{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:QQCHIGZZNJBIPWFIOYRBNGJEUM","short_pith_number":"pith:QQCHIGZZ","canonical_record":{"source":{"id":"1812.10236","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2018-12-26T05:55:56Z","cross_cats_sorted":[],"title_canon_sha256":"10de2fc3c23f4818ec627431808cc4241819f6cea90d7b4f6db13bcfb52c89da","abstract_canon_sha256":"c268e7b7340334d7adcabba9f6c5ec21f5e4ceab49382baf23f9c4957146d18e"},"schema_version":"1.0"},"canonical_sha256":"8404741b396a4287d8a87622169924a33d97a0300fdb426617d4622b4fcf934c","source":{"kind":"arxiv","id":"1812.10236","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1812.10236","created_at":"2026-05-17T23:57:24Z"},{"alias_kind":"arxiv_version","alias_value":"1812.10236v1","created_at":"2026-05-17T23:57:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.10236","created_at":"2026-05-17T23:57:24Z"},{"alias_kind":"pith_short_12","alias_value":"QQCHIGZZNJBI","created_at":"2026-05-18T12:32:46Z"},{"alias_kind":"pith_short_16","alias_value":"QQCHIGZZNJBIPWFI","created_at":"2026-05-18T12:32:46Z"},{"alias_kind":"pith_short_8","alias_value":"QQCHIGZZ","created_at":"2026-05-18T12:32:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:QQCHIGZZNJBIPWFIOYRBNGJEUM","target":"record","payload":{"canonical_record":{"source":{"id":"1812.10236","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2018-12-26T05:55:56Z","cross_cats_sorted":[],"title_canon_sha256":"10de2fc3c23f4818ec627431808cc4241819f6cea90d7b4f6db13bcfb52c89da","abstract_canon_sha256":"c268e7b7340334d7adcabba9f6c5ec21f5e4ceab49382baf23f9c4957146d18e"},"schema_version":"1.0"},"canonical_sha256":"8404741b396a4287d8a87622169924a33d97a0300fdb426617d4622b4fcf934c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:57:24.708834Z","signature_b64":"7IzZCzs2ybiqCAYhoEnasdEOoViAxo4ZRxojXpzfOb/G9idu0NhIHtwlduviklRP12OWElyBY96dMhgz8YsSBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8404741b396a4287d8a87622169924a33d97a0300fdb426617d4622b4fcf934c","last_reissued_at":"2026-05-17T23:57:24.708253Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:57:24.708253Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1812.10236","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":"2026-05-17T23:57:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7t5gnw6B9Bw7gJeGjfcfjLQ/hklhaMGvOMfNS3N8A/B5LtiVGm1EgXoaVX0LV+cSdK4PzTU622sq6tAVasbkCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T22:37:55.804014Z"},"content_sha256":"d6483692552e944379ee4f06718e1ee491ec5936aff673a564e296426cfc6b24","schema_version":"1.0","event_id":"sha256:d6483692552e944379ee4f06718e1ee491ec5936aff673a564e296426cfc6b24"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:QQCHIGZZNJBIPWFIOYRBNGJEUM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Comparing Spatial Regression to Random Forests for Large Environmental Data Sets","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.AP","authors_text":"Anthony R. Olsen, Eric W. Fox, Jay M. Ver Hoef","submitted_at":"2018-12-26T05:55:56Z","abstract_excerpt":"Environmental data may be \"large\" due to number of records, number of covariates, or both. Random forests has a reputation for good predictive performance when using many covariates with nonlinear relationships, whereas spatial regression, when using reduced rank methods, has a reputation for good predictive performance when using many records that are spatially autocorrelated. In this study, we compare these two techniques using a data set containing the macroinvertebrate multimetric index (MMI) at 1859 stream sites with over 200 landscape covariates. A primary application is mapping MMI pred"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.10236","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":"2026-05-17T23:57:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yreprBsxxMxbJd/k294R6QM8XQVZCFoQ8p4+O+PFmfa0RbsFR15cInpQ9LR6IvlvLMihLt9UOclyrMQGPF+LCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T22:37:55.804370Z"},"content_sha256":"3c74e62aa4390f9797a5ca91714e1352a149d3a5610bc338af3f281493d178e1","schema_version":"1.0","event_id":"sha256:3c74e62aa4390f9797a5ca91714e1352a149d3a5610bc338af3f281493d178e1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QQCHIGZZNJBIPWFIOYRBNGJEUM/bundle.json","state_url":"https://pith.science/pith/QQCHIGZZNJBIPWFIOYRBNGJEUM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QQCHIGZZNJBIPWFIOYRBNGJEUM/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":"2026-06-01T22:37:55Z","links":{"resolver":"https://pith.science/pith/QQCHIGZZNJBIPWFIOYRBNGJEUM","bundle":"https://pith.science/pith/QQCHIGZZNJBIPWFIOYRBNGJEUM/bundle.json","state":"https://pith.science/pith/QQCHIGZZNJBIPWFIOYRBNGJEUM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QQCHIGZZNJBIPWFIOYRBNGJEUM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:QQCHIGZZNJBIPWFIOYRBNGJEUM","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":"c268e7b7340334d7adcabba9f6c5ec21f5e4ceab49382baf23f9c4957146d18e","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2018-12-26T05:55:56Z","title_canon_sha256":"10de2fc3c23f4818ec627431808cc4241819f6cea90d7b4f6db13bcfb52c89da"},"schema_version":"1.0","source":{"id":"1812.10236","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1812.10236","created_at":"2026-05-17T23:57:24Z"},{"alias_kind":"arxiv_version","alias_value":"1812.10236v1","created_at":"2026-05-17T23:57:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.10236","created_at":"2026-05-17T23:57:24Z"},{"alias_kind":"pith_short_12","alias_value":"QQCHIGZZNJBI","created_at":"2026-05-18T12:32:46Z"},{"alias_kind":"pith_short_16","alias_value":"QQCHIGZZNJBIPWFI","created_at":"2026-05-18T12:32:46Z"},{"alias_kind":"pith_short_8","alias_value":"QQCHIGZZ","created_at":"2026-05-18T12:32:46Z"}],"graph_snapshots":[{"event_id":"sha256:3c74e62aa4390f9797a5ca91714e1352a149d3a5610bc338af3f281493d178e1","target":"graph","created_at":"2026-05-17T23:57:24Z","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":"Environmental data may be \"large\" due to number of records, number of covariates, or both. Random forests has a reputation for good predictive performance when using many covariates with nonlinear relationships, whereas spatial regression, when using reduced rank methods, has a reputation for good predictive performance when using many records that are spatially autocorrelated. In this study, we compare these two techniques using a data set containing the macroinvertebrate multimetric index (MMI) at 1859 stream sites with over 200 landscape covariates. A primary application is mapping MMI pred","authors_text":"Anthony R. Olsen, Eric W. Fox, Jay M. Ver Hoef","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2018-12-26T05:55:56Z","title":"Comparing Spatial Regression to Random Forests for Large Environmental Data Sets"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.10236","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:d6483692552e944379ee4f06718e1ee491ec5936aff673a564e296426cfc6b24","target":"record","created_at":"2026-05-17T23:57:24Z","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":"c268e7b7340334d7adcabba9f6c5ec21f5e4ceab49382baf23f9c4957146d18e","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2018-12-26T05:55:56Z","title_canon_sha256":"10de2fc3c23f4818ec627431808cc4241819f6cea90d7b4f6db13bcfb52c89da"},"schema_version":"1.0","source":{"id":"1812.10236","kind":"arxiv","version":1}},"canonical_sha256":"8404741b396a4287d8a87622169924a33d97a0300fdb426617d4622b4fcf934c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8404741b396a4287d8a87622169924a33d97a0300fdb426617d4622b4fcf934c","first_computed_at":"2026-05-17T23:57:24.708253Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:57:24.708253Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"7IzZCzs2ybiqCAYhoEnasdEOoViAxo4ZRxojXpzfOb/G9idu0NhIHtwlduviklRP12OWElyBY96dMhgz8YsSBQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:57:24.708834Z","signed_message":"canonical_sha256_bytes"},"source_id":"1812.10236","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d6483692552e944379ee4f06718e1ee491ec5936aff673a564e296426cfc6b24","sha256:3c74e62aa4390f9797a5ca91714e1352a149d3a5610bc338af3f281493d178e1"],"state_sha256":"6859ed939e5420c8590ef62c7c6164a363e75665ca291f35e3c9b68013afecf5"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Mtc4aUD73qTboIu6BoZN7xE6Lfamd1Mtx51YJacVy4FzBD+hoFzt9TBntEvxwlFR2Fn1OHIX/O9uv7j8/PjQBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-01T22:37:55.806355Z","bundle_sha256":"cb226a572a2930dc1e5bff895e315c68d959e4d203390bb7d1787ae268915aa3"}}