{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:JG6JECQU4MJZMPT2NFERO7FWVV","short_pith_number":"pith:JG6JECQU","schema_version":"1.0","canonical_sha256":"49bc920a14e313963e7a6949177cb6ad454c1689555a1ae32d9e0552c0d5fde8","source":{"kind":"arxiv","id":"2412.06934","version":3},"attestation_state":"computed","paper":{"title":"A Scalable Bayesian Spatiotemporal Model for Water Level Predictions using a Nearest Neighbor Gaussian Process Approach","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"stat.AP","authors_text":"James Sweeney, Niamh Cahill, Victor Hugo Nagahama","submitted_at":"2024-12-09T19:24:18Z","abstract_excerpt":"Obtaining accurate water level predictions are essential for water resource management and implementing flood mitigation strategies. Several data-driven models can be found in the literature. However, there has been limited research with regard to addressing the challenges posed by large spatio-temporally referenced hydrological datasets, in particular, the challenges of maintaining predictive performance and uncertainty quantification. Gaussian Processes (GPs) are commonly used to capture complex space-time interactions. However, GPs are computationally expensive and suffer from poor scaling "},"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":"2412.06934","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.AP","submitted_at":"2024-12-09T19:24:18Z","cross_cats_sorted":[],"title_canon_sha256":"cc3d7ebf5caef5cd81af54c3e268b1cd225e3618ce4a2c66767393b0f9b2f80f","abstract_canon_sha256":"f36b5d9ecd819c74b478d2abe1e98a238c0bfd0f441396ad1fb2fe197daf340b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-23T02:12:31.090230Z","signature_b64":"zCy6J4kDIUWuhOS4z3fJS4Z/H7PlvoGSsEI9cllxDxpoI2WEV8xRTCxlZ5+N2MTSWEyF93zwAW3htDSEpZMyBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"49bc920a14e313963e7a6949177cb6ad454c1689555a1ae32d9e0552c0d5fde8","last_reissued_at":"2026-06-23T02:12:31.089740Z","signature_status":"signed_v1","first_computed_at":"2026-06-23T02:12:31.089740Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Scalable Bayesian Spatiotemporal Model for Water Level Predictions using a Nearest Neighbor Gaussian Process Approach","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"stat.AP","authors_text":"James Sweeney, Niamh Cahill, Victor Hugo Nagahama","submitted_at":"2024-12-09T19:24:18Z","abstract_excerpt":"Obtaining accurate water level predictions are essential for water resource management and implementing flood mitigation strategies. Several data-driven models can be found in the literature. However, there has been limited research with regard to addressing the challenges posed by large spatio-temporally referenced hydrological datasets, in particular, the challenges of maintaining predictive performance and uncertainty quantification. Gaussian Processes (GPs) are commonly used to capture complex space-time interactions. However, GPs are computationally expensive and suffer from poor scaling "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.06934","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2412.06934/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2412.06934","created_at":"2026-06-23T02:12:31.089800+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.06934v3","created_at":"2026-06-23T02:12:31.089800+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.06934","created_at":"2026-06-23T02:12:31.089800+00:00"},{"alias_kind":"pith_short_12","alias_value":"JG6JECQU4MJZ","created_at":"2026-06-23T02:12:31.089800+00:00"},{"alias_kind":"pith_short_16","alias_value":"JG6JECQU4MJZMPT2","created_at":"2026-06-23T02:12:31.089800+00:00"},{"alias_kind":"pith_short_8","alias_value":"JG6JECQU","created_at":"2026-06-23T02:12:31.089800+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/JG6JECQU4MJZMPT2NFERO7FWVV","json":"https://pith.science/pith/JG6JECQU4MJZMPT2NFERO7FWVV.json","graph_json":"https://pith.science/api/pith-number/JG6JECQU4MJZMPT2NFERO7FWVV/graph.json","events_json":"https://pith.science/api/pith-number/JG6JECQU4MJZMPT2NFERO7FWVV/events.json","paper":"https://pith.science/paper/JG6JECQU"},"agent_actions":{"view_html":"https://pith.science/pith/JG6JECQU4MJZMPT2NFERO7FWVV","download_json":"https://pith.science/pith/JG6JECQU4MJZMPT2NFERO7FWVV.json","view_paper":"https://pith.science/paper/JG6JECQU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.06934&json=true","fetch_graph":"https://pith.science/api/pith-number/JG6JECQU4MJZMPT2NFERO7FWVV/graph.json","fetch_events":"https://pith.science/api/pith-number/JG6JECQU4MJZMPT2NFERO7FWVV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JG6JECQU4MJZMPT2NFERO7FWVV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JG6JECQU4MJZMPT2NFERO7FWVV/action/storage_attestation","attest_author":"https://pith.science/pith/JG6JECQU4MJZMPT2NFERO7FWVV/action/author_attestation","sign_citation":"https://pith.science/pith/JG6JECQU4MJZMPT2NFERO7FWVV/action/citation_signature","submit_replication":"https://pith.science/pith/JG6JECQU4MJZMPT2NFERO7FWVV/action/replication_record"}},"created_at":"2026-06-23T02:12:31.089800+00:00","updated_at":"2026-06-23T02:12:31.089800+00:00"}