"observation_digest":"sha256:f526fbb02bae7213a2953754b3d1fcd3f4cb6207b4046bad2425f70c00787339","observation_id":"ee37418f-cb2c-46ce-a094-793c1d4175e0","resolution":{"observed_at":"2026-08-07T11:15:02.834533Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:15:06.142552Z","title":"Verification, validation, and predictive capability in computational engineering and physics.Appl","venue":null,"work_id":"49ae6455-dc72-4019-835c-eae0cbcbd340","year":2004},"citing_paper":{"arxiv_id":"2506.03037","last_updated":"2025-06-03T16:19:59Z","snapshot_observed_at":"2026-08-07T11:07:40.811572Z","submitted_at":"2025-06-03T16:19:59Z","title":"On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T11:15:02.938588Z"},"links":{"citing_paper":"/paper/2506.03037"},"observation_digest":"sha256:aa8f21c252496a46034d052be8a03e831d2daa91380ac2e8f10a1f887b8062ff","observation_id":"57a9009e-7c2a-4c1b-8fab-ded468b9bab0","resolution":{"observed_at":"2026-08-07T11:15:06.255143Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:15:05.944052Z","title":"Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods.Machine learning, 110(3):457–506, 2021","venue":null,"work_id":"d0ef7a4e-4979-4b35-9f6c-82bcae2446f3","year":2021},"citing_paper":{"arxiv_id":"2506.03037","last_updated":"2025-06-03T16:19:59Z","snapshot_observed_at":"2026-08-07T11:07:40.811572Z","submitted_at":"2025-06-03T16:19:59Z","title":"On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T11:15:03.005891Z"},"links":{"citing_paper":"/paper/2506.03037"},"observation_digest":"sha256:17b3ec2e83dba7589ca4b61a3f427e200c96f602857efbe468a253c062ca1433","observation_id":"001f2bc9-8323-401d-ba57-1e69419b3e2e","resolution":{"observed_at":"2026-08-07T11:15:06.008499Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T11:15:03.098185Z","title":"Aleatory or epistemic? does it matter?Structural safety, 31(2):105–112, 2009","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2506.03037","last_updated":"2025-06-03T16:19:59Z","snapshot_observed_at":"2026-08-07T11:07:40.811572Z","submitted_at":"2025-06-03T16:19:59Z","title":"On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T11:15:03.098185Z"},"links":{"citing_paper":"/paper/2506.03037"},"observation_digest":"sha256:d8c9cbdf7a4d483e08eb7b959e69373c35375d259826cba1f90670f5f5d7891c","observation_id":"15bc7c57-f268-4855-b319-13d4dd13644d","resolution":{"observed_at":"2026-08-07T11:15:03.098185Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:15:05.683849Z","title":"Explainable uncertainty quantifications for deep learning-based molecular property prediction.Journal of Cheminformatics, 15(1):13, 2023","venue":null,"work_id":"cf231b01-fac8-45ec-a678-de84ee26c452","year":2023},"citing_paper":{"arxiv_id":"2506.03037","last_updated":"2025-06-03T16:19:59Z","snapshot_observed_at":"2026-08-07T11:07:40.811572Z","submitted_at":"2025-06-03T16:19:59Z","title":"On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T11:15:03.162860Z"},"links":{"citing_paper":"/paper/2506.03037"},"observation_digest":"sha256:39a5f2ef7f3c0c80bde4612df50396330d8033f33f2f5fc793c4463050c92870","observation_id":"4162d769-d10e-438f-b49f-1954f49619df","resolution":{"observed_at":"2026-08-07T11:15:05.775985Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:15:05.466745Z","title":"Bayesian astrostatistics: a backward look to the future","venue":null,"work_id":"bc52e75f-23a2-4ba2-9565-550df70cb95c","year":2012},"citing_paper":{"arxiv_id":"2506.03037","last_updated":"2025-06-03T16:19:59Z","snapshot_observed_at":"2026-08-07T11:07:40.811572Z","submitted_at":"2025-06-03T16:19:59Z","title":"On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-07T11:15:03.242644Z"},"links":{"citing_paper":"/paper/2506.03037"},"observation_digest":"sha256:7dd75bd93547cd1284b5ec200580583a9da09dd41882cf83bdf19ca3258d68eb","observation_id":"51acd74e-2921-4ad2-8799-eac5052b026b","resolution":{"observed_at":"2026-08-07T11:15:05.551977Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:15:05.248412Z","title":"Towards reliable simulation-based inference with balanced neural ratio estimation.Advances in Neural Information Processing Systems, 35:20025–20037, 2022","venue":null,"work_id":"1db071ae-fbd1-4c40-a35f-38c5310f34c1","year":2022},"citing_paper":{"arxiv_id":"2506.03037","last_updated":"2025-06-03T16:19:59Z","snapshot_observed_at":"2026-08-07T11:07:40.811572Z","submitted_at":"2025-06-03T16:19:59Z","title":"On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T11:15:03.333578Z"},"links":{"citing_paper":"/paper/2506.03037"},"observation_digest":"sha256:556d2e8ee0ed90b1e1592b075456b63f988d49efae7abfac5be8563228c4223e","observation_id":"555c7ba0-8455-48fb-97fb-f800c305cf5c","resolution":{"observed_at":"2026-08-07T11:15:05.326693Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T11:15:03.445403Z","title":"Bayes and frequentism: a particle physicist’s perspective.Contemporary Physics, 54(1):1–16, February 2013","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2506.03037","last_updated":"2025-06-03T16:19:59Z","snapshot_observed_at":"2026-08-07T11:07:40.811572Z","submitted_at":"2025-06-03T16:19:59Z","title":"On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T11:15:03.445403Z"},"links":{"citing_paper":"/paper/2506.03037"},"observation_digest":"sha256:ded4b6197fea7b9d1db4348496028566ef5bb6737229975ad858cb9e45102273","observation_id":"4a1b3fea-2807-4524-b1ac-744f1dd9684c","resolution":{"observed_at":"2026-08-07T11:15:03.445403Z","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":"2026-08-07T11:15:03.552831Z","title":"Conformal prediction with temporal quantile adjustments.Advances in Neural Information Processing Systems, 35:31017–31030, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.03037","last_updated":"2025-06-03T16:19:59Z","snapshot_observed_at":"2026-08-07T11:07:40.811572Z","submitted_at":"2025-06-03T16:19:59Z","title":"On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-07T11:15:03.552831Z"},"links":{"citing_paper":"/paper/2506.03037"},"observation_digest":"sha256:3f8896ca8a12e6aceef545110de6bab13852012e83197164af6966f3ec99815d","observation_id":"5128d6ec-a455-4cbe-854e-d5988d251592","resolution":{"observed_at":"2026-08-07T11:15:03.552831Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.12940","last_updated":"2022-10-02T20:59:36Z","snapshot_observed_at":"2026-08-09T22:50:18.481284Z","submitted_at":"2022-05-25T17:45:04Z","title":"Conformal Prediction Intervals with Temporal Dependence","version":3},"cited_work":{"arxiv_id":"2205.12940","doi":null,"metadata_source":"pith","pith_arxiv_id":"2205.12940","snapshot_observed_at":"2026-08-07T11:15:04.528670Z","title":"Conformal Prediction Intervals with Temporal Dependence","venue":"stat.ML","work_id":"f939665e-51f2-4a39-a69b-2858688c3110","year":2022},"citing_paper":{"arxiv_id":"2506.03037","last_updated":"2025-06-03T16:19:59Z","snapshot_observed_at":"2026-08-07T11:07:40.811572Z","submitted_at":"2025-06-03T16:19:59Z","title":"On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-07T11:15:03.602095Z"},"links":{"cited_paper":"/paper/2205.12940","citing_paper":"/paper/2506.03037"},"observation_digest":"sha256:a9fd6869174ea1c5e8b91c948c066e193bdba64ac912635843de15c598ec498d","observation_id":"438da0dc-273e-4be7-8e1e-dcc639b9499b","resolution":{"observed_at":"2026-08-07T11:15:04.613916Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1804.06788","last_updated":"2020-10-21T17:50:23Z","snapshot_observed_at":"2026-08-08T23:32:23.868694Z","submitted_at":"2018-04-18T15:29:42Z","title":"Validating Bayesian Inference Algorithms with Simulation-Based Calibration","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1804.06788","snapshot_observed_at":"2026-08-07T11:15:03.700686Z","title":"Validating bayesian inference algorithms with simulation-based calibration, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.03037","last_updated":"2025-06-03T16:19:59Z","snapshot_observed_at":"2026-08-07T11:07:40.811572Z","submitted_at":"2025-06-03T16:19:59Z","title":"On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T11:15:03.700686Z"},"links":{"cited_paper":"/paper/1804.06788","citing_paper":"/paper/2506.03037"},"observation_digest":"sha256:f1e09e474d045d83e81db81fc9aeaf32173ffdc2cd275d4fc9530c94a58a7e9d","observation_id":"3ee05fbe-f898-492f-9bd1-b39011e24d2b","resolution":{"observed_at":"2026-08-07T11:15:03.700686Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:15:04.992163Z","title":"Bayesianly justifiable and relevant frequency calculations for the applied statistician.The Annals of Statistics, pages 1151–1172, 1984","venue":null,"work_id":"84b4dc28-0067-414a-8b45-098fd2209093","year":1984},"citing_paper":{"arxiv_id":"2506.03037","last_updated":"2025-06-03T16:19:59Z","snapshot_observed_at":"2026-08-07T11:07:40.811572Z","submitted_at":"2025-06-03T16:19:59Z","title":"On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-07T11:15:03.767375Z"},"links":{"citing_paper":"/paper/2506.03037"},"observation_digest":"sha256:e8e19725a4b9970b4837862441575261f160731b762af8a4e520140f1eb60cf7","observation_id":"8b3e5a6c-5504-401e-8572-266b7e7682f8","resolution":{"observed_at":"2026-08-07T11:15:05.074266Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T11:15:03.858685Z","title":"Posterior predictive assessment of model fitness via realized discrepancies.Statistica sinica, pages 733–760, 1996","venue":null,"work_id":null,"year":1996},"citing_paper":{"arxiv_id":"2506.03037","last_updated":"2025-06-03T16:19:59Z","snapshot_observed_at":"2026-08-07T11:07:40.811572Z","submitted_at":"2025-06-03T16:19:59Z","title":"On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-07T11:15:03.858685Z"},"links":{"citing_paper":"/paper/2506.03037"},"observation_digest":"sha256:4e73b871af71c005b8b30af10c5b9b189193e25edaf578efceb4d1f5771f8e1b","observation_id":"95c0abc5-4b5a-4a2f-9928-2f82d4f33a82","resolution":{"observed_at":"2026-08-07T11:15:03.858685Z","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":"2026-08-07T11:15:03.916791Z","title":"Predicting good probabilities with supervised learning","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2506.03037","last_updated":"2025-06-03T16:19:59Z","snapshot_observed_at":"2026-08-07T11:07:40.811572Z","submitted_at":"2025-06-03T16:19:59Z","title":"On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-07T11:15:03.916791Z"},"links":{"citing_paper":"/paper/2506.03037"},"observation_digest":"sha256:b9b702f408f65986e535e5248677288e708ff9520901cdde0c3bed75f2f256b6","observation_id":"f5f12203-d0c8-4351-9f15-055adee6ceb7","resolution":{"observed_at":"2026-08-07T11:15:03.916791Z","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":"2026-08-07T11:15:03.987047Z","title":"The comparison and evaluation of forecasters","venue":null,"work_id":null,"year":1983},"citing_paper":{"arxiv_id":"2506.03037","last_updated":"2025-06-03T16:19:59Z","snapshot_observed_at":"2026-08-07T11:07:40.811572Z","submitted_at":"2025-06-03T16:19:59Z","title":"On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-07T11:15:03.987047Z"},"links":{"citing_paper":"/paper/2506.03037"},"observation_digest":"sha256:64da97f26f63789bca2e15d5e2434f3223415278bb5de72a33dbbbf6ff3a5883","observation_id":"e7ae2318-3340-4fbf-85a1-53766d2ad979","resolution":{"observed_at":"2026-08-07T11:15:03.987047Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.12616","last_updated":"2024-09-16T22:31:36Z","snapshot_observed_at":"2026-07-06T15:30:22.612066Z","submitted_at":"2023-05-22T00:49:49Z","title":"Conformal Prediction With Conditional Guarantees","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.12616","snapshot_observed_at":"2026-08-07T11:15:04.067607Z","title":"Cherian, and Emmanuel J","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.03037","last_updated":"2025-06-03T16:19:59Z","snapshot_observed_at":"2026-08-07T11:07:40.811572Z","submitted_at":"2025-06-03T16:19:59Z","title":"On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-07T11:15:04.067607Z"},"links":{"cited_paper":"/paper/2305.12616","citing_paper":"/paper/2506.03037"},"observation_digest":"sha256:fd8049b0a97cfde0a35df2bb2f6202825ec3d0d4f479cdb1a3f0f626dda727e7","observation_id":"7d96905c-00f3-4ed1-a1f6-a310cb313407","resolution":{"observed_at":"2026-08-07T11:15:04.067607Z","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":"10.1103/physrevmaterials.7.025201","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Uncertainty cards,","venue":"Physical Review Materials","work_id":"63c032dc-05c6-4807-be42-c10528ac6ddc","year":2023},"citing_paper":{"arxiv_id":"2506.03037","last_updated":"2025-06-03T16:19:59Z","snapshot_observed_at":"2026-08-07T11:07:40.811572Z","submitted_at":"2025-06-03T16:19:59Z","title":"On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-07T11:15:04.199928Z"},"links":{"citing_paper":"/paper/2506.03037"},"observation_digest":"sha256:adb4ce81f675dacf94b8f4c016d5d3f51c352b028ff96cc81d34c2aa6ff2a6b6","observation_id":"65713567-3e0c-46cf-8ccc-fb42d4f2cdda","resolution":{"observed_at":"2026-08-07T11:15:04.388570Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.03037","last_updated":"2025-06-03T16:19:59Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-07T11:07:40.811572Z","submitted_at":"2025-06-03T16:19:59Z","title":"On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning"},"reference_resolution":{"displayed":50,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":20,"verified_exact":2,"verified_fuzzy":28},"total_outbound_references":50},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 0 inbound Pith citation observations for arXiv:2506.03037."}