{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:CIXNC5QYVTLMI7FSTNU34EYRB5","short_pith_number":"pith:CIXNC5QY","schema_version":"1.0","canonical_sha256":"122ed17618acd6c47cb29b69be13110f6b95006dd3b100bd968452e28e3530dd","source":{"kind":"arxiv","id":"2412.16375","version":1},"attestation_state":"computed","paper":{"title":"Iterative Encoding-Decoding VAEs Anomaly Detection in NOAA's DART Time Series: A Machine Learning Approach for Enhancing Data Integrity for NASA's GRACE-FO Verification and Validation","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI","physics.geo-ph"],"primary_cat":"cs.LG","authors_text":"Kevin Lee","submitted_at":"2024-12-20T22:19:11Z","abstract_excerpt":"NOAA's Deep-ocean Assessment and Reporting of Tsunamis (DART) data are critical for NASA-JPL's tsunami detection, real-time operations, and oceanographic research. However, these time-series data often contain spikes, steps, and drifts that degrade data quality and obscure essential oceanographic features. To address these anomalies, the work introduces an Iterative Encoding-Decoding Variational Autoencoders (Iterative Encoding-Decoding VAEs) model to improve the quality of DART time series. Unlike traditional filtering and thresholding methods that risk distorting inherent signal characterist"},"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":"2412.16375","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2024-12-20T22:19:11Z","cross_cats_sorted":["cs.AI","physics.geo-ph"],"title_canon_sha256":"c6892c3ee7727503a5ea866a2a59eb05ece070acabf80df423fe0f89afba683c","abstract_canon_sha256":"b9a1127f28ff06fa79651442976b5cdf8078899265ccf06881e9b9b72e020909"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:52:45.295389Z","signature_b64":"ZNhp0LvphBhRuNrmZaWb+h+4bus3YNT2rD5IKYQhjEUJOyZ644+kXMvfupDQIQTZh+OTVVYzZWrpQrl7p/38AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"122ed17618acd6c47cb29b69be13110f6b95006dd3b100bd968452e28e3530dd","last_reissued_at":"2026-07-05T09:52:45.294977Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:52:45.294977Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Iterative Encoding-Decoding VAEs Anomaly Detection in NOAA's DART Time Series: A Machine Learning Approach for Enhancing Data Integrity for NASA's GRACE-FO Verification and Validation","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI","physics.geo-ph"],"primary_cat":"cs.LG","authors_text":"Kevin Lee","submitted_at":"2024-12-20T22:19:11Z","abstract_excerpt":"NOAA's Deep-ocean Assessment and Reporting of Tsunamis (DART) data are critical for NASA-JPL's tsunami detection, real-time operations, and oceanographic research. However, these time-series data often contain spikes, steps, and drifts that degrade data quality and obscure essential oceanographic features. To address these anomalies, the work introduces an Iterative Encoding-Decoding Variational Autoencoders (Iterative Encoding-Decoding VAEs) model to improve the quality of DART time series. Unlike traditional filtering and thresholding methods that risk distorting inherent signal characterist"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.16375","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2412.16375/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":"2412.16375","created_at":"2026-07-05T09:52:45.295038+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.16375v1","created_at":"2026-07-05T09:52:45.295038+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.16375","created_at":"2026-07-05T09:52:45.295038+00:00"},{"alias_kind":"pith_short_12","alias_value":"CIXNC5QYVTLM","created_at":"2026-07-05T09:52:45.295038+00:00"},{"alias_kind":"pith_short_16","alias_value":"CIXNC5QYVTLMI7FS","created_at":"2026-07-05T09:52:45.295038+00:00"},{"alias_kind":"pith_short_8","alias_value":"CIXNC5QY","created_at":"2026-07-05T09:52:45.295038+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/CIXNC5QYVTLMI7FSTNU34EYRB5","json":"https://pith.science/pith/CIXNC5QYVTLMI7FSTNU34EYRB5.json","graph_json":"https://pith.science/api/pith-number/CIXNC5QYVTLMI7FSTNU34EYRB5/graph.json","events_json":"https://pith.science/api/pith-number/CIXNC5QYVTLMI7FSTNU34EYRB5/events.json","paper":"https://pith.science/paper/CIXNC5QY"},"agent_actions":{"view_html":"https://pith.science/pith/CIXNC5QYVTLMI7FSTNU34EYRB5","download_json":"https://pith.science/pith/CIXNC5QYVTLMI7FSTNU34EYRB5.json","view_paper":"https://pith.science/paper/CIXNC5QY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.16375&json=true","fetch_graph":"https://pith.science/api/pith-number/CIXNC5QYVTLMI7FSTNU34EYRB5/graph.json","fetch_events":"https://pith.science/api/pith-number/CIXNC5QYVTLMI7FSTNU34EYRB5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CIXNC5QYVTLMI7FSTNU34EYRB5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CIXNC5QYVTLMI7FSTNU34EYRB5/action/storage_attestation","attest_author":"https://pith.science/pith/CIXNC5QYVTLMI7FSTNU34EYRB5/action/author_attestation","sign_citation":"https://pith.science/pith/CIXNC5QYVTLMI7FSTNU34EYRB5/action/citation_signature","submit_replication":"https://pith.science/pith/CIXNC5QYVTLMI7FSTNU34EYRB5/action/replication_record"}},"created_at":"2026-07-05T09:52:45.295038+00:00","updated_at":"2026-07-05T09:52:45.295038+00:00"}