{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:2D2KY5E7DFOMKDVPDZAQWT3YA3","short_pith_number":"pith:2D2KY5E7","schema_version":"1.0","canonical_sha256":"d0f4ac749f195cc50eaf1e410b4f7806edc0920374758557c0c2ca41eecd291a","source":{"kind":"arxiv","id":"1809.04389","version":2},"attestation_state":"computed","paper":{"title":"Spatio-Temporal Data Fusion for Massive Sea Surface Temperature Data from MODIS and AMSR-E Instruments","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP","stat.CO"],"primary_cat":"stat.ME","authors_text":"Emily L. Kang, Pulong Ma","submitted_at":"2018-09-12T12:54:06Z","abstract_excerpt":"Remote sensing data have been widely used to study various geophysical processes. With the advances in remote-sensing technology, massive amount of remote sensing data are collected in space over time. Different satellite instruments typically have different footprints, measurement-error characteristics, and data coverages. To combine datasets from different satellite instruments, we propose a dynamic fused Gaussian process (DFGP) model that enables fast statistical inference such as filtering and smoothing for massive spatio-temporal datasets in a data-fusion context. Based upon a spatio-temp"},"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":"1809.04389","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2018-09-12T12:54:06Z","cross_cats_sorted":["stat.AP","stat.CO"],"title_canon_sha256":"6b6e83e1cf55da1da01ea7a4c51e8a26f722c0921844f11474ce5381a20d56fc","abstract_canon_sha256":"fcd5159ee8b3e9c12df7a1bf887bb0a7a97b277a4ab9d78dbe9e9c2c0bafccfc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:43:56.530901Z","signature_b64":"zMMGnTM+VnttGZZeTkDpgodr+q3/lBnbR6WxfcCT3vKYF28WnJpUX4wKz0p7YzNA50g5ECJtv5CAV/UTFv8WDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d0f4ac749f195cc50eaf1e410b4f7806edc0920374758557c0c2ca41eecd291a","last_reissued_at":"2026-05-17T23:43:56.530201Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:43:56.530201Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Spatio-Temporal Data Fusion for Massive Sea Surface Temperature Data from MODIS and AMSR-E Instruments","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP","stat.CO"],"primary_cat":"stat.ME","authors_text":"Emily L. Kang, Pulong Ma","submitted_at":"2018-09-12T12:54:06Z","abstract_excerpt":"Remote sensing data have been widely used to study various geophysical processes. With the advances in remote-sensing technology, massive amount of remote sensing data are collected in space over time. Different satellite instruments typically have different footprints, measurement-error characteristics, and data coverages. To combine datasets from different satellite instruments, we propose a dynamic fused Gaussian process (DFGP) model that enables fast statistical inference such as filtering and smoothing for massive spatio-temporal datasets in a data-fusion context. Based upon a spatio-temp"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.04389","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":"1809.04389","created_at":"2026-05-17T23:43:56.530341+00:00"},{"alias_kind":"arxiv_version","alias_value":"1809.04389v2","created_at":"2026-05-17T23:43:56.530341+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.04389","created_at":"2026-05-17T23:43:56.530341+00:00"},{"alias_kind":"pith_short_12","alias_value":"2D2KY5E7DFOM","created_at":"2026-05-18T12:31:59.375834+00:00"},{"alias_kind":"pith_short_16","alias_value":"2D2KY5E7DFOMKDVP","created_at":"2026-05-18T12:31:59.375834+00:00"},{"alias_kind":"pith_short_8","alias_value":"2D2KY5E7","created_at":"2026-05-18T12:31:59.375834+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/2D2KY5E7DFOMKDVPDZAQWT3YA3","json":"https://pith.science/pith/2D2KY5E7DFOMKDVPDZAQWT3YA3.json","graph_json":"https://pith.science/api/pith-number/2D2KY5E7DFOMKDVPDZAQWT3YA3/graph.json","events_json":"https://pith.science/api/pith-number/2D2KY5E7DFOMKDVPDZAQWT3YA3/events.json","paper":"https://pith.science/paper/2D2KY5E7"},"agent_actions":{"view_html":"https://pith.science/pith/2D2KY5E7DFOMKDVPDZAQWT3YA3","download_json":"https://pith.science/pith/2D2KY5E7DFOMKDVPDZAQWT3YA3.json","view_paper":"https://pith.science/paper/2D2KY5E7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1809.04389&json=true","fetch_graph":"https://pith.science/api/pith-number/2D2KY5E7DFOMKDVPDZAQWT3YA3/graph.json","fetch_events":"https://pith.science/api/pith-number/2D2KY5E7DFOMKDVPDZAQWT3YA3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2D2KY5E7DFOMKDVPDZAQWT3YA3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2D2KY5E7DFOMKDVPDZAQWT3YA3/action/storage_attestation","attest_author":"https://pith.science/pith/2D2KY5E7DFOMKDVPDZAQWT3YA3/action/author_attestation","sign_citation":"https://pith.science/pith/2D2KY5E7DFOMKDVPDZAQWT3YA3/action/citation_signature","submit_replication":"https://pith.science/pith/2D2KY5E7DFOMKDVPDZAQWT3YA3/action/replication_record"}},"created_at":"2026-05-17T23:43:56.530341+00:00","updated_at":"2026-05-17T23:43:56.530341+00:00"}