{"as_of":"2026-08-21T06:06:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d2c19114976f60cf57f27cee1dda87d6fa8c297a263c63802cdbe7fb1315a5d0","coverage":[{"denominator":36,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":36,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T00:11:15.348582Z","state":"measured"},{"denominator":36,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":36,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+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/2608.12603/citation-record","integrity":"/paper/2608.12603/integrity","json":"/paper/2608.12603/citation-record.json","paper":"/paper/2608.12603"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/978-3-540-28650-9_3","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-17T11:03:46.409680Z","title":"Bayesian Inference: An Introduction to Principles and Practice in Machine Learning","venue":"Lecture notes in computer science","work_id":"1d04d2d9-143d-4b86-882c-f5155c5a46d4","year":2003},"citing_paper":{"arxiv_id":"2608.12603","last_updated":"2026-08-12T21:26:39Z","snapshot_observed_at":"2026-08-20T17:59:54.784427Z","submitted_at":"2026-08-12T21:26:39Z","title":"Hierarchical Bayesian Calibration with Bayesian Committee Machine","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-16T00:11:15.225309Z"},"links":{"citing_paper":"/paper/2608.12603"},"observation_digest":"sha256:9255c574ba210d658d7c1e518669410d9760fb7b587a79233142e99f96ba5fcd","observation_id":"868ac606-a395-4d0c-a722-94acf0a3db10","resolution":{"observed_at":"2026-08-16T00:11:15.470719Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T00:11:15.229454Z","title":"Bayesian calibration of computer models","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2608.12603","last_updated":"2026-08-12T21:26:39Z","snapshot_observed_at":"2026-08-20T17:59:54.784427Z","submitted_at":"2026-08-12T21:26:39Z","title":"Hierarchical Bayesian Calibration with Bayesian Committee Machine","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-16T00:11:15.229454Z"},"links":{"citing_paper":"/paper/2608.12603"},"observation_digest":"sha256:8cc33d93e36f331c6a313e7bec44cc91ce4746dd53d5005a3c43beb0eb073dca","observation_id":"2d25412a-7a1d-46a1-a1d5-42bb9ff2c382","resolution":{"observed_at":"2026-08-16T00:11:15.229454Z","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":"2026-08-16T00:11:15.989187Z","title":"Combining Field Data and Computer Simulations for Calibration and Predic- tion","venue":null,"work_id":"346f031c-0938-4ae4-afa2-5fd8a8c05b3f","year":2004},"citing_paper":{"arxiv_id":"2608.12603","last_updated":"2026-08-12T21:26:39Z","snapshot_observed_at":"2026-08-20T17:59:54.784427Z","submitted_at":"2026-08-12T21:26:39Z","title":"Hierarchical Bayesian Calibration with Bayesian Committee Machine","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-16T00:11:15.233230Z"},"links":{"citing_paper":"/paper/2608.12603"},"observation_digest":"sha256:fc208d0e4cff23d511def047a7404b2a78292255fbe8bd87827191a880ffed69","observation_id":"55968043-f801-4a15-89d0-719c4ee54a69","resolution":{"observed_at":"2026-08-16T00:11:15.992863Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1214/009053607000000163.full","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T00:11:15.450742Z","title":"Computer model validation with functional output","venue":null,"work_id":"1dcacdd4-5202-40cb-8321-d9a2720d9bf8","year":2007},"citing_paper":{"arxiv_id":"2608.12603","last_updated":"2026-08-12T21:26:39Z","snapshot_observed_at":"2026-08-20T17:59:54.784427Z","submitted_at":"2026-08-12T21:26:39Z","title":"Hierarchical Bayesian Calibration with Bayesian Committee Machine","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-16T00:11:15.236971Z"},"links":{"citing_paper":"/paper/2608.12603"},"observation_digest":"sha256:fe30618d4289b89821bade93ad23020fe0620bfa7e1d3f49520f19431c721b7b","observation_id":"a5173a97-f4ea-49fd-8153-71e198c9cf3f","resolution":{"observed_at":"2026-08-16T00:11:15.454379Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1198/jasa.2009.ap06623","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T00:11:15.440145Z","title":"Predicting Vehicle Crashworthiness: Validation of Computer Models for Functional and Hierarchical Data","venue":null,"work_id":"4f13df66-e1c7-408d-9e55-c83c1dfab8a0","year":2009},"citing_paper":{"arxiv_id":"2608.12603","last_updated":"2026-08-12T21:26:39Z","snapshot_observed_at":"2026-08-20T17:59:54.784427Z","submitted_at":"2026-08-12T21:26:39Z","title":"Hierarchical Bayesian Calibration with Bayesian Committee Machine","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-16T00:11:15.240735Z"},"links":{"citing_paper":"/paper/2608.12603"},"observation_digest":"sha256:cb449b6f5a19e1cde18f26b047dd76f0c8b7f215401faba7fb1e8ce81a517829","observation_id":"5e71b151-2cbd-48aa-8a3c-2dfa2c82c8a2","resolution":{"observed_at":"2026-08-16T00:11:15.443660Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"stable/2764008","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T00:11:15.760360Z","title":"Computer Model Calibration Using High-Dimensional Output","venue":null,"work_id":"fb3cbae8-35e5-4f56-aeee-67b96ed1cdd1","year":2008},"citing_paper":{"arxiv_id":"2608.12603","last_updated":"2026-08-12T21:26:39Z","snapshot_observed_at":"2026-08-20T17:59:54.784427Z","submitted_at":"2026-08-12T21:26:39Z","title":"Hierarchical Bayesian Calibration with Bayesian Committee Machine","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-16T00:11:15.244633Z"},"links":{"citing_paper":"/paper/2608.12603"},"observation_digest":"sha256:d9ebc2c546c1fce0fe2d2e1a53c42516b5a9cd7d7a447a0457f78e4849790992","observation_id":"06b00212-24e5-45c4-878c-e2dad5091798","resolution":{"observed_at":"2026-08-16T00:11:15.765633Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T00:11:15.977866Z","title":"Reified Bayesian modelling and inference for physical systems","venue":null,"work_id":"651f42a4-cc19-42d0-8049-4f842f8b3bb6","year":2009},"citing_paper":{"arxiv_id":"2608.12603","last_updated":"2026-08-12T21:26:39Z","snapshot_observed_at":"2026-08-20T17:59:54.784427Z","submitted_at":"2026-08-12T21:26:39Z","title":"Hierarchical Bayesian Calibration with Bayesian Committee Machine","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-16T00:11:15.249299Z"},"links":{"citing_paper":"/paper/2608.12603"},"observation_digest":"sha256:9887350aff356f79f53669455cbf1d032ea028cd9987b3bc70a25a1bf1e97f73","observation_id":"a97150df-a5f5-4b6f-9945-8b4380df67c1","resolution":{"observed_at":"2026-08-16T00:11:15.982410Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"stable/2547145","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T00:11:15.703990Z","title":"Bayesian Hierarchical Modeling for Integrating Low-Accuracy and High-Accuracy Experiments","venue":null,"work_id":"9c429ff6-8718-4def-90d4-ee40ce129194","year":2008},"citing_paper":{"arxiv_id":"2608.12603","last_updated":"2026-08-12T21:26:39Z","snapshot_observed_at":"2026-08-20T17:59:54.784427Z","submitted_at":"2026-08-12T21:26:39Z","title":"Hierarchical Bayesian Calibration with Bayesian Committee Machine","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-16T00:11:15.253030Z"},"links":{"citing_paper":"/paper/2608.12603"},"observation_digest":"sha256:666cc362208e1dd11f3eb394ef4e844ddcb48aa1b3b9198d0af1b4745749fc39","observation_id":"e4a76cea-2475-442b-bd96-7f09571eab67","resolution":{"observed_at":"2026-08-16T00:11:15.709065Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1214/15-aos1314.full","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T00:11:15.430000Z","title":"Eﬀicient calibration for imperfect computer models","venue":null,"work_id":"de6993f3-907e-4488-89db-ae365702d4d7","year":2015},"citing_paper":{"arxiv_id":"2608.12603","last_updated":"2026-08-12T21:26:39Z","snapshot_observed_at":"2026-08-20T17:59:54.784427Z","submitted_at":"2026-08-12T21:26:39Z","title":"Hierarchical Bayesian Calibration with Bayesian Committee Machine","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-16T00:11:15.256169Z"},"links":{"citing_paper":"/paper/2608.12603"},"observation_digest":"sha256:c39e509851697d855cc85b8fed046a1ba88b9b7ef9f039944a022864ae5d7649","observation_id":"4ec11e9d-9df5-4c18-ba58-e5e74ded8b09","resolution":{"observed_at":"2026-08-16T00:11:15.433718Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T00:11:15.259315Z","title":"Bayesian Calibration of Inexact Computer Models","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2608.12603","last_updated":"2026-08-12T21:26:39Z","snapshot_observed_at":"2026-08-20T17:59:54.784427Z","submitted_at":"2026-08-12T21:26:39Z","title":"Hierarchical Bayesian Calibration with Bayesian Committee Machine","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-16T00:11:15.259315Z"},"links":{"citing_paper":"/paper/2608.12603"},"observation_digest":"sha256:ec0ac7dc0be8e70934c7b168009218fe3d3eb2ebf9cd0064198eba4dc9f9cf09","observation_id":"3542963e-c793-4ebc-8e0b-d28c33cfd028","resolution":{"observed_at":"2026-08-16T00:11:15.259315Z","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":"2026-08-16T00:11:15.967245Z","title":"A unified framework for multilevel uncertainty quantification in Bayesian inverse problems","venue":null,"work_id":"0bb91ef6-50b0-4d41-a565-a76a865a5106","year":2016},"citing_paper":{"arxiv_id":"2608.12603","last_updated":"2026-08-12T21:26:39Z","snapshot_observed_at":"2026-08-20T17:59:54.784427Z","submitted_at":"2026-08-12T21:26:39Z","title":"Hierarchical Bayesian Calibration with Bayesian Committee Machine","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-16T00:11:15.262444Z"},"links":{"citing_paper":"/paper/2608.12603"},"observation_digest":"sha256:bf794c3a4bc814edbdda6c3135ad22f0b038939f34b5665066e0dbc3e04245e3","observation_id":"e4559016-7f8b-443f-9247-8a02877908ed","resolution":{"observed_at":"2026-08-16T00:11:15.971187Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T00:11:15.955661Z","title":"Gaussian Processes for Machine Learning","venue":null,"work_id":"45d9165f-bcfe-4121-9c15-e8508b9f8152","year":2005},"citing_paper":{"arxiv_id":"2608.12603","last_updated":"2026-08-12T21:26:39Z","snapshot_observed_at":"2026-08-20T17:59:54.784427Z","submitted_at":"2026-08-12T21:26:39Z","title":"Hierarchical Bayesian Calibration with Bayesian Committee Machine","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-16T00:11:15.265942Z"},"links":{"citing_paper":"/paper/2608.12603"},"observation_digest":"sha256:198ebaf3377c165ff8855b2b80d9a44b9b6a805fb12b1bb6490e49318d0349b5","observation_id":"fdf70fd8-568a-47f3-8117-70e52e8c823f","resolution":{"observed_at":"2026-08-16T00:11:15.959661Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T00:11:15.942270Z","title":"Variational Learning of Inducing Variables in Sparse Gaussian Processes","venue":null,"work_id":"08dfe5db-bf4e-43e1-a5e3-13ca46d53ca5","year":2009},"citing_paper":{"arxiv_id":"2608.12603","last_updated":"2026-08-12T21:26:39Z","snapshot_observed_at":"2026-08-20T17:59:54.784427Z","submitted_at":"2026-08-12T21:26:39Z","title":"Hierarchical Bayesian Calibration with Bayesian Committee Machine","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-16T00:11:15.269140Z"},"links":{"citing_paper":"/paper/2608.12603"},"observation_digest":"sha256:628076f4d7745799343479f736476af84e2e6d3eb98edebdd92063538bcc88e8","observation_id":"59f9d453-5b55-42ea-aef2-60e8ac4d63e1","resolution":{"observed_at":"2026-08-16T00:11:15.946893Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T00:11:15.931079Z","title":"Convergence of Sparse Variational Inference in Gaussian Processes Regression","venue":null,"work_id":"7ccfa9c3-e9e3-4759-b201-dea41aee5e9f","year":2020},"citing_paper":{"arxiv_id":"2608.12603","last_updated":"2026-08-12T21:26:39Z","snapshot_observed_at":"2026-08-20T17:59:54.784427Z","submitted_at":"2026-08-12T21:26:39Z","title":"Hierarchical Bayesian Calibration with Bayesian Committee Machine","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-16T00:11:15.272768Z"},"links":{"citing_paper":"/paper/2608.12603"},"observation_digest":"sha256:c8ecb4e0d507f73e6a6b6453b4aedc8b13162db4df72276644dcd4550b06f3d6","observation_id":"f2bf5315-cd19-4dac-9d0e-e794a64a9ad0","resolution":{"observed_at":"2026-08-16T00:11:15.934929Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1214/26-ba1583.full","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T00:11:15.419627Z","title":"Adaptive Sparse Variational Approximations for Gaussian Process Regression","venue":null,"work_id":"3e490d90-eb4b-42fb-ba9b-c6a43a110cc7","year":2025},"citing_paper":{"arxiv_id":"2608.12603","last_updated":"2026-08-12T21:26:39Z","snapshot_observed_at":"2026-08-20T17:59:54.784427Z","submitted_at":"2026-08-12T21:26:39Z","title":"Hierarchical Bayesian Calibration with Bayesian Committee Machine","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-16T00:11:15.276759Z"},"links":{"citing_paper":"/paper/2608.12603"},"observation_digest":"sha256:bf5c38654c2354bb87eb05349ff254a81d144a0bd506d7221866f60810501b2d","observation_id":"30c16bc7-2ab5-44ea-87d3-a1a9262906d0","resolution":{"observed_at":"2026-08-16T00:11:15.423337Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"stable/2345768","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T00:11:15.572270Z","title":"Estimation and Model Identification for Continuous Spatial Processes","venue":null,"work_id":"e91f9045-5cf4-419b-b0e9-c46f572f3a91","year":1988},"citing_paper":{"arxiv_id":"2608.12603","last_updated":"2026-08-12T21:26:39Z","snapshot_observed_at":"2026-08-20T17:59:54.784427Z","submitted_at":"2026-08-12T21:26:39Z","title":"Hierarchical Bayesian Calibration with Bayesian Committee Machine","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-16T00:11:15.280092Z"},"links":{"citing_paper":"/paper/2608.12603"},"observation_digest":"sha256:d39472d9225e440228f7d50ab5708a041c04219fdd607999c8da2bf4c3b8a7b6","observation_id":"c0d4bf1b-bb6d-4dfb-b03c-e2fcb314cb8f","resolution":{"observed_at":"2026-08-16T00:11:15.577611Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1214/19-sts755.full","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T00:11:15.408081Z","title":"A General Framework for Vecchia Approximations of Gaussian Processes","venue":null,"work_id":"b8e51392-4257-4286-9d06-c5ab2cc073be","year":2021},"citing_paper":{"arxiv_id":"2608.12603","last_updated":"2026-08-12T21:26:39Z","snapshot_observed_at":"2026-08-20T17:59:54.784427Z","submitted_at":"2026-08-12T21:26:39Z","title":"Hierarchical Bayesian Calibration with Bayesian Committee Machine","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-16T00:11:15.284011Z"},"links":{"citing_paper":"/paper/2608.12603"},"observation_digest":"sha256:25810492c0cb7ba404ed6261a9eafac4aa6ba25d18a03bf57a238d3a7f28b064","observation_id":"52f217be-09b4-4001-a558-12f3cf9fb5ca","resolution":{"observed_at":"2026-08-16T00:11:15.411849Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T00:11:15.920609Z","title":"Using the Nyström Method to Speed Up Kernel Machines","venue":null,"work_id":"13c28d67-c697-4681-891f-317d4834de45","year":2000},"citing_paper":{"arxiv_id":"2608.12603","last_updated":"2026-08-12T21:26:39Z","snapshot_observed_at":"2026-08-20T17:59:54.784427Z","submitted_at":"2026-08-12T21:26:39Z","title":"Hierarchical Bayesian Calibration with Bayesian Committee Machine","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-16T00:11:15.287526Z"},"links":{"citing_paper":"/paper/2608.12603"},"observation_digest":"sha256:74e3c2e3e0388cf157e8c7b269ee831ce3ce328d80d8b85b3b62844667509429","observation_id":"4fd91f19-4ca3-48be-b506-da2f28d94b88","resolution":{"observed_at":"2026-08-16T00:11:15.924182Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T00:11:15.910480Z","title":"When Gaussian Process Meets Big Data: A Review of Scalable GPs","venue":null,"work_id":"2d917dc1-51aa-427c-bcbe-4f51bf96ff2a","year":2020},"citing_paper":{"arxiv_id":"2608.12603","last_updated":"2026-08-12T21:26:39Z","snapshot_observed_at":"2026-08-20T17:59:54.784427Z","submitted_at":"2026-08-12T21:26:39Z","title":"Hierarchical Bayesian Calibration with Bayesian Committee Machine","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-16T00:11:15.291581Z"},"links":{"citing_paper":"/paper/2608.12603"},"observation_digest":"sha256:2d95175a46ad70005fd975b57c20170d8e08676f53c99ba10c90501f667b1c84","observation_id":"5e6632db-06f8-4860-a916-8197c95674cb","resolution":{"observed_at":"2026-08-16T00:11:15.913991Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T00:11:15.899710Z","title":"A Bayesian Committee Machine","venue":null,"work_id":"c5a87ba7-0d09-4ec7-877c-24bb93a00303","year":2000},"citing_paper":{"arxiv_id":"2608.12603","last_updated":"2026-08-12T21:26:39Z","snapshot_observed_at":"2026-08-20T17:59:54.784427Z","submitted_at":"2026-08-12T21:26:39Z","title":"Hierarchical Bayesian Calibration with Bayesian Committee Machine","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-16T00:11:15.294945Z"},"links":{"citing_paper":"/paper/2608.12603"},"observation_digest":"sha256:ed14391388f3034d8332479e766033afd3da79ccc902a0a2e98f677ee3c42014","observation_id":"03e1ef2b-4e3b-4a45-a585-2ab1f57bcc41","resolution":{"observed_at":"2026-08-16T00:11:15.903383Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T00:11:15.889967Z","title":"Gaussian Processes for Regression","venue":null,"work_id":"c4194deb-8bda-4d71-a654-d4ee951b560b","year":1995},"citing_paper":{"arxiv_id":"2608.12603","last_updated":"2026-08-12T21:26:39Z","snapshot_observed_at":"2026-08-20T17:59:54.784427Z","submitted_at":"2026-08-12T21:26:39Z","title":"Hierarchical Bayesian Calibration with Bayesian Committee Machine","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-16T00:11:15.298225Z"},"links":{"citing_paper":"/paper/2608.12603"},"observation_digest":"sha256:5fe44f5a7dd31451046fc08271d46e9a8073a19e8dff3ee741a6f84644211a2b","observation_id":"f1c58954-33bc-4341-b40b-7dbc76441476","resolution":{"observed_at":"2026-08-16T00:11:15.893483Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1214/06-ba117a.full","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T00:11:15.396331Z","title":"Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper)","venue":null,"work_id":"07108869-c74d-4fce-b934-3efbe2af9474","year":2006},"citing_paper":{"arxiv_id":"2608.12603","last_updated":"2026-08-12T21:26:39Z","snapshot_observed_at":"2026-08-20T17:59:54.784427Z","submitted_at":"2026-08-12T21:26:39Z","title":"Hierarchical Bayesian Calibration with Bayesian Committee Machine","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-16T00:11:15.301439Z"},"links":{"citing_paper":"/paper/2608.12603"},"observation_digest":"sha256:c35250ef16f5ce5eca6a3f71213e28ca01230df7083f30f69b50f7c531d901bc","observation_id":"7f44b30b-a9b7-417d-9431-d6c93a8dcaec","resolution":{"observed_at":"2026-08-16T00:11:15.400357Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T00:11:15.880030Z","title":"Generating random correlation matrices based on vines and extended onion method","venue":null,"work_id":"84d4583e-98eb-4794-9b47-b38d95f9976f","year":2009},"citing_paper":{"arxiv_id":"2608.12603","last_updated":"2026-08-12T21:26:39Z","snapshot_observed_at":"2026-08-20T17:59:54.784427Z","submitted_at":"2026-08-12T21:26:39Z","title":"Hierarchical Bayesian Calibration with Bayesian Committee Machine","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-16T00:11:15.304795Z"},"links":{"citing_paper":"/paper/2608.12603"},"observation_digest":"sha256:110b4d6ac0bfbf51f18764b09631f021760afe1d0a0d56c8d2d74788a70410b4","observation_id":"507fcebb-e17c-46a7-91fa-0391703d3062","resolution":{"observed_at":"2026-08-16T00:11:15.883799Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T00:11:15.869519Z","title":"The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo","venue":null,"work_id":"75881f20-99d5-43d1-81c9-d91d08e9406e","year":2014},"citing_paper":{"arxiv_id":"2608.12603","last_updated":"2026-08-12T21:26:39Z","snapshot_observed_at":"2026-08-20T17:59:54.784427Z","submitted_at":"2026-08-12T21:26:39Z","title":"Hierarchical Bayesian Calibration with Bayesian Committee Machine","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-16T00:11:15.308065Z"},"links":{"citing_paper":"/paper/2608.12603"},"observation_digest":"sha256:2e02a4afcfa8dc8515d88873a48fa771b41b4fe3534deecb673abb8cd9c76059","observation_id":"a89de6c3-387e-42ef-b140-55cda4d80af1","resolution":{"observed_at":"2026-08-16T00:11:15.873055Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T00:11:15.859561Z","title":"Turing: A Language for Flexible Probabilistic Inference","venue":null,"work_id":"2606b76f-4427-4eca-8879-7ce1a46795a3","year":2018},"citing_paper":{"arxiv_id":"2608.12603","last_updated":"2026-08-12T21:26:39Z","snapshot_observed_at":"2026-08-20T17:59:54.784427Z","submitted_at":"2026-08-12T21:26:39Z","title":"Hierarchical Bayesian Calibration with Bayesian Committee Machine","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-16T00:11:15.311338Z"},"links":{"citing_paper":"/paper/2608.12603"},"observation_digest":"sha256:5180a8f510038379a8be9d6ea2e4adaff5b6f970ec8fa823324f24ef0307b1a3","observation_id":"01c95101-b212-41b7-90d0-683be7168e04","resolution":{"observed_at":"2026-08-16T00:11:15.863122Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T00:11:15.847500Z","title":"AdvancedHMC.jl: A robust, modular and eﬀicient implementation of advanced HMC algo- rithms","venue":null,"work_id":"570fcbd0-5b52-4308-b9b6-7cfa6e39bf39","year":2020},"citing_paper":{"arxiv_id":"2608.12603","last_updated":"2026-08-12T21:26:39Z","snapshot_observed_at":"2026-08-20T17:59:54.784427Z","submitted_at":"2026-08-12T21:26:39Z","title":"Hierarchical Bayesian Calibration with Bayesian Committee Machine","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-16T00:11:15.314608Z"},"links":{"citing_paper":"/paper/2608.12603"},"observation_digest":"sha256:9e61ced670a599e62f56a9afc311d064246deabfae6f9317144988201b541e90","observation_id":"7ca18f44-3bc2-472b-879f-e0d4914026f9","resolution":{"observed_at":"2026-08-16T00:11:15.851618Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T00:11:15.837250Z","title":"Sparse Greedy Gaussian Process Regression","venue":null,"work_id":"0457be49-2644-4209-815c-04775fd9a47b","year":2000},"citing_paper":{"arxiv_id":"2608.12603","last_updated":"2026-08-12T21:26:39Z","snapshot_observed_at":"2026-08-20T17:59:54.784427Z","submitted_at":"2026-08-12T21:26:39Z","title":"Hierarchical Bayesian Calibration with Bayesian Committee Machine","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-16T00:11:15.317783Z"},"links":{"citing_paper":"/paper/2608.12603"},"observation_digest":"sha256:0f77d619381d043df3f23e73abce9a92d9a503434ba75184c964bf4b22202855","observation_id":"a5ef96c3-afb2-449a-b41a-791e2859e2c5","resolution":{"observed_at":"2026-08-16T00:11:15.840531Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T00:11:15.827769Z","title":"Fast Sparse Gaussian Process Methods: The Informative Vector Machine","venue":null,"work_id":"4eb310cf-b0b3-4c79-ad84-e07903626998","year":2002},"citing_paper":{"arxiv_id":"2608.12603","last_updated":"2026-08-12T21:26:39Z","snapshot_observed_at":"2026-08-20T17:59:54.784427Z","submitted_at":"2026-08-12T21:26:39Z","title":"Hierarchical Bayesian Calibration with Bayesian Committee Machine","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-16T00:11:15.321261Z"},"links":{"citing_paper":"/paper/2608.12603"},"observation_digest":"sha256:f81c91f494a5159ae5f2d7324d8f543f413e32b387bab04ec0374886e5372720","observation_id":"e43e4d2e-6035-4ff3-b650-956ed1a54e90","resolution":{"observed_at":"2026-08-16T00:11:15.831106Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T00:11:15.817502Z","title":"Distributed Gaussian processes","venue":null,"work_id":"979d7ba2-e216-4270-917e-894252894708","year":2015},"citing_paper":{"arxiv_id":"2608.12603","last_updated":"2026-08-12T21:26:39Z","snapshot_observed_at":"2026-08-20T17:59:54.784427Z","submitted_at":"2026-08-12T21:26:39Z","title":"Hierarchical Bayesian Calibration with Bayesian Committee Machine","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-16T00:11:15.324382Z"},"links":{"citing_paper":"/paper/2608.12603"},"observation_digest":"sha256:6d6ed6540fc82f3dec10a5eec3f1e76416985223c436496c19e268a7427b4bca","observation_id":"b35cf75e-7273-47ad-92d7-ccdb949d0386","resolution":{"observed_at":"2026-08-16T00:11:15.821096Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T00:11:15.327564Z","title":"Sparse On-Line Gaussian Processes","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2608.12603","last_updated":"2026-08-12T21:26:39Z","snapshot_observed_at":"2026-08-20T17:59:54.784427Z","submitted_at":"2026-08-12T21:26:39Z","title":"Hierarchical Bayesian Calibration with Bayesian Committee Machine","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-16T00:11:15.327564Z"},"links":{"citing_paper":"/paper/2608.12603"},"observation_digest":"sha256:9c1f526a043029b076c2139f2b5017ccc405534ee60a563363e5276775b33928","observation_id":"d366d911-2649-4e2f-a6ef-169430ff73dc","resolution":{"observed_at":"2026-08-16T00:11:15.327564Z","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":"2026-08-16T00:11:15.806429Z","title":"Generalized Robust Bayesian Committee Machine for Large-scale Gaussian Process Regression","venue":null,"work_id":"21031042-1631-4938-bf82-46bad039d4be","year":2018},"citing_paper":{"arxiv_id":"2608.12603","last_updated":"2026-08-12T21:26:39Z","snapshot_observed_at":"2026-08-20T17:59:54.784427Z","submitted_at":"2026-08-12T21:26:39Z","title":"Hierarchical Bayesian Calibration with Bayesian Committee Machine","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-16T00:11:15.330633Z"},"links":{"citing_paper":"/paper/2608.12603"},"observation_digest":"sha256:38f7bfd284bdbeaa694c113a37ec7a6e4a6c33e593fe4ce6e80dd3c07d04773a","observation_id":"8c3c204e-1532-4db2-b784-ed7ecc8c3eb1","resolution":{"observed_at":"2026-08-16T00:11:15.810475Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T00:11:15.795577Z","title":"A sparse Bayesian Committee Machine potential for oxygen-containing organic compounds","venue":null,"work_id":"ab302362-c701-4dc3-8e4b-74e6b09e90f1","year":2025},"citing_paper":{"arxiv_id":"2608.12603","last_updated":"2026-08-12T21:26:39Z","snapshot_observed_at":"2026-08-20T17:59:54.784427Z","submitted_at":"2026-08-12T21:26:39Z","title":"Hierarchical Bayesian Calibration with Bayesian Committee Machine","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-16T00:11:15.333770Z"},"links":{"citing_paper":"/paper/2608.12603"},"observation_digest":"sha256:b264b3b94d74816ec3a838a2b4ea0cb5c8b590b782a47d01197948b500b12efb","observation_id":"2bab562d-5fa7-4fe5-9f8c-87b1d12fd05e","resolution":{"observed_at":"2026-08-16T00:11:15.799721Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T00:11:15.785702Z","title":"A statistical method for tuning a computer code to a data base","venue":null,"work_id":"e24910c1-3040-4d45-a760-0b5875a575b0","year":2001},"citing_paper":{"arxiv_id":"2608.12603","last_updated":"2026-08-12T21:26:39Z","snapshot_observed_at":"2026-08-20T17:59:54.784427Z","submitted_at":"2026-08-12T21:26:39Z","title":"Hierarchical Bayesian Calibration with Bayesian Committee Machine","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-16T00:11:15.337438Z"},"links":{"citing_paper":"/paper/2608.12603"},"observation_digest":"sha256:d22320ad9c0aaaef139b66c9b3ac17f3e9d989cee7a4dc3668cd16c3bcd24aca","observation_id":"85f95846-119d-4a5d-b7ed-d2a3083f6136","resolution":{"observed_at":"2026-08-16T00:11:15.789215Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1080/15732470500254618","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T00:11:15.375797Z","title":"Investigation of reliability method formulations in DAKOTA/UQ","venue":null,"work_id":"2a7ed808-7dcd-4bd9-80e1-26e55134b6cd","year":2007},"citing_paper":{"arxiv_id":"2608.12603","last_updated":"2026-08-12T21:26:39Z","snapshot_observed_at":"2026-08-20T17:59:54.784427Z","submitted_at":"2026-08-12T21:26:39Z","title":"Hierarchical Bayesian Calibration with Bayesian Committee Machine","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-16T00:11:15.340808Z"},"links":{"citing_paper":"/paper/2608.12603"},"observation_digest":"sha256:9df310bab6d1d7aa7770f8993155dc0c2109f9ccb13bc0fa14e303e4d362c126","observation_id":"38d0c597-74f0-4860-a024-8c8efbb91755","resolution":{"observed_at":"2026-08-16T00:11:15.381279Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T00:11:15.774985Z","title":"Research Program and Recent Results at the Argonne Wakefield Accelerator Facility (A W A)","venue":null,"work_id":"8190e541-98fd-4769-acaa-b16a9f506b92","year":2017},"citing_paper":{"arxiv_id":"2608.12603","last_updated":"2026-08-12T21:26:39Z","snapshot_observed_at":"2026-08-20T17:59:54.784427Z","submitted_at":"2026-08-12T21:26:39Z","title":"Hierarchical Bayesian Calibration with Bayesian Committee Machine","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-16T00:11:15.344239Z"},"links":{"citing_paper":"/paper/2608.12603"},"observation_digest":"sha256:02acc16b0fbc6bc886d83d914e280570d4f3e0a9d4a8e1697b2670c46a96c25c","observation_id":"dee7c8d9-927d-43fa-a020-8b4519e05ef1","resolution":{"observed_at":"2026-08-16T00:11:15.779082Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1905.06654","last_updated":"2019-05-16T10:56:28Z","snapshot_observed_at":"2026-08-19T19:59:58.913822Z","submitted_at":"2019-05-16T10:56:28Z","title":"OPAL a Versatile Tool for Charged Particle Accelerator Simulations","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.06654","snapshot_observed_at":"2026-08-16T00:11:15.348582Z","title":"OPAL a Versatile Tool for Charged Particle Accelerator Simulations; 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2608.12603","last_updated":"2026-08-12T21:26:39Z","snapshot_observed_at":"2026-08-20T17:59:54.784427Z","submitted_at":"2026-08-12T21:26:39Z","title":"Hierarchical Bayesian Calibration with Bayesian Committee Machine","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-16T00:11:15.348582Z"},"links":{"cited_paper":"/paper/1905.06654","citing_paper":"/paper/2608.12603"},"observation_digest":"sha256:2abe8455e84af258a57e4551dfb96389d7d8c5d4f67fc1f7a147ea58e2c45187","observation_id":"8eb3a692-9f1a-4401-b894-c6b12d74535d","resolution":{"observed_at":"2026-08-16T00:11:15.348582Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2608.12603","last_updated":"2026-08-12T21:26:39Z","latest_version":1,"primary_category":"stat.CO","snapshot_observed_at":"2026-08-20T17:59:54.784427Z","submitted_at":"2026-08-12T21:26:39Z","title":"Hierarchical Bayesian Calibration with Bayesian Committee Machine"},"reference_resolution":{"displayed":36,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":4,"verified_exact":11,"verified_fuzzy":20},"total_outbound_references":36},"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":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2608.12603."}