{"as_of":"2026-08-09T17:13:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1fa967070a6cc2cf6ec4c3bd0afdefdfaccd02159ae8290c03ee8d5e65ba29b9","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":21,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":21,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":21,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":21,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T13:48:10.774120Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"arxiv_reference","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":141,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2011.01808","last_updated":"2020-11-03T15:59:50Z","snapshot_observed_at":"2026-08-05T18:31:01.469147Z","submitted_at":"2020-11-03T15:59:50Z","title":"Bayesian Workflow","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2011.01808","snapshot_observed_at":"2026-08-09T13:48:10.774120Z","title":"Margossian, Bob Carpenter, Yuling Yao, Lauren Kennedy, Jonah Gabry, Paul-Christian B \\\"u rkner, and Martin Modr \\'a k","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2502.02000","last_updated":"2025-02-04T04:22:02Z","snapshot_observed_at":"2026-08-09T13:41:03.597237Z","submitted_at":"2025-02-04T04:22:02Z","title":"Bayesian Spatiotemporal Nonstationary Model Quantifies Robust Increases in Daily Extreme Rainfall Across the Western Gulf Coast","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-09T13:48:10.774120Z"},"links":{"cited_paper":"/paper/2011.01808","citing_paper":"/paper/2502.02000"},"observation_digest":"sha256:dc658b08646c19acabfed83f96accdd08ce1739615be1889d3f7c0f05aefc12b","observation_id":"d899b791-c962-4f02-9377-2dba5abd0507","resolution":{"observed_at":"2026-08-09T13:48:10.774120Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2011.01808","last_updated":"2020-11-03T15:59:50Z","snapshot_observed_at":"2026-08-05T18:31:01.469147Z","submitted_at":"2020-11-03T15:59:50Z","title":"Bayesian Workflow","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2011.01808","snapshot_observed_at":"2026-08-09T05:23:16.966195Z","title":"C., Carpenter, B., Yao, Y., Kennedy, L., Gabry, J., Bürkner, P.-C., and Modrák, M","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.03279","last_updated":"2025-03-10T12:16:16Z","snapshot_observed_at":"2026-08-09T05:14:44.412260Z","submitted_at":"2025-02-05T15:35:06Z","title":"Posterior SBC: Simulation-Based Calibration Checking Conditional on Data","version":2},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-09T05:23:16.966195Z"},"links":{"cited_paper":"/paper/2011.01808","citing_paper":"/paper/2502.03279"},"observation_digest":"sha256:f68a27c3e5a84029785607d07bcbb24a874a68183d91faf8bf03572db830b3cd","observation_id":"b917e9b8-fb26-4ae8-8cba-3744acddf57d","resolution":{"observed_at":"2026-08-09T05:23:16.966195Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2011.01808","last_updated":"2020-11-03T15:59:50Z","snapshot_observed_at":"2026-08-05T18:31:01.469147Z","submitted_at":"2020-11-03T15:59:50Z","title":"Bayesian Workflow","version":1},"cited_work":{"arxiv_id":"2011.01808","doi":"10.48550/arxiv.2011.01808","metadata_source":"arxiv_reference","pith_arxiv_id":"2011.01808","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Gelman, A","venue":"arXiv (Cornell University)","work_id":"c578ed1d-d3f2-4207-80b1-2146321398b0","year":2011},"citing_paper":{"arxiv_id":"2505.14429","last_updated":"2026-04-07T14:12:18Z","snapshot_observed_at":"2026-07-06T21:27:08.958780Z","submitted_at":"2025-05-20T14:38:53Z","title":"Compositional amortized inference for large-scale hierarchical Bayesian models","version":5},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-22T14:23:35.960476Z"},"links":{"cited_paper":"/paper/2011.01808","citing_paper":"/paper/2505.14429"},"observation_digest":"sha256:bb9e00ae8932829ed9889ae173b920b6de06341eb779cf70ceb13171d8291763","observation_id":"6823c327-e956-4b8d-a4db-76685357c6da","resolution":{"observed_at":"2026-05-22T14:24:53.569221Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2011.01808","last_updated":"2020-11-03T15:59:50Z","snapshot_observed_at":"2026-08-05T18:31:01.469147Z","submitted_at":"2020-11-03T15:59:50Z","title":"Bayesian Workflow","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2011.01808","snapshot_observed_at":"2026-08-07T05:04:53.290153Z","title":"Bayesian workflow.arXiv preprint arXiv:2011.01808,","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2506.08756","last_updated":"2025-06-10T12:53:31Z","snapshot_observed_at":"2026-08-08T21:29:54.701321Z","submitted_at":"2025-06-10T12:53:31Z","title":"Bayesian Inverse Physics for Neuro-Symbolic Robot Learning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T05:04:53.290153Z"},"links":{"cited_paper":"/paper/2011.01808","citing_paper":"/paper/2506.08756"},"observation_digest":"sha256:fa6618c4766ed2ef5ddc70d4ab2154ee4deaa761e2df435862ef6647d9e63cf2","observation_id":"b0f74a6e-1b59-4241-aa9d-9f828ab4ce27","resolution":{"observed_at":"2026-08-07T05:04:53.290153Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2011.01808","last_updated":"2020-11-03T15:59:50Z","snapshot_observed_at":"2026-08-05T18:31:01.469147Z","submitted_at":"2020-11-03T15:59:50Z","title":"Bayesian Workflow","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2011.01808","snapshot_observed_at":"2026-08-06T17:11:32.373286Z","title":"2020, arXiv e-prints, arXiv:2011.01808","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.11699","last_updated":"2025-07-15T20:07:02Z","snapshot_observed_at":"2026-08-06T17:01:40.796763Z","submitted_at":"2025-07-15T20:07:02Z","title":"Granulation signatures in 3D hydrodynamical simulations: evaluating background model performance using a Bayesian nested sampling framework","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-06T17:11:32.373286Z"},"links":{"cited_paper":"/paper/2011.01808","citing_paper":"/paper/2507.11699"},"observation_digest":"sha256:630477db33e10eef3dd9868d0b23b0659d18a009a2372cff6b714e3bf984e142","observation_id":"a99e60f0-8cec-4875-9879-c42cb7bdb890","resolution":{"observed_at":"2026-08-06T17:11:32.373286Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2011.01808","last_updated":"2020-11-03T15:59:50Z","snapshot_observed_at":"2026-08-05T18:31:01.469147Z","submitted_at":"2020-11-03T15:59:50Z","title":"Bayesian Workflow","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2011.01808","snapshot_observed_at":"2026-08-06T11:36:12.723429Z","title":"From,” containing the cause variable, “To,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.05659","last_updated":"2026-07-13T18:40:13Z","snapshot_observed_at":"2026-08-06T11:36:07.731364Z","submitted_at":"2025-07-30T11:27:07Z","title":"Diagrams-to-Dynamics (D2D): Exploring Causal Loop Diagram Leverage Points under Uncertainty","version":4},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T11:36:12.723429Z"},"links":{"cited_paper":"/paper/2011.01808","citing_paper":"/paper/2508.05659"},"observation_digest":"sha256:08e63388ebb385750c50c254fd487092b2946df6fe4999ab67baabe8d86407cd","observation_id":"ee2a30f1-6a1d-4a69-9599-5edee7425c67","resolution":{"observed_at":"2026-08-06T11:36:12.723429Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2011.01808","last_updated":"2020-11-03T15:59:50Z","snapshot_observed_at":"2026-08-05T18:31:01.469147Z","submitted_at":"2020-11-03T15:59:50Z","title":"Bayesian Workflow","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2011.01808","snapshot_observed_at":"2026-08-05T22:24:20.086954Z","title":", author Vehtari, A","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2508.07136","last_updated":"2025-08-24T05:43:57Z","snapshot_observed_at":"2026-08-09T09:09:14.561172Z","submitted_at":"2025-08-10T01:29:27Z","title":"Bayesian Forecast Combination with Predictive Priors via Particle Filtering","version":2},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-05T22:24:20.086954Z"},"links":{"cited_paper":"/paper/2011.01808","citing_paper":"/paper/2508.07136"},"observation_digest":"sha256:0cd3c011a072b5821e870ea83d80d61bdc517cb7c15ab540796f84a27309c4f2","observation_id":"eb83953b-cec4-4dea-b83f-c0ec4e1006ef","resolution":{"observed_at":"2026-08-05T22:24:20.086954Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2011.01808","last_updated":"2020-11-03T15:59:50Z","snapshot_observed_at":"2026-08-05T18:31:01.469147Z","submitted_at":"2020-11-03T15:59:50Z","title":"Bayesian Workflow","version":1},"cited_work":{"arxiv_id":"2011.01808","doi":"10.48550/arxiv.2011.01808","metadata_source":"arxiv_reference","pith_arxiv_id":"2011.01808","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Gelman, A","venue":"arXiv (Cornell University)","work_id":"c578ed1d-d3f2-4207-80b1-2146321398b0","year":2011},"citing_paper":{"arxiv_id":"2509.01082","last_updated":"2026-04-19T01:13:14Z","snapshot_observed_at":"2026-08-07T10:03:46.349624Z","submitted_at":"2025-09-01T03:13:36Z","title":"RefineStat: Efficient Exploration for Probabilistic Program Synthesis","version":3},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-05-18T20:28:26.767266Z"},"links":{"cited_paper":"/paper/2011.01808","citing_paper":"/paper/2509.01082"},"observation_digest":"sha256:9be894d72f3a6c8a37de9d3d78389c23f8cceae9f2b713813e0fee37b8e3802b","observation_id":"8f799081-9ee8-428a-89f9-f1bc1b46fe36","resolution":{"observed_at":"2026-05-18T20:31:50.614510Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2011.01808","last_updated":"2020-11-03T15:59:50Z","snapshot_observed_at":"2026-08-05T18:31:01.469147Z","submitted_at":"2020-11-03T15:59:50Z","title":"Bayesian Workflow","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2011.01808","snapshot_observed_at":"2026-08-05T05:03:46.259320Z","title":"arXiv preprint arXiv:2011.01808","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.05846","last_updated":"2025-09-06T22:04:49Z","snapshot_observed_at":"2026-08-09T16:13:55.944963Z","submitted_at":"2025-09-06T22:04:49Z","title":"Comparative study of Bayesian and Frequentist methods for epidemic forecasting: Insights from simulated and historical data","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-05T05:03:46.259320Z"},"links":{"cited_paper":"/paper/2011.01808","citing_paper":"/paper/2509.05846"},"observation_digest":"sha256:b7cb39452eb3de79514cbfeab3fec219305d9878ad530da8712e3bb53cdef4fa","observation_id":"31d7547d-ed8a-4ee0-8e30-86c451425023","resolution":{"observed_at":"2026-08-05T05:03:46.259320Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2011.01808","last_updated":"2020-11-03T15:59:50Z","snapshot_observed_at":"2026-08-05T18:31:01.469147Z","submitted_at":"2020-11-03T15:59:50Z","title":"Bayesian Workflow","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2011.01808","snapshot_observed_at":"2026-08-04T23:10:50.357666Z","title":"C., Carpenter, B., Yao, Y., Kennedy, L., Gabry, J., Bürkner, P.-C., and Modrák, M","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.06779","last_updated":"2025-09-08T15:03:57Z","snapshot_observed_at":"2026-08-09T12:13:02.225584Z","submitted_at":"2025-09-08T15:03:57Z","title":"A nutritionally informed model for Bayesian variable selection with metabolite response variables","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-04T23:10:50.357666Z"},"links":{"cited_paper":"/paper/2011.01808","citing_paper":"/paper/2509.06779"},"observation_digest":"sha256:788b7cfe866a2df90f0d66caaf1870e1b39c2dbe81c27ac93828cc79eb3373a8","observation_id":"4a698214-2a7a-4464-b747-8446c08c378a","resolution":{"observed_at":"2026-08-04T23:10:50.357666Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2011.01808","last_updated":"2020-11-03T15:59:50Z","snapshot_observed_at":"2026-08-05T18:31:01.469147Z","submitted_at":"2020-11-03T15:59:50Z","title":"Bayesian Workflow","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2011.01808","snapshot_observed_at":"2026-08-02T18:45:24.531959Z","title":"Ahrens, T","venue":null,"work_id":null,"year":1993},"citing_paper":{"arxiv_id":"2603.05961","last_updated":"2026-07-18T20:39:44Z","snapshot_observed_at":"2026-08-06T03:16:51.122205Z","submitted_at":"2026-03-06T06:50:58Z","title":"A Tutorial on Bayesian Analysis of Linear Shock Compression Data","version":3},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-02T18:45:24.531959Z"},"links":{"cited_paper":"/paper/2011.01808","citing_paper":"/paper/2603.05961"},"observation_digest":"sha256:8380081dd265f650c037ba4008f3da972e674c3b7891100c78882f616bf9d203","observation_id":"1a4fada5-8845-49d5-b9ac-063fcaf06667","resolution":{"observed_at":"2026-08-02T18:45:24.531959Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2011.01808","last_updated":"2020-11-03T15:59:50Z","snapshot_observed_at":"2026-08-05T18:31:01.469147Z","submitted_at":"2020-11-03T15:59:50Z","title":"Bayesian Workflow","version":1},"cited_work":{"arxiv_id":"2011.01808","doi":"10.48550/arxiv.2011.01808","metadata_source":"arxiv_reference","pith_arxiv_id":"2011.01808","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Gelman, A","venue":"arXiv (Cornell University)","work_id":"c578ed1d-d3f2-4207-80b1-2146321398b0","year":2011},"citing_paper":{"arxiv_id":"2605.27889","last_updated":"2026-06-01T20:36:56Z","snapshot_observed_at":"2026-07-06T23:37:31.844094Z","submitted_at":"2026-05-27T03:10:38Z","title":"Beyond Empirical Bayes: A Hierarchical Bayesian Approach to Crash Rate Estimation with Missing Traffic Volume","version":2},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-06-29T09:50:15.995515Z"},"links":{"cited_paper":"/paper/2011.01808","citing_paper":"/paper/2605.27889"},"observation_digest":"sha256:33c44b76eef588a69c8fad8e6bc0f579e7f298c1367c84ac51c877df760011c7","observation_id":"a4af7c84-d361-4f5e-aae8-87ea12d0b762","resolution":{"observed_at":"2026-06-29T09:53:17.385150Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2011.01808","last_updated":"2020-11-03T15:59:50Z","snapshot_observed_at":"2026-08-05T18:31:01.469147Z","submitted_at":"2020-11-03T15:59:50Z","title":"Bayesian Workflow","version":1},"cited_work":{"arxiv_id":"2011.01808","doi":"10.48550/arxiv.2011.01808","metadata_source":"arxiv_reference","pith_arxiv_id":"2011.01808","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Gelman, A","venue":"arXiv (Cornell University)","work_id":"c578ed1d-d3f2-4207-80b1-2146321398b0","year":2011},"citing_paper":{"arxiv_id":"2606.01428","last_updated":"2026-05-31T19:56:12Z","snapshot_observed_at":"2026-08-02T03:29:22.450639Z","submitted_at":"2026-05-31T19:56:12Z","title":"Quantifying Evidential Rigor in Meta-Analytic Corpora: A Simulation-Characterized, Bias-Robust Bayesian Workflow with a Nutrition Case Study","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-06-28T16:11:49.418251Z"},"links":{"cited_paper":"/paper/2011.01808","citing_paper":"/paper/2606.01428"},"observation_digest":"sha256:89277f2e67dfccafe1ae06c01a7c9defd7f5a470a4aa8ebaf39346496fa27785","observation_id":"8c39daa1-8c93-4c23-99f7-8e2ef37c6716","resolution":{"observed_at":"2026-06-28T16:12:21.759630Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2011.01808","last_updated":"2020-11-03T15:59:50Z","snapshot_observed_at":"2026-08-05T18:31:01.469147Z","submitted_at":"2020-11-03T15:59:50Z","title":"Bayesian Workflow","version":1},"cited_work":{"arxiv_id":"2011.01808","doi":"10.48550/arxiv.2011.01808","metadata_source":"arxiv_reference","pith_arxiv_id":"2011.01808","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Gelman, A","venue":"arXiv (Cornell University)","work_id":"c578ed1d-d3f2-4207-80b1-2146321398b0","year":2011},"citing_paper":{"arxiv_id":"2606.11834","last_updated":"2026-06-10T09:16:15Z","snapshot_observed_at":"2026-08-04T05:11:04.409659Z","submitted_at":"2026-06-10T09:16:15Z","title":"How Requirements Quality Makes (or Breaks) Traceability Link Recovery","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-06-27T09:21:54.458621Z"},"links":{"cited_paper":"/paper/2011.01808","citing_paper":"/paper/2606.11834"},"observation_digest":"sha256:b7c160cc763a29669802ae28bbfc7664c80929b64c6b0801b4caf9db4859a9ff","observation_id":"6500a042-2200-4228-822b-f7a080e60c2d","resolution":{"observed_at":"2026-07-03T11:48:04.911368Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2011.01808","last_updated":"2020-11-03T15:59:50Z","snapshot_observed_at":"2026-08-05T18:31:01.469147Z","submitted_at":"2020-11-03T15:59:50Z","title":"Bayesian Workflow","version":1},"cited_work":{"arxiv_id":"2011.01808","doi":"10.48550/arxiv.2011.01808","metadata_source":"arxiv_reference","pith_arxiv_id":"2011.01808","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Gelman, A","venue":"arXiv (Cornell University)","work_id":"c578ed1d-d3f2-4207-80b1-2146321398b0","year":2011},"citing_paper":{"arxiv_id":"2606.12677","last_updated":"2026-06-10T21:01:49Z","snapshot_observed_at":"2026-08-07T16:30:58.231765Z","submitted_at":"2026-06-10T21:01:49Z","title":"Restricted Multivariate Spatial Modeling","version":1},"reference_index":115,"source":"arxiv_source","source_observed_at":"2026-06-27T08:32:13.850472Z"},"links":{"cited_paper":"/paper/2011.01808","citing_paper":"/paper/2606.12677"},"observation_digest":"sha256:3e67159600e9ce22a6005bea115185e54c2cae200fab878ac84aa104f0d1c063","observation_id":"9c40682c-e363-476e-9c47-c62d516f4339","resolution":{"observed_at":"2026-06-28T03:21:31.079200Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2011.01808","last_updated":"2020-11-03T15:59:50Z","snapshot_observed_at":"2026-08-05T18:31:01.469147Z","submitted_at":"2020-11-03T15:59:50Z","title":"Bayesian Workflow","version":1},"cited_work":{"arxiv_id":"2011.01808","doi":"10.48550/arxiv.2011.01808","metadata_source":"arxiv_reference","pith_arxiv_id":"2011.01808","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Gelman, A","venue":"arXiv (Cornell University)","work_id":"c578ed1d-d3f2-4207-80b1-2146321398b0","year":2011},"citing_paper":{"arxiv_id":"2606.22850","last_updated":"2026-06-22T04:52:33Z","snapshot_observed_at":"2026-08-07T17:42:08.900842Z","submitted_at":"2026-06-22T04:52:33Z","title":"To select or not to select: predictively consistent priors instead of model selection","version":1},"reference_index":229,"source":"arxiv_source","source_observed_at":"2026-06-26T07:52:42.009332Z"},"links":{"cited_paper":"/paper/2011.01808","citing_paper":"/paper/2606.22850"},"observation_digest":"sha256:a68d3d2c59f5bfb67f5843e9f2cabe3cde55105446f21170a312c66bd43148d3","observation_id":"cd194b20-aedf-4161-996e-00a18bf0be94","resolution":{"observed_at":"2026-06-26T09:09:16.314001Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2011.01808","last_updated":"2020-11-03T15:59:50Z","snapshot_observed_at":"2026-08-05T18:31:01.469147Z","submitted_at":"2020-11-03T15:59:50Z","title":"Bayesian Workflow","version":1},"cited_work":{"arxiv_id":"2011.01808","doi":"10.48550/arxiv.2011.01808","metadata_source":"arxiv_reference","pith_arxiv_id":"2011.01808","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Gelman, A","venue":"arXiv (Cornell University)","work_id":"c578ed1d-d3f2-4207-80b1-2146321398b0","year":2011},"citing_paper":{"arxiv_id":"2606.30898","last_updated":"2026-06-29T20:39:47Z","snapshot_observed_at":"2026-07-07T00:04:39.472457Z","submitted_at":"2026-06-29T20:39:47Z","title":"Bridging electrode preparation and electrocatalyst performance with physics-based causal AI","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-07-01T01:22:47.560769Z"},"links":{"cited_paper":"/paper/2011.01808","citing_paper":"/paper/2606.30898"},"observation_digest":"sha256:a29c163887456a3f2e0e9a43dee939c677ad1b24de2f598e5fb3a9a0b994d012","observation_id":"36e37d19-94c2-44d7-b0fa-d73118eb9fb2","resolution":{"observed_at":"2026-07-01T12:55:45.267339Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2011.01808","last_updated":"2020-11-03T15:59:50Z","snapshot_observed_at":"2026-08-05T18:31:01.469147Z","submitted_at":"2020-11-03T15:59:50Z","title":"Bayesian Workflow","version":1},"cited_work":{"arxiv_id":"2011.01808","doi":"10.48550/arxiv.2011.01808","metadata_source":"arxiv_reference","pith_arxiv_id":"2011.01808","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Gelman, A","venue":"arXiv (Cornell University)","work_id":"c578ed1d-d3f2-4207-80b1-2146321398b0","year":2011},"citing_paper":{"arxiv_id":"2606.31630","last_updated":"2026-06-30T13:16:39Z","snapshot_observed_at":"2026-08-05T11:04:53.475955Z","submitted_at":"2026-06-30T13:16:39Z","title":"Calibration, Not Compilation: Detecting and Repairing Misspecified Probabilistic Programs Written by Language Models","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-07-01T06:17:08.273981Z"},"links":{"cited_paper":"/paper/2011.01808","citing_paper":"/paper/2606.31630"},"observation_digest":"sha256:08f754735193629a0aa94ccb3c20450de2b274feddae81e2be1e4a13718cdbfb","observation_id":"d998cd04-c892-4198-9e9a-d70e62963b39","resolution":{"observed_at":"2026-07-01T09:45:39.899005Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2011.01808","last_updated":"2020-11-03T15:59:50Z","snapshot_observed_at":"2026-08-05T18:31:01.469147Z","submitted_at":"2020-11-03T15:59:50Z","title":"Bayesian Workflow","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2011.01808","snapshot_observed_at":"2026-07-14T14:31:36.487965Z","title":"Bayesian workflow","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2607.09931","last_updated":"2026-07-17T00:48:06Z","snapshot_observed_at":"2026-08-07T10:09:29.598768Z","submitted_at":"2026-07-10T19:28:01Z","title":"Toward Joint Prediction of a Longitudinal Marker and a Terminal Event: A bivariate discrete-time framework","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-07-14T14:31:36.487965Z"},"links":{"cited_paper":"/paper/2011.01808","citing_paper":"/paper/2607.09931"},"observation_digest":"sha256:55e3fe27cad58c310f51a80b86c223f2c3975b0ba82366294a5121bd3b722ae2","observation_id":"a9046112-0772-4b27-b181-193ad8098588","resolution":{"observed_at":"2026-07-14T14:31:36.487965Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2011.01808","last_updated":"2020-11-03T15:59:50Z","snapshot_observed_at":"2026-08-05T18:31:01.469147Z","submitted_at":"2020-11-03T15:59:50Z","title":"Bayesian Workflow","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2011.01808","snapshot_observed_at":"2026-08-01T07:00:58.130409Z","title":"arXiv preprint arXiv:2011.01808 , year=","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2607.21702","last_updated":"2026-07-23T18:00:01Z","snapshot_observed_at":"2026-08-08T08:29:48.407071Z","submitted_at":"2026-07-23T18:00:01Z","title":"An Introduction to Bayesian and Frequentist Simulation-Based Inference with Machine Learning","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-01T07:00:58.130409Z"},"links":{"cited_paper":"/paper/2011.01808","citing_paper":"/paper/2607.21702"},"observation_digest":"sha256:fb00541fc66ebf2e15f6820171517de4297035f3ee96f792e768a72d3a78a7da","observation_id":"e3bf2c80-eacb-410a-a275-51fadbe5cae2","resolution":{"observed_at":"2026-08-01T07:00:58.130409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2011.01808","last_updated":"2020-11-03T15:59:50Z","snapshot_observed_at":"2026-08-05T18:31:01.469147Z","submitted_at":"2020-11-03T15:59:50Z","title":"Bayesian Workflow","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2011.01808","snapshot_observed_at":"2026-08-01T13:39:53.314536Z","title":"arXiv preprint arXiv:2011.01808 , year=","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2607.26552","last_updated":"2026-07-29T07:23:36Z","snapshot_observed_at":"2026-08-06T04:13:54.904882Z","submitted_at":"2026-07-29T07:23:36Z","title":"Bayesian Inference of Discretization Error Means in ODEs via Ensemble Kalman Filtering","version":1},"reference_index":289,"source":"arxiv_source","source_observed_at":"2026-08-01T13:39:53.314536Z"},"links":{"cited_paper":"/paper/2011.01808","citing_paper":"/paper/2607.26552"},"observation_digest":"sha256:6a92ccb2573a1096a5a0b0bbcd3a220f175d2af1d2644a830eb85e55b86063da","observation_id":"734a1929-e54f-4b4f-8f44-38a24d48419b","resolution":{"observed_at":"2026-08-01T13:39:53.314536Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2011.01808/citation-record","integrity":"/paper/2011.01808/integrity","json":"/paper/2011.01808/citation-record.json","paper":"/paper/2011.01808"},"outbound":[],"paper":{"arxiv_id":"2011.01808","last_updated":"2020-11-03T15:59:50Z","latest_version":1,"primary_category":"stat.ME","snapshot_observed_at":"2026-08-05T18:31:01.469147Z","submitted_at":"2020-11-03T15:59:50Z","title":"Bayesian Workflow"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 21 inbound Pith citation observations for arXiv:2011.01808."}