{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:DZQIWIAIMRNUJFNJVKSRCBSOME","short_pith_number":"pith:DZQIWIAI","canonical_record":{"source":{"id":"1611.04702","kind":"arxiv","version":6},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2016-11-15T04:20:19Z","cross_cats_sorted":["stat.ME"],"title_canon_sha256":"dcb85257ae1aec9c3d0d1d5f009a71c06dcedb2052840023539ef5a66ebab48e","abstract_canon_sha256":"af5a9437e42508ded0005e088104b5c2d67c70280aff2073e810ae224dbf3e9f"},"schema_version":"1.0"},"canonical_sha256":"1e608b2008645b4495a9aaa511064e61111147c952cba41ab82e0b494bf8020b","source":{"kind":"arxiv","id":"1611.04702","version":6},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1611.04702","created_at":"2026-05-18T00:22:40Z"},{"alias_kind":"arxiv_version","alias_value":"1611.04702v6","created_at":"2026-05-18T00:22:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.04702","created_at":"2026-05-18T00:22:40Z"},{"alias_kind":"pith_short_12","alias_value":"DZQIWIAIMRNU","created_at":"2026-05-18T12:30:12Z"},{"alias_kind":"pith_short_16","alias_value":"DZQIWIAIMRNUJFNJ","created_at":"2026-05-18T12:30:12Z"},{"alias_kind":"pith_short_8","alias_value":"DZQIWIAI","created_at":"2026-05-18T12:30:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:DZQIWIAIMRNUJFNJVKSRCBSOME","target":"record","payload":{"canonical_record":{"source":{"id":"1611.04702","kind":"arxiv","version":6},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2016-11-15T04:20:19Z","cross_cats_sorted":["stat.ME"],"title_canon_sha256":"dcb85257ae1aec9c3d0d1d5f009a71c06dcedb2052840023539ef5a66ebab48e","abstract_canon_sha256":"af5a9437e42508ded0005e088104b5c2d67c70280aff2073e810ae224dbf3e9f"},"schema_version":"1.0"},"canonical_sha256":"1e608b2008645b4495a9aaa511064e61111147c952cba41ab82e0b494bf8020b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:22:40.212871Z","signature_b64":"q94NajbZv89gqD8pLTFKNPCx+e3lUQQZAT+d/C4yLYeMwe+ZD9g1gnHk2YINsQirytgwolvtP2g+a503BGlXDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1e608b2008645b4495a9aaa511064e61111147c952cba41ab82e0b494bf8020b","last_reissued_at":"2026-05-18T00:22:40.212382Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:22:40.212382Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1611.04702","source_version":6,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:22:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"V5DXAdyOMn+oFVaefUv3ffz10Eo1cn7AZXGyt2MuDqxh74X7XX2O1oKFmsdlwmDRFPqvzMjl8f4btaXvjBn6Dw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T21:59:07.459055Z"},"content_sha256":"0e38e25a37ef857f4393c22a14d6e9230b8cf4c8ed6be0d1a35833d4af1a6a6d","schema_version":"1.0","event_id":"sha256:0e38e25a37ef857f4393c22a14d6e9230b8cf4c8ed6be0d1a35833d4af1a6a6d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:DZQIWIAIMRNUJFNJVKSRCBSOME","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"An iterative local updating ensemble smoother for estimation and uncertainty assessment of hydrologic model parameters with multimodal distributions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ME"],"primary_cat":"math.OC","authors_text":"Guang Lin, Jiangjiang Zhang, Laosheng Wu, Lingzao Zeng, Weixuan Li","submitted_at":"2016-11-15T04:20:19Z","abstract_excerpt":"Ensemble smoother (ES) has been widely used in inverse modeling of hydrologic systems. However, for problems where the distribution of model parameters is multimodal, using ES directly would be problematic. One popular solution is to use a clustering algorithm to identify each mode and update the clusters with ES separately. However, this strategy may not be very efficient when the dimension of parameter space is high or the number of modes is large. Alternatively, we propose in this paper a very simple and efficient algorithm, i.e., the iterative local updating ensemble smoother (ILUES), to e"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.04702","kind":"arxiv","version":6},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:22:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aS+cz6c+P8vns/iRqZsjvWfmvlxILERQ31GirJ6dZhZgyrLpVkSUYQS4DSvedfiNBz0I/fDSz2YLwJ8kTWSpDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T21:59:07.459479Z"},"content_sha256":"0d135b447e8d18a8d1ee3eb45f5f0e1b67ed85372c72fff18ec8eff730801642","schema_version":"1.0","event_id":"sha256:0d135b447e8d18a8d1ee3eb45f5f0e1b67ed85372c72fff18ec8eff730801642"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/DZQIWIAIMRNUJFNJVKSRCBSOME/bundle.json","state_url":"https://pith.science/pith/DZQIWIAIMRNUJFNJVKSRCBSOME/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/DZQIWIAIMRNUJFNJVKSRCBSOME/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-28T21:59:07Z","links":{"resolver":"https://pith.science/pith/DZQIWIAIMRNUJFNJVKSRCBSOME","bundle":"https://pith.science/pith/DZQIWIAIMRNUJFNJVKSRCBSOME/bundle.json","state":"https://pith.science/pith/DZQIWIAIMRNUJFNJVKSRCBSOME/state.json","well_known_bundle":"https://pith.science/.well-known/pith/DZQIWIAIMRNUJFNJVKSRCBSOME/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:DZQIWIAIMRNUJFNJVKSRCBSOME","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"af5a9437e42508ded0005e088104b5c2d67c70280aff2073e810ae224dbf3e9f","cross_cats_sorted":["stat.ME"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2016-11-15T04:20:19Z","title_canon_sha256":"dcb85257ae1aec9c3d0d1d5f009a71c06dcedb2052840023539ef5a66ebab48e"},"schema_version":"1.0","source":{"id":"1611.04702","kind":"arxiv","version":6}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1611.04702","created_at":"2026-05-18T00:22:40Z"},{"alias_kind":"arxiv_version","alias_value":"1611.04702v6","created_at":"2026-05-18T00:22:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.04702","created_at":"2026-05-18T00:22:40Z"},{"alias_kind":"pith_short_12","alias_value":"DZQIWIAIMRNU","created_at":"2026-05-18T12:30:12Z"},{"alias_kind":"pith_short_16","alias_value":"DZQIWIAIMRNUJFNJ","created_at":"2026-05-18T12:30:12Z"},{"alias_kind":"pith_short_8","alias_value":"DZQIWIAI","created_at":"2026-05-18T12:30:12Z"}],"graph_snapshots":[{"event_id":"sha256:0d135b447e8d18a8d1ee3eb45f5f0e1b67ed85372c72fff18ec8eff730801642","target":"graph","created_at":"2026-05-18T00:22:40Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Ensemble smoother (ES) has been widely used in inverse modeling of hydrologic systems. However, for problems where the distribution of model parameters is multimodal, using ES directly would be problematic. One popular solution is to use a clustering algorithm to identify each mode and update the clusters with ES separately. However, this strategy may not be very efficient when the dimension of parameter space is high or the number of modes is large. Alternatively, we propose in this paper a very simple and efficient algorithm, i.e., the iterative local updating ensemble smoother (ILUES), to e","authors_text":"Guang Lin, Jiangjiang Zhang, Laosheng Wu, Lingzao Zeng, Weixuan Li","cross_cats":["stat.ME"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2016-11-15T04:20:19Z","title":"An iterative local updating ensemble smoother for estimation and uncertainty assessment of hydrologic model parameters with multimodal distributions"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.04702","kind":"arxiv","version":6},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:0e38e25a37ef857f4393c22a14d6e9230b8cf4c8ed6be0d1a35833d4af1a6a6d","target":"record","created_at":"2026-05-18T00:22:40Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"af5a9437e42508ded0005e088104b5c2d67c70280aff2073e810ae224dbf3e9f","cross_cats_sorted":["stat.ME"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2016-11-15T04:20:19Z","title_canon_sha256":"dcb85257ae1aec9c3d0d1d5f009a71c06dcedb2052840023539ef5a66ebab48e"},"schema_version":"1.0","source":{"id":"1611.04702","kind":"arxiv","version":6}},"canonical_sha256":"1e608b2008645b4495a9aaa511064e61111147c952cba41ab82e0b494bf8020b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1e608b2008645b4495a9aaa511064e61111147c952cba41ab82e0b494bf8020b","first_computed_at":"2026-05-18T00:22:40.212382Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:22:40.212382Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"q94NajbZv89gqD8pLTFKNPCx+e3lUQQZAT+d/C4yLYeMwe+ZD9g1gnHk2YINsQirytgwolvtP2g+a503BGlXDQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:22:40.212871Z","signed_message":"canonical_sha256_bytes"},"source_id":"1611.04702","source_kind":"arxiv","source_version":6}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0e38e25a37ef857f4393c22a14d6e9230b8cf4c8ed6be0d1a35833d4af1a6a6d","sha256:0d135b447e8d18a8d1ee3eb45f5f0e1b67ed85372c72fff18ec8eff730801642"],"state_sha256":"0b56e8a927bc760a1f211d2a0e59a313050997d500ef75941d2722bc3657f942"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"p/zx/hxOTjhfOUCGiA/9XPHFhU2bk92KVwszj3rhFcceIxCDuZSxK071V/Vuvs6/sB5gAKTNmflz5aE/AdjDAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T21:59:07.462676Z","bundle_sha256":"766e80d1fb36250a0994e62acf3cd7db12bf982603bee4a8a63940c81f391eca"}}