{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:TAXP2C5RYZOO4HXECF665OD2LQ","short_pith_number":"pith:TAXP2C5R","canonical_record":{"source":{"id":"2501.14946","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.CO","submitted_at":"2025-01-24T22:13:18Z","cross_cats_sorted":[],"title_canon_sha256":"639df9cbe395131f614b62796979c6c740f287252d8e91154a4b5f4780db45ea","abstract_canon_sha256":"c32de83e1e90fdea596d488579c3a9d9517ce35d9260b69df00e9e8af9a46d4f"},"schema_version":"1.0"},"canonical_sha256":"982efd0bb1c65cee1ee4117deeb87a5c0f3f2c8811452f8f0e05631f869dfb71","source":{"kind":"arxiv","id":"2501.14946","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.14946","created_at":"2026-07-05T10:05:21Z"},{"alias_kind":"arxiv_version","alias_value":"2501.14946v1","created_at":"2026-07-05T10:05:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.14946","created_at":"2026-07-05T10:05:21Z"},{"alias_kind":"pith_short_12","alias_value":"TAXP2C5RYZOO","created_at":"2026-07-05T10:05:21Z"},{"alias_kind":"pith_short_16","alias_value":"TAXP2C5RYZOO4HXE","created_at":"2026-07-05T10:05:21Z"},{"alias_kind":"pith_short_8","alias_value":"TAXP2C5R","created_at":"2026-07-05T10:05:21Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:TAXP2C5RYZOO4HXECF665OD2LQ","target":"record","payload":{"canonical_record":{"source":{"id":"2501.14946","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.CO","submitted_at":"2025-01-24T22:13:18Z","cross_cats_sorted":[],"title_canon_sha256":"639df9cbe395131f614b62796979c6c740f287252d8e91154a4b5f4780db45ea","abstract_canon_sha256":"c32de83e1e90fdea596d488579c3a9d9517ce35d9260b69df00e9e8af9a46d4f"},"schema_version":"1.0"},"canonical_sha256":"982efd0bb1c65cee1ee4117deeb87a5c0f3f2c8811452f8f0e05631f869dfb71","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:05:21.250377Z","signature_b64":"5TcdYLwdg8W/2yLWei8kCjb+9t2ILEudOzCVAMjDNqkmvHDuzqRQVW+yXDk7v7SrTWO/4uogjB9VtZx9CLiYAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"982efd0bb1c65cee1ee4117deeb87a5c0f3f2c8811452f8f0e05631f869dfb71","last_reissued_at":"2026-07-05T10:05:21.249831Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:05:21.249831Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2501.14946","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:05:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rDoMu3kKOGsiFc3PCep5JjtvC356/e9MDlRMUWBzLh4KIy+/Km2FALfm05SsA1Zqt1DQjUkyTVECN557C3PMCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T04:18:14.671987Z"},"content_sha256":"72e91dd03a6e9ee5e75121398aa67ae17d713b4305178dbdd00bf366c7c90ae1","schema_version":"1.0","event_id":"sha256:72e91dd03a6e9ee5e75121398aa67ae17d713b4305178dbdd00bf366c7c90ae1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:TAXP2C5RYZOO4HXECF665OD2LQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Modernizing full posterior inference for surrogate modeling of categorical-output simulation experiments","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"stat.CO","authors_text":"Andrew Cooper, Annie S. Booth, Robert B. Gramacy","submitted_at":"2025-01-24T22:13:18Z","abstract_excerpt":"Gaussian processes (GPs) are powerful tools for nonlinear classification in which latent GPs are combined with link functions. But GPs do not scale well to large training data. This is compounded for classification where the latent GPs require Markov chain Monte Carlo integration. Consequently, fully Bayesian, sampling-based approaches had been largely abandoned. Instead, maximization-based alternatives, such as Laplace/variational inference (VI) combined with low rank approximations, are preferred. Though feasible for large training data sets, such schemes sacrifice uncertainty quantification"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.14946","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2501.14946/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:05:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NnZ9D+7uiZOak2PzuG8592VFYBKvC1/Zb38lR7IdD75deJ1Q53AqNNy3cmnyEUdKVtFA+sns7t+wLPgY612jBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T04:18:14.672511Z"},"content_sha256":"e27f56437ff7404cfe8452708d28abbc39049316afb833a879d76ca1da3f98d9","schema_version":"1.0","event_id":"sha256:e27f56437ff7404cfe8452708d28abbc39049316afb833a879d76ca1da3f98d9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TAXP2C5RYZOO4HXECF665OD2LQ/bundle.json","state_url":"https://pith.science/pith/TAXP2C5RYZOO4HXECF665OD2LQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TAXP2C5RYZOO4HXECF665OD2LQ/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-12T04:18:14Z","links":{"resolver":"https://pith.science/pith/TAXP2C5RYZOO4HXECF665OD2LQ","bundle":"https://pith.science/pith/TAXP2C5RYZOO4HXECF665OD2LQ/bundle.json","state":"https://pith.science/pith/TAXP2C5RYZOO4HXECF665OD2LQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TAXP2C5RYZOO4HXECF665OD2LQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:TAXP2C5RYZOO4HXECF665OD2LQ","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"c32de83e1e90fdea596d488579c3a9d9517ce35d9260b69df00e9e8af9a46d4f","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.CO","submitted_at":"2025-01-24T22:13:18Z","title_canon_sha256":"639df9cbe395131f614b62796979c6c740f287252d8e91154a4b5f4780db45ea"},"schema_version":"1.0","source":{"id":"2501.14946","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.14946","created_at":"2026-07-05T10:05:21Z"},{"alias_kind":"arxiv_version","alias_value":"2501.14946v1","created_at":"2026-07-05T10:05:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.14946","created_at":"2026-07-05T10:05:21Z"},{"alias_kind":"pith_short_12","alias_value":"TAXP2C5RYZOO","created_at":"2026-07-05T10:05:21Z"},{"alias_kind":"pith_short_16","alias_value":"TAXP2C5RYZOO4HXE","created_at":"2026-07-05T10:05:21Z"},{"alias_kind":"pith_short_8","alias_value":"TAXP2C5R","created_at":"2026-07-05T10:05:21Z"}],"graph_snapshots":[{"event_id":"sha256:e27f56437ff7404cfe8452708d28abbc39049316afb833a879d76ca1da3f98d9","target":"graph","created_at":"2026-07-05T10:05:21Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2501.14946/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Gaussian processes (GPs) are powerful tools for nonlinear classification in which latent GPs are combined with link functions. But GPs do not scale well to large training data. This is compounded for classification where the latent GPs require Markov chain Monte Carlo integration. Consequently, fully Bayesian, sampling-based approaches had been largely abandoned. Instead, maximization-based alternatives, such as Laplace/variational inference (VI) combined with low rank approximations, are preferred. Though feasible for large training data sets, such schemes sacrifice uncertainty quantification","authors_text":"Andrew Cooper, Annie S. Booth, Robert B. Gramacy","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.CO","submitted_at":"2025-01-24T22:13:18Z","title":"Modernizing full posterior inference for surrogate modeling of categorical-output simulation experiments"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.14946","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:72e91dd03a6e9ee5e75121398aa67ae17d713b4305178dbdd00bf366c7c90ae1","target":"record","created_at":"2026-07-05T10:05:21Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"c32de83e1e90fdea596d488579c3a9d9517ce35d9260b69df00e9e8af9a46d4f","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.CO","submitted_at":"2025-01-24T22:13:18Z","title_canon_sha256":"639df9cbe395131f614b62796979c6c740f287252d8e91154a4b5f4780db45ea"},"schema_version":"1.0","source":{"id":"2501.14946","kind":"arxiv","version":1}},"canonical_sha256":"982efd0bb1c65cee1ee4117deeb87a5c0f3f2c8811452f8f0e05631f869dfb71","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"982efd0bb1c65cee1ee4117deeb87a5c0f3f2c8811452f8f0e05631f869dfb71","first_computed_at":"2026-07-05T10:05:21.249831Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:05:21.249831Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"5TcdYLwdg8W/2yLWei8kCjb+9t2ILEudOzCVAMjDNqkmvHDuzqRQVW+yXDk7v7SrTWO/4uogjB9VtZx9CLiYAw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:05:21.250377Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.14946","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:72e91dd03a6e9ee5e75121398aa67ae17d713b4305178dbdd00bf366c7c90ae1","sha256:e27f56437ff7404cfe8452708d28abbc39049316afb833a879d76ca1da3f98d9"],"state_sha256":"81ac584cf59325f361cda1f8ba7db4d14077d2faa41685fabe835396d9b4e3df"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"O4tBxbXkPOj5mN0eF/LjHBdtOu/GkFX1oTLGky3Mus6PXw8ocrLpwBGu1QWrz3bMSFdd7YLh1XQToSxEiqWFBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-12T04:18:14.678000Z","bundle_sha256":"a98cd600e7da50edae8381090a07a34785e1d981afdb919749b5ed7cafc5a816"}}