{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2008:AKQYD64MB44KIRRMXX5FF7HJ3L","short_pith_number":"pith:AKQYD64M","schema_version":"1.0","canonical_sha256":"02a181fb8c0f38a4462cbdfa52fce9dae02abbc9a77e655cb778de72649c67f5","source":{"kind":"arxiv","id":"0809.3918","version":2},"attestation_state":"computed","paper":{"title":"Multilevel Discretized Random Field Models with \"Spin\" Correlations for the Simulation of Environmental Spatial Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.PR"],"primary_cat":"stat.AP","authors_text":"Dionissios T. Hristopulos, Milan \\v{Z}ukovi\\v{c}","submitted_at":"2008-09-23T13:54:56Z","abstract_excerpt":"A problem of practical significance is the analysis of large, spatially distributed data sets. The problem is more challenging for variables that follow non-Gaussian distributions. We show that the spatial correlations between variables can be captured by interactions between \"spins\". The spins represent multilevel discretizations of the initial field with respect to a number of pre-defined thresholds. The spatial dependence between the \"spins\" is imposed by means of short-range interactions. We present two approaches, inspired by the Ising and Potts models, that generate conditional simulatio"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"0809.3918","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2008-09-23T13:54:56Z","cross_cats_sorted":["math.PR"],"title_canon_sha256":"44dcd0f42873b6e48cf541a8d7b43a1235eb99f80d6b51907ee9978f9a254e80","abstract_canon_sha256":"8f54cfbe193a1195aec376a118af7dffa416c3eefc5e6e043ab18b10c04ae472"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:38:03.072140Z","signature_b64":"TQHfL1hCanX0UdjD6ZFv5+KUgNr+wiIdrc99KanP+To4bgenTj4sb0HNMAvp+Euuea9H9P+aFejxX2Xwcz3SBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"02a181fb8c0f38a4462cbdfa52fce9dae02abbc9a77e655cb778de72649c67f5","last_reissued_at":"2026-05-18T03:38:03.071598Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:38:03.071598Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multilevel Discretized Random Field Models with \"Spin\" Correlations for the Simulation of Environmental Spatial Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.PR"],"primary_cat":"stat.AP","authors_text":"Dionissios T. Hristopulos, Milan \\v{Z}ukovi\\v{c}","submitted_at":"2008-09-23T13:54:56Z","abstract_excerpt":"A problem of practical significance is the analysis of large, spatially distributed data sets. The problem is more challenging for variables that follow non-Gaussian distributions. We show that the spatial correlations between variables can be captured by interactions between \"spins\". The spins represent multilevel discretizations of the initial field with respect to a number of pre-defined thresholds. The spatial dependence between the \"spins\" is imposed by means of short-range interactions. We present two approaches, inspired by the Ising and Potts models, that generate conditional simulatio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"0809.3918","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"0809.3918","created_at":"2026-05-18T03:38:03.071678+00:00"},{"alias_kind":"arxiv_version","alias_value":"0809.3918v2","created_at":"2026-05-18T03:38:03.071678+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.0809.3918","created_at":"2026-05-18T03:38:03.071678+00:00"},{"alias_kind":"pith_short_12","alias_value":"AKQYD64MB44K","created_at":"2026-05-18T12:25:56.245647+00:00"},{"alias_kind":"pith_short_16","alias_value":"AKQYD64MB44KIRRM","created_at":"2026-05-18T12:25:56.245647+00:00"},{"alias_kind":"pith_short_8","alias_value":"AKQYD64M","created_at":"2026-05-18T12:25:56.245647+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AKQYD64MB44KIRRMXX5FF7HJ3L","json":"https://pith.science/pith/AKQYD64MB44KIRRMXX5FF7HJ3L.json","graph_json":"https://pith.science/api/pith-number/AKQYD64MB44KIRRMXX5FF7HJ3L/graph.json","events_json":"https://pith.science/api/pith-number/AKQYD64MB44KIRRMXX5FF7HJ3L/events.json","paper":"https://pith.science/paper/AKQYD64M"},"agent_actions":{"view_html":"https://pith.science/pith/AKQYD64MB44KIRRMXX5FF7HJ3L","download_json":"https://pith.science/pith/AKQYD64MB44KIRRMXX5FF7HJ3L.json","view_paper":"https://pith.science/paper/AKQYD64M","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=0809.3918&json=true","fetch_graph":"https://pith.science/api/pith-number/AKQYD64MB44KIRRMXX5FF7HJ3L/graph.json","fetch_events":"https://pith.science/api/pith-number/AKQYD64MB44KIRRMXX5FF7HJ3L/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AKQYD64MB44KIRRMXX5FF7HJ3L/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AKQYD64MB44KIRRMXX5FF7HJ3L/action/storage_attestation","attest_author":"https://pith.science/pith/AKQYD64MB44KIRRMXX5FF7HJ3L/action/author_attestation","sign_citation":"https://pith.science/pith/AKQYD64MB44KIRRMXX5FF7HJ3L/action/citation_signature","submit_replication":"https://pith.science/pith/AKQYD64MB44KIRRMXX5FF7HJ3L/action/replication_record"}},"created_at":"2026-05-18T03:38:03.071678+00:00","updated_at":"2026-05-18T03:38:03.071678+00:00"}