{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:VADK5O6RSKGDCB52DH5QSEKCIM","short_pith_number":"pith:VADK5O6R","canonical_record":{"source":{"id":"1807.01239","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2018-07-03T15:35:52Z","cross_cats_sorted":["stat.CO","stat.ME"],"title_canon_sha256":"eaf2245e6c944cb6fe7e355ebf5a87e4bf59692dc8859fd55724cfca013c09b3","abstract_canon_sha256":"e8733835cd65c1847ac2432e85e101b0b2c6cdbfc207d5103577d794604ee386"},"schema_version":"1.0"},"canonical_sha256":"a806aebbd1928c3107ba19fb091142433106ccf0430f18a2aa860727bf0871ef","source":{"kind":"arxiv","id":"1807.01239","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.01239","created_at":"2026-05-18T00:11:44Z"},{"alias_kind":"arxiv_version","alias_value":"1807.01239v1","created_at":"2026-05-18T00:11:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.01239","created_at":"2026-05-18T00:11:44Z"},{"alias_kind":"pith_short_12","alias_value":"VADK5O6RSKGD","created_at":"2026-05-18T12:32:59Z"},{"alias_kind":"pith_short_16","alias_value":"VADK5O6RSKGDCB52","created_at":"2026-05-18T12:32:59Z"},{"alias_kind":"pith_short_8","alias_value":"VADK5O6R","created_at":"2026-05-18T12:32:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:VADK5O6RSKGDCB52DH5QSEKCIM","target":"record","payload":{"canonical_record":{"source":{"id":"1807.01239","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2018-07-03T15:35:52Z","cross_cats_sorted":["stat.CO","stat.ME"],"title_canon_sha256":"eaf2245e6c944cb6fe7e355ebf5a87e4bf59692dc8859fd55724cfca013c09b3","abstract_canon_sha256":"e8733835cd65c1847ac2432e85e101b0b2c6cdbfc207d5103577d794604ee386"},"schema_version":"1.0"},"canonical_sha256":"a806aebbd1928c3107ba19fb091142433106ccf0430f18a2aa860727bf0871ef","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:11:44.817346Z","signature_b64":"bgc7xzBqrtpFCY3UYANEgyqlan9Bjf8OFxlnNRMsYDbCTV3bhnq68CbrZiS5Kfr7HTO/Bst99W4VBMNS4sNYBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a806aebbd1928c3107ba19fb091142433106ccf0430f18a2aa860727bf0871ef","last_reissued_at":"2026-05-18T00:11:44.816600Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:11:44.816600Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1807.01239","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":"2026-05-18T00:11:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9TVaabeAOT4S02TF1OJKzEgOayNYuLgXsRH9LR1Pf7P5lvFxu9TFYKAb0yTOHfAwFk17gCy13XhMwfAEodtGCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T07:48:19.100888Z"},"content_sha256":"b6bec92f799a9329780a55a3e2a188552c86e08aa2672ec02df44751d72d375e","schema_version":"1.0","event_id":"sha256:b6bec92f799a9329780a55a3e2a188552c86e08aa2672ec02df44751d72d375e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:VADK5O6RSKGDCB52DH5QSEKCIM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Bayesian Spatial Analysis of Hardwood Tree Counts in Forests via MCMC","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.CO","stat.ME"],"primary_cat":"stat.AP","authors_text":"Jeffrey S. Rosenthal, Patrick E. Brown, Reihaneh Entezari","submitted_at":"2018-07-03T15:35:52Z","abstract_excerpt":"In this paper, we perform Bayesian Inference to analyze spatial tree count data from the Timiskaming and Abitibi River forests in Ontario, Canada. We consider a Bayesian Generalized Linear Geostatistical Model and implement a Markov Chain Monte Carlo algorithm to sample from its posterior distribution. How spatial predictions for new sites in the forests change as the amount of training data is reduced is studied and compared with a Logistic Regression model without a spatial effect. Finally, we discuss a stratified sampling approach for selecting subsets of data that allows for potential bett"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.01239","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":""},"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":"2026-05-18T00:11:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2cCM0EeE3DLhM6QX/GWBo7F6Bl7aqpSPxDme+rFOouCC4hIRbSpl6EBhWc0WJFb/+2PtcHpSl5o9IZ/++wQ7AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T07:48:19.101246Z"},"content_sha256":"b587d4844ced94f63e97269d0f69889805ee9b5c5b3024ec79aefbfc9c375d86","schema_version":"1.0","event_id":"sha256:b587d4844ced94f63e97269d0f69889805ee9b5c5b3024ec79aefbfc9c375d86"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VADK5O6RSKGDCB52DH5QSEKCIM/bundle.json","state_url":"https://pith.science/pith/VADK5O6RSKGDCB52DH5QSEKCIM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VADK5O6RSKGDCB52DH5QSEKCIM/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":"2026-05-30T07:48:19Z","links":{"resolver":"https://pith.science/pith/VADK5O6RSKGDCB52DH5QSEKCIM","bundle":"https://pith.science/pith/VADK5O6RSKGDCB52DH5QSEKCIM/bundle.json","state":"https://pith.science/pith/VADK5O6RSKGDCB52DH5QSEKCIM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VADK5O6RSKGDCB52DH5QSEKCIM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:VADK5O6RSKGDCB52DH5QSEKCIM","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":"e8733835cd65c1847ac2432e85e101b0b2c6cdbfc207d5103577d794604ee386","cross_cats_sorted":["stat.CO","stat.ME"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2018-07-03T15:35:52Z","title_canon_sha256":"eaf2245e6c944cb6fe7e355ebf5a87e4bf59692dc8859fd55724cfca013c09b3"},"schema_version":"1.0","source":{"id":"1807.01239","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.01239","created_at":"2026-05-18T00:11:44Z"},{"alias_kind":"arxiv_version","alias_value":"1807.01239v1","created_at":"2026-05-18T00:11:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.01239","created_at":"2026-05-18T00:11:44Z"},{"alias_kind":"pith_short_12","alias_value":"VADK5O6RSKGD","created_at":"2026-05-18T12:32:59Z"},{"alias_kind":"pith_short_16","alias_value":"VADK5O6RSKGDCB52","created_at":"2026-05-18T12:32:59Z"},{"alias_kind":"pith_short_8","alias_value":"VADK5O6R","created_at":"2026-05-18T12:32:59Z"}],"graph_snapshots":[{"event_id":"sha256:b587d4844ced94f63e97269d0f69889805ee9b5c5b3024ec79aefbfc9c375d86","target":"graph","created_at":"2026-05-18T00:11:44Z","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":"In this paper, we perform Bayesian Inference to analyze spatial tree count data from the Timiskaming and Abitibi River forests in Ontario, Canada. We consider a Bayesian Generalized Linear Geostatistical Model and implement a Markov Chain Monte Carlo algorithm to sample from its posterior distribution. How spatial predictions for new sites in the forests change as the amount of training data is reduced is studied and compared with a Logistic Regression model without a spatial effect. Finally, we discuss a stratified sampling approach for selecting subsets of data that allows for potential bett","authors_text":"Jeffrey S. Rosenthal, Patrick E. Brown, Reihaneh Entezari","cross_cats":["stat.CO","stat.ME"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2018-07-03T15:35:52Z","title":"Bayesian Spatial Analysis of Hardwood Tree Counts in Forests via MCMC"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.01239","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:b6bec92f799a9329780a55a3e2a188552c86e08aa2672ec02df44751d72d375e","target":"record","created_at":"2026-05-18T00:11:44Z","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":"e8733835cd65c1847ac2432e85e101b0b2c6cdbfc207d5103577d794604ee386","cross_cats_sorted":["stat.CO","stat.ME"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2018-07-03T15:35:52Z","title_canon_sha256":"eaf2245e6c944cb6fe7e355ebf5a87e4bf59692dc8859fd55724cfca013c09b3"},"schema_version":"1.0","source":{"id":"1807.01239","kind":"arxiv","version":1}},"canonical_sha256":"a806aebbd1928c3107ba19fb091142433106ccf0430f18a2aa860727bf0871ef","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a806aebbd1928c3107ba19fb091142433106ccf0430f18a2aa860727bf0871ef","first_computed_at":"2026-05-18T00:11:44.816600Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:11:44.816600Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"bgc7xzBqrtpFCY3UYANEgyqlan9Bjf8OFxlnNRMsYDbCTV3bhnq68CbrZiS5Kfr7HTO/Bst99W4VBMNS4sNYBA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:11:44.817346Z","signed_message":"canonical_sha256_bytes"},"source_id":"1807.01239","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b6bec92f799a9329780a55a3e2a188552c86e08aa2672ec02df44751d72d375e","sha256:b587d4844ced94f63e97269d0f69889805ee9b5c5b3024ec79aefbfc9c375d86"],"state_sha256":"c9b93770bd6f56608d6d8509124b4a2d3384686180d179bfbd6a4eb75007bad7"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/v+Vdc2yNE6Jcq34tyBUTCrRDCK5Cfxa4QpNYUwB1kuwNrfObL4ixmT/JGZk7uHQvPQ/t1G0zn8ZfCiFV61hBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T07:48:19.103633Z","bundle_sha256":"31991301bc4aa377357f9883de0032e10a9881660c282da55a4ce7740973fa55"}}