{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:NYVUBQSNBYM36CLZLN7R3F3XW6","short_pith_number":"pith:NYVUBQSN","canonical_record":{"source":{"id":"1512.08560","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2015-12-28T23:31:20Z","cross_cats_sorted":[],"title_canon_sha256":"f8bb9951ca30042f5f85bc56d592cfbc2b3eb1fec2bc4b3ac68cc11e44b3b5c6","abstract_canon_sha256":"61a8c4d581887d1001f39d1e1f51ffd6fa4d5979a946d035ff557990dae17081"},"schema_version":"1.0"},"canonical_sha256":"6e2b40c24d0e19bf09795b7f1d9777b784a4241a61409dd5a0053e8cf68021f3","source":{"kind":"arxiv","id":"1512.08560","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1512.08560","created_at":"2026-05-18T00:49:25Z"},{"alias_kind":"arxiv_version","alias_value":"1512.08560v2","created_at":"2026-05-18T00:49:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1512.08560","created_at":"2026-05-18T00:49:25Z"},{"alias_kind":"pith_short_12","alias_value":"NYVUBQSNBYM3","created_at":"2026-05-18T12:29:34Z"},{"alias_kind":"pith_short_16","alias_value":"NYVUBQSNBYM36CLZ","created_at":"2026-05-18T12:29:34Z"},{"alias_kind":"pith_short_8","alias_value":"NYVUBQSN","created_at":"2026-05-18T12:29:34Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:NYVUBQSNBYM36CLZLN7R3F3XW6","target":"record","payload":{"canonical_record":{"source":{"id":"1512.08560","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2015-12-28T23:31:20Z","cross_cats_sorted":[],"title_canon_sha256":"f8bb9951ca30042f5f85bc56d592cfbc2b3eb1fec2bc4b3ac68cc11e44b3b5c6","abstract_canon_sha256":"61a8c4d581887d1001f39d1e1f51ffd6fa4d5979a946d035ff557990dae17081"},"schema_version":"1.0"},"canonical_sha256":"6e2b40c24d0e19bf09795b7f1d9777b784a4241a61409dd5a0053e8cf68021f3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:49:25.056891Z","signature_b64":"uu2EDe1X/5VQFfBacHTppHKbJg/QTQS4vFCh3f4024qXr/soom7THBVCa8qp5TyWLeWy76nrWxY/wtx6psiECA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6e2b40c24d0e19bf09795b7f1d9777b784a4241a61409dd5a0053e8cf68021f3","last_reissued_at":"2026-05-18T00:49:25.056130Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:49:25.056130Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1512.08560","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:49:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lShpsVkO+qhhzWfslLaG8hUHQsMDIUu9lv3WilJps0I1PKUv0Yt5lQu9wUexFB0ki3MD5vVnDdT2vJJswXupCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T14:08:30.546537Z"},"content_sha256":"72ace68e7d91699935248ac3b662fc47b95dd0ed5a8bfcc70af797da896d221e","schema_version":"1.0","event_id":"sha256:72ace68e7d91699935248ac3b662fc47b95dd0ed5a8bfcc70af797da896d221e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:NYVUBQSNBYM36CLZLN7R3F3XW6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Spatial Bayesian hierarchical modeling of precipitation extremes over a large domain","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Balaji Rajagopalan, Cameron Bracken, Linyin Cheng, Subhrendu Gangopadhyay, Will Kleiber","submitted_at":"2015-12-28T23:31:20Z","abstract_excerpt":"We propose a Bayesian hierarchical model for spatial extremes on a large domain. In the data layer a Gaussian elliptical copula having generalized extreme value (GEV) marginals is applied. Spatial dependence in the GEV parameters are captured with a latent spatial regression with spatially varying coefficients. Using a composite likelihood approach, we are able to efficiently incorporate a large precipitation dataset, which includes stations with missing data. The model is demonstrated by application to fall precipitation extremes at approximately 2600 stations covering the western United Stat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1512.08560","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"2026-05-18T00:49:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8JeV9A0nYtdF7cVRItzI3fS6Yd4zaSV3mkb22QozXYhkvSq+0W2DCgRUYys8rFRc7DRATfg9hyAFB/F9gcqNDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T14:08:30.547190Z"},"content_sha256":"82683dec77430eae5c6d0ec950a89563d6d829b20c170e84e7a844e8f521f7fa","schema_version":"1.0","event_id":"sha256:82683dec77430eae5c6d0ec950a89563d6d829b20c170e84e7a844e8f521f7fa"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NYVUBQSNBYM36CLZLN7R3F3XW6/bundle.json","state_url":"https://pith.science/pith/NYVUBQSNBYM36CLZLN7R3F3XW6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NYVUBQSNBYM36CLZLN7R3F3XW6/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":"2026-05-27T14:08:30Z","links":{"resolver":"https://pith.science/pith/NYVUBQSNBYM36CLZLN7R3F3XW6","bundle":"https://pith.science/pith/NYVUBQSNBYM36CLZLN7R3F3XW6/bundle.json","state":"https://pith.science/pith/NYVUBQSNBYM36CLZLN7R3F3XW6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NYVUBQSNBYM36CLZLN7R3F3XW6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:NYVUBQSNBYM36CLZLN7R3F3XW6","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":"61a8c4d581887d1001f39d1e1f51ffd6fa4d5979a946d035ff557990dae17081","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2015-12-28T23:31:20Z","title_canon_sha256":"f8bb9951ca30042f5f85bc56d592cfbc2b3eb1fec2bc4b3ac68cc11e44b3b5c6"},"schema_version":"1.0","source":{"id":"1512.08560","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1512.08560","created_at":"2026-05-18T00:49:25Z"},{"alias_kind":"arxiv_version","alias_value":"1512.08560v2","created_at":"2026-05-18T00:49:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1512.08560","created_at":"2026-05-18T00:49:25Z"},{"alias_kind":"pith_short_12","alias_value":"NYVUBQSNBYM3","created_at":"2026-05-18T12:29:34Z"},{"alias_kind":"pith_short_16","alias_value":"NYVUBQSNBYM36CLZ","created_at":"2026-05-18T12:29:34Z"},{"alias_kind":"pith_short_8","alias_value":"NYVUBQSN","created_at":"2026-05-18T12:29:34Z"}],"graph_snapshots":[{"event_id":"sha256:82683dec77430eae5c6d0ec950a89563d6d829b20c170e84e7a844e8f521f7fa","target":"graph","created_at":"2026-05-18T00:49:25Z","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"},"paper":{"abstract_excerpt":"We propose a Bayesian hierarchical model for spatial extremes on a large domain. In the data layer a Gaussian elliptical copula having generalized extreme value (GEV) marginals is applied. Spatial dependence in the GEV parameters are captured with a latent spatial regression with spatially varying coefficients. Using a composite likelihood approach, we are able to efficiently incorporate a large precipitation dataset, which includes stations with missing data. The model is demonstrated by application to fall precipitation extremes at approximately 2600 stations covering the western United Stat","authors_text":"Balaji Rajagopalan, Cameron Bracken, Linyin Cheng, Subhrendu Gangopadhyay, Will Kleiber","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2015-12-28T23:31:20Z","title":"Spatial Bayesian hierarchical modeling of precipitation extremes over a large domain"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1512.08560","kind":"arxiv","version":2},"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":"sha256:72ace68e7d91699935248ac3b662fc47b95dd0ed5a8bfcc70af797da896d221e","target":"record","created_at":"2026-05-18T00:49:25Z","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":"61a8c4d581887d1001f39d1e1f51ffd6fa4d5979a946d035ff557990dae17081","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2015-12-28T23:31:20Z","title_canon_sha256":"f8bb9951ca30042f5f85bc56d592cfbc2b3eb1fec2bc4b3ac68cc11e44b3b5c6"},"schema_version":"1.0","source":{"id":"1512.08560","kind":"arxiv","version":2}},"canonical_sha256":"6e2b40c24d0e19bf09795b7f1d9777b784a4241a61409dd5a0053e8cf68021f3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6e2b40c24d0e19bf09795b7f1d9777b784a4241a61409dd5a0053e8cf68021f3","first_computed_at":"2026-05-18T00:49:25.056130Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:49:25.056130Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"uu2EDe1X/5VQFfBacHTppHKbJg/QTQS4vFCh3f4024qXr/soom7THBVCa8qp5TyWLeWy76nrWxY/wtx6psiECA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:49:25.056891Z","signed_message":"canonical_sha256_bytes"},"source_id":"1512.08560","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:72ace68e7d91699935248ac3b662fc47b95dd0ed5a8bfcc70af797da896d221e","sha256:82683dec77430eae5c6d0ec950a89563d6d829b20c170e84e7a844e8f521f7fa"],"state_sha256":"bced6e1c78612c8859cc7849c32b466e5d763507e135f8ce252e183b1cac1e81"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Hvo2VeLAX40JdpkQ6xHbB97QT34r2UN3ugg2vkh4VzBWhJngK7kAsKs7m1z8PRYlhYK/NujcvwpFFzZudy5xBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T14:08:30.550613Z","bundle_sha256":"7b587014892b32bfbed9bcbd50ed1a69a8d3b3d98422dc9f7b40c20fb4bb4910"}}