{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:TLNIP6VQR4PWLXC3ZLQ35LXOLM","short_pith_number":"pith:TLNIP6VQ","schema_version":"1.0","canonical_sha256":"9ada87fab08f1f65dc5bcae1beaeee5b11c9ef84bf0681b212c76eed26dc1dad","source":{"kind":"arxiv","id":"1804.07648","version":1},"attestation_state":"computed","paper":{"title":"Assimilation of semi-qualitative observations with a stochastic Ensemble Kalman Filter","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP","stat.ME"],"primary_cat":"math.OC","authors_text":"Abhishek Shah, Laurent Bertino, Mohamad El Gharamti","submitted_at":"2018-04-20T14:45:12Z","abstract_excerpt":"The Ensemble Kalman filter assumes the observations to be Gaussian random variables with a pre-specified mean and variance. In practice, observations may also have detection limits, for instance when a gauge has a minimum or maximum value. In such cases most data assimilation schemes discard out-of-range values, treating them as \"not a number\", at a loss of possibly useful qualitative information.\n  The current work focuses on the development of a data assimilation scheme that tackles observations with a detection limit. We present the Ensemble Kalman Filter Semi-Qualitative (EnKF-SQ) and test"},"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":"1804.07648","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2018-04-20T14:45:12Z","cross_cats_sorted":["stat.AP","stat.ME"],"title_canon_sha256":"6861c03a5b3649df87c400299a91cb545b688898e955be0a7c65c56ec0e5f35c","abstract_canon_sha256":"9242d7c3c27af6128030f302cfab681c4d2629408bf601d0c00d36d4d2ea994a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:00:56.363278Z","signature_b64":"+S56XK//HYq/NPalNpQHvRaGORsc4NUM9xjTZPRxD/psc44pQubvG4fGwx1zwdeY2Pym7+00s2qbGGcqVk8DBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9ada87fab08f1f65dc5bcae1beaeee5b11c9ef84bf0681b212c76eed26dc1dad","last_reissued_at":"2026-05-18T00:00:56.362769Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:00:56.362769Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Assimilation of semi-qualitative observations with a stochastic Ensemble Kalman Filter","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP","stat.ME"],"primary_cat":"math.OC","authors_text":"Abhishek Shah, Laurent Bertino, Mohamad El Gharamti","submitted_at":"2018-04-20T14:45:12Z","abstract_excerpt":"The Ensemble Kalman filter assumes the observations to be Gaussian random variables with a pre-specified mean and variance. In practice, observations may also have detection limits, for instance when a gauge has a minimum or maximum value. In such cases most data assimilation schemes discard out-of-range values, treating them as \"not a number\", at a loss of possibly useful qualitative information.\n  The current work focuses on the development of a data assimilation scheme that tackles observations with a detection limit. We present the Ensemble Kalman Filter Semi-Qualitative (EnKF-SQ) and test"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1804.07648","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":""},"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":"1804.07648","created_at":"2026-05-18T00:00:56.362837+00:00"},{"alias_kind":"arxiv_version","alias_value":"1804.07648v1","created_at":"2026-05-18T00:00:56.362837+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1804.07648","created_at":"2026-05-18T00:00:56.362837+00:00"},{"alias_kind":"pith_short_12","alias_value":"TLNIP6VQR4PW","created_at":"2026-05-18T12:32:53.628368+00:00"},{"alias_kind":"pith_short_16","alias_value":"TLNIP6VQR4PWLXC3","created_at":"2026-05-18T12:32:53.628368+00:00"},{"alias_kind":"pith_short_8","alias_value":"TLNIP6VQ","created_at":"2026-05-18T12:32:53.628368+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/TLNIP6VQR4PWLXC3ZLQ35LXOLM","json":"https://pith.science/pith/TLNIP6VQR4PWLXC3ZLQ35LXOLM.json","graph_json":"https://pith.science/api/pith-number/TLNIP6VQR4PWLXC3ZLQ35LXOLM/graph.json","events_json":"https://pith.science/api/pith-number/TLNIP6VQR4PWLXC3ZLQ35LXOLM/events.json","paper":"https://pith.science/paper/TLNIP6VQ"},"agent_actions":{"view_html":"https://pith.science/pith/TLNIP6VQR4PWLXC3ZLQ35LXOLM","download_json":"https://pith.science/pith/TLNIP6VQR4PWLXC3ZLQ35LXOLM.json","view_paper":"https://pith.science/paper/TLNIP6VQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1804.07648&json=true","fetch_graph":"https://pith.science/api/pith-number/TLNIP6VQR4PWLXC3ZLQ35LXOLM/graph.json","fetch_events":"https://pith.science/api/pith-number/TLNIP6VQR4PWLXC3ZLQ35LXOLM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TLNIP6VQR4PWLXC3ZLQ35LXOLM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TLNIP6VQR4PWLXC3ZLQ35LXOLM/action/storage_attestation","attest_author":"https://pith.science/pith/TLNIP6VQR4PWLXC3ZLQ35LXOLM/action/author_attestation","sign_citation":"https://pith.science/pith/TLNIP6VQR4PWLXC3ZLQ35LXOLM/action/citation_signature","submit_replication":"https://pith.science/pith/TLNIP6VQR4PWLXC3ZLQ35LXOLM/action/replication_record"}},"created_at":"2026-05-18T00:00:56.362837+00:00","updated_at":"2026-05-18T00:00:56.362837+00:00"}