{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:SKM2QFIZHS6OTW2Q7ZBVXB36D4","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":"d3ea3e48631b67c290e4be5f4ce053ae1ff02c711f01a5b80bb1afb51b79db7a","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-01-28T10:23:29Z","title_canon_sha256":"661d5bc1f9f4bcea0be5c6b11940df8c607bad32ec3adb56c9a7942bac14cdae"},"schema_version":"1.0","source":{"id":"1901.09583","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1901.09583","created_at":"2026-05-17T23:55:24Z"},{"alias_kind":"arxiv_version","alias_value":"1901.09583v1","created_at":"2026-05-17T23:55:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.09583","created_at":"2026-05-17T23:55:24Z"},{"alias_kind":"pith_short_12","alias_value":"SKM2QFIZHS6O","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_16","alias_value":"SKM2QFIZHS6OTW2Q","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_8","alias_value":"SKM2QFIZ","created_at":"2026-05-18T12:33:27Z"}],"graph_snapshots":[{"event_id":"sha256:f298b45112b4636bcbc488006b500ca5c62e6c6849b619b11ca9fdcca7001059","target":"graph","created_at":"2026-05-17T23:55:24Z","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":"Effective riverine flood forecasting at scale is hindered by a multitude of factors, most notably the need to rely on human calibration in current methodology, the limited amount of data for a specific location, and the computational difficulty of building continent/global level models that are sufficiently accurate. Machine learning (ML) is primed to be useful in this scenario: learned models often surpass human experts in complex high-dimensional scenarios, and the framework of transfer or multitask learning is an appealing solution for leveraging local signals to achieve improved global per","authors_text":"Ajai Tirumali, Ami Wiesel, Avinatan Hassidim, Gal Elidan, Guy Shalev, Mor Schlesinger, Oleg Zlydenko, Pete Giencke, Ran El-Yaniv, Sella Nevo, Vova Anisimov, Yossi Matias, Yotam Gigi, Zach Moshe","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-01-28T10:23:29Z","title":"ML for Flood Forecasting at Scale"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.09583","kind":"arxiv","version":1},"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":"sha256:32d51847dc7cd1b165f25d4f722f61170e45b664c6551e4ce92972ead3338095","target":"record","created_at":"2026-05-17T23:55:24Z","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":"d3ea3e48631b67c290e4be5f4ce053ae1ff02c711f01a5b80bb1afb51b79db7a","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-01-28T10:23:29Z","title_canon_sha256":"661d5bc1f9f4bcea0be5c6b11940df8c607bad32ec3adb56c9a7942bac14cdae"},"schema_version":"1.0","source":{"id":"1901.09583","kind":"arxiv","version":1}},"canonical_sha256":"9299a815193cbce9db50fe435b877e1f25857c70bca41f390dcbbc71466c7842","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9299a815193cbce9db50fe435b877e1f25857c70bca41f390dcbbc71466c7842","first_computed_at":"2026-05-17T23:55:24.391852Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:55:24.391852Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ERMnBV0yW28U7KljLqQIA4vKF0bxWLOnpwcfZeBD55DoNhS+g+25uAHV1BAPBX6wGbX5cRCkYpYQmG9pZ3E8Cw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:55:24.392208Z","signed_message":"canonical_sha256_bytes"},"source_id":"1901.09583","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:32d51847dc7cd1b165f25d4f722f61170e45b664c6551e4ce92972ead3338095","sha256:f298b45112b4636bcbc488006b500ca5c62e6c6849b619b11ca9fdcca7001059"],"state_sha256":"4caee7ec7fa5083f6711d30b5cab49a42b2a361d09a799c1fbb322b205e203e1"}