{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:VL3KJ756L6QVDKJAP2JRYABTO2","short_pith_number":"pith:VL3KJ756","schema_version":"1.0","canonical_sha256":"aaf6a4ffbe5fa151a9207e931c00337685ecad735a9dd66b7ce6818ad16e124f","source":{"kind":"arxiv","id":"1603.07409","version":2},"attestation_state":"computed","paper":{"title":"Joint hierarchical models for sparsely sampled high-dimensional LiDAR and forest variables","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.AP","authors_text":"Andrew O. Finley, Bruce D. Cook, Chad Babcock, Sudipto Banerjee, Yuzhen Zhou","submitted_at":"2016-03-24T01:42:19Z","abstract_excerpt":"Recent advancements in remote sensing technology, specifically Light Detection and Ranging (LiDAR) sensors, provide the data needed to quantify forest characteristics at a fine spatial resolution over large geographic domains. From an inferential standpoint, there is interest in prediction and interpolation of the often sparsely sampled and spatially misaligned LiDAR signals and forest variables. We propose a fully process-based Bayesian hierarchical model for above ground biomass (AGB) and LiDAR signals. The process-based framework offers richness in inferential capabilities, e.g., inference "},"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":"1603.07409","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2016-03-24T01:42:19Z","cross_cats_sorted":[],"title_canon_sha256":"7cdd44440d640292e061c0092480b865d5f335153eb1ac3192c82158b2591a13","abstract_canon_sha256":"fffbe2b6fd04813c3b93c37bd850f8027f45ac0e6bb05cd809b2805c09093f70"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:55:52.052768Z","signature_b64":"kR+sUbxA/bVDhQwY83O7WmCgnlnzaF7KzgpPFW03+++aWsrUqgL22LUVhWYsD68c55YCvSMF1BXBnsMf7J4sDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"aaf6a4ffbe5fa151a9207e931c00337685ecad735a9dd66b7ce6818ad16e124f","last_reissued_at":"2026-05-18T00:55:52.052091Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:55:52.052091Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Joint hierarchical models for sparsely sampled high-dimensional LiDAR and forest variables","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.AP","authors_text":"Andrew O. Finley, Bruce D. Cook, Chad Babcock, Sudipto Banerjee, Yuzhen Zhou","submitted_at":"2016-03-24T01:42:19Z","abstract_excerpt":"Recent advancements in remote sensing technology, specifically Light Detection and Ranging (LiDAR) sensors, provide the data needed to quantify forest characteristics at a fine spatial resolution over large geographic domains. From an inferential standpoint, there is interest in prediction and interpolation of the often sparsely sampled and spatially misaligned LiDAR signals and forest variables. We propose a fully process-based Bayesian hierarchical model for above ground biomass (AGB) and LiDAR signals. The process-based framework offers richness in inferential capabilities, e.g., inference "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1603.07409","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":"1603.07409","created_at":"2026-05-18T00:55:52.052208+00:00"},{"alias_kind":"arxiv_version","alias_value":"1603.07409v2","created_at":"2026-05-18T00:55:52.052208+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1603.07409","created_at":"2026-05-18T00:55:52.052208+00:00"},{"alias_kind":"pith_short_12","alias_value":"VL3KJ756L6QV","created_at":"2026-05-18T12:30:48.956258+00:00"},{"alias_kind":"pith_short_16","alias_value":"VL3KJ756L6QVDKJA","created_at":"2026-05-18T12:30:48.956258+00:00"},{"alias_kind":"pith_short_8","alias_value":"VL3KJ756","created_at":"2026-05-18T12:30:48.956258+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/VL3KJ756L6QVDKJAP2JRYABTO2","json":"https://pith.science/pith/VL3KJ756L6QVDKJAP2JRYABTO2.json","graph_json":"https://pith.science/api/pith-number/VL3KJ756L6QVDKJAP2JRYABTO2/graph.json","events_json":"https://pith.science/api/pith-number/VL3KJ756L6QVDKJAP2JRYABTO2/events.json","paper":"https://pith.science/paper/VL3KJ756"},"agent_actions":{"view_html":"https://pith.science/pith/VL3KJ756L6QVDKJAP2JRYABTO2","download_json":"https://pith.science/pith/VL3KJ756L6QVDKJAP2JRYABTO2.json","view_paper":"https://pith.science/paper/VL3KJ756","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1603.07409&json=true","fetch_graph":"https://pith.science/api/pith-number/VL3KJ756L6QVDKJAP2JRYABTO2/graph.json","fetch_events":"https://pith.science/api/pith-number/VL3KJ756L6QVDKJAP2JRYABTO2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VL3KJ756L6QVDKJAP2JRYABTO2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VL3KJ756L6QVDKJAP2JRYABTO2/action/storage_attestation","attest_author":"https://pith.science/pith/VL3KJ756L6QVDKJAP2JRYABTO2/action/author_attestation","sign_citation":"https://pith.science/pith/VL3KJ756L6QVDKJAP2JRYABTO2/action/citation_signature","submit_replication":"https://pith.science/pith/VL3KJ756L6QVDKJAP2JRYABTO2/action/replication_record"}},"created_at":"2026-05-18T00:55:52.052208+00:00","updated_at":"2026-05-18T00:55:52.052208+00:00"}