{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:63VCBJOK5WBRWW2UJOVRUV6XU2","short_pith_number":"pith:63VCBJOK","canonical_record":{"source":{"id":"2606.04143","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-06-02T19:01:06Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"ad1a6cdba322015e45aff4baa2f925358a4396e692ede48cc6ab0b6e3fd74e3c","abstract_canon_sha256":"f762d904f7e358cea8ca61150883cf46f50b1e89746479657fb9363ccee41444"},"schema_version":"1.0"},"canonical_sha256":"f6ea20a5caed831b5b544bab1a57d7a68746150b7a6d46701d125591cbf9b222","source":{"kind":"arxiv","id":"2606.04143","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.04143","created_at":"2026-06-04T00:06:51Z"},{"alias_kind":"arxiv_version","alias_value":"2606.04143v1","created_at":"2026-06-04T00:06:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.04143","created_at":"2026-06-04T00:06:51Z"},{"alias_kind":"pith_short_12","alias_value":"63VCBJOK5WBR","created_at":"2026-06-04T00:06:51Z"},{"alias_kind":"pith_short_16","alias_value":"63VCBJOK5WBRWW2U","created_at":"2026-06-04T00:06:51Z"},{"alias_kind":"pith_short_8","alias_value":"63VCBJOK","created_at":"2026-06-04T00:06:51Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:63VCBJOK5WBRWW2UJOVRUV6XU2","target":"record","payload":{"canonical_record":{"source":{"id":"2606.04143","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-06-02T19:01:06Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"ad1a6cdba322015e45aff4baa2f925358a4396e692ede48cc6ab0b6e3fd74e3c","abstract_canon_sha256":"f762d904f7e358cea8ca61150883cf46f50b1e89746479657fb9363ccee41444"},"schema_version":"1.0"},"canonical_sha256":"f6ea20a5caed831b5b544bab1a57d7a68746150b7a6d46701d125591cbf9b222","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-04T00:06:51.818148Z","signature_b64":"sMTg+KLFDo473ZLbFo93rPTGV4gzTkv85PcXn0L27+O8DWdoR4MVm54edeNDZ7YhcRKErlcW6Bfh0Z0kCL7iAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f6ea20a5caed831b5b544bab1a57d7a68746150b7a6d46701d125591cbf9b222","last_reissued_at":"2026-06-04T00:06:51.817777Z","signature_status":"signed_v1","first_computed_at":"2026-06-04T00:06:51.817777Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2606.04143","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-04T00:06:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XQer0Oq1yQnRI6TRiYq44BJ+/PuYUTd5lkt5cx1hLdwwE1JWrzyx3DREtFFymxUYb0o+VTpQ9Tw7XifgoUUeDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-27T15:30:20.280371Z"},"content_sha256":"1282f7d08ca4d12542db7493fdc7fd0560bba7bf86973078f3b24c78a9f68d8b","schema_version":"1.0","event_id":"sha256:1282f7d08ca4d12542db7493fdc7fd0560bba7bf86973078f3b24c78a9f68d8b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:63VCBJOK5WBRWW2UJOVRUV6XU2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Physics-Informed Machine Learning for Short-Term Flood Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Jagrati Talreja, Leila Hashemi-Beni, Tewodros Syum Gebre","submitted_at":"2026-06-02T19:01:06Z","abstract_excerpt":"Accurate flood forecasting is essential for mitigating disaster risks and protecting communities. However, purely data-driven machine learning models often struggle in data-scarce environments and may violate fundamental hydrological principles. Standard Long Short-Term Memory (LSTM) networks can generate physically inconsistent predictions, particularly when extrapolating to extreme weather conditions. To address these limitations, we propose a Physics-Informed Machine Learning (PIML) framework that incorporates hydrological knowledge directly into the loss function of an LSTM model. Specific"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.04143","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/2606.04143/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-04T00:06:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"j/7gou8viwp7niYEaK2enw90uar/RaJLkor+gNc98UPN078RGNPROGZaHHNLxp2SzMBbYCc7iPpiEO9KTeLhCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-27T15:30:20.280746Z"},"content_sha256":"d076b88f9d7d883fb132a74a446938a2aab5baa8890cd01f995dfe98acae47ca","schema_version":"1.0","event_id":"sha256:d076b88f9d7d883fb132a74a446938a2aab5baa8890cd01f995dfe98acae47ca"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/63VCBJOK5WBRWW2UJOVRUV6XU2/bundle.json","state_url":"https://pith.science/pith/63VCBJOK5WBRWW2UJOVRUV6XU2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/63VCBJOK5WBRWW2UJOVRUV6XU2/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":"2026-06-27T15:30:20Z","links":{"resolver":"https://pith.science/pith/63VCBJOK5WBRWW2UJOVRUV6XU2","bundle":"https://pith.science/pith/63VCBJOK5WBRWW2UJOVRUV6XU2/bundle.json","state":"https://pith.science/pith/63VCBJOK5WBRWW2UJOVRUV6XU2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/63VCBJOK5WBRWW2UJOVRUV6XU2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:63VCBJOK5WBRWW2UJOVRUV6XU2","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":"f762d904f7e358cea8ca61150883cf46f50b1e89746479657fb9363ccee41444","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-06-02T19:01:06Z","title_canon_sha256":"ad1a6cdba322015e45aff4baa2f925358a4396e692ede48cc6ab0b6e3fd74e3c"},"schema_version":"1.0","source":{"id":"2606.04143","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.04143","created_at":"2026-06-04T00:06:51Z"},{"alias_kind":"arxiv_version","alias_value":"2606.04143v1","created_at":"2026-06-04T00:06:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.04143","created_at":"2026-06-04T00:06:51Z"},{"alias_kind":"pith_short_12","alias_value":"63VCBJOK5WBR","created_at":"2026-06-04T00:06:51Z"},{"alias_kind":"pith_short_16","alias_value":"63VCBJOK5WBRWW2U","created_at":"2026-06-04T00:06:51Z"},{"alias_kind":"pith_short_8","alias_value":"63VCBJOK","created_at":"2026-06-04T00:06:51Z"}],"graph_snapshots":[{"event_id":"sha256:d076b88f9d7d883fb132a74a446938a2aab5baa8890cd01f995dfe98acae47ca","target":"graph","created_at":"2026-06-04T00:06:51Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2606.04143/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Accurate flood forecasting is essential for mitigating disaster risks and protecting communities. However, purely data-driven machine learning models often struggle in data-scarce environments and may violate fundamental hydrological principles. Standard Long Short-Term Memory (LSTM) networks can generate physically inconsistent predictions, particularly when extrapolating to extreme weather conditions. To address these limitations, we propose a Physics-Informed Machine Learning (PIML) framework that incorporates hydrological knowledge directly into the loss function of an LSTM model. Specific","authors_text":"Jagrati Talreja, Leila Hashemi-Beni, Tewodros Syum Gebre","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-06-02T19:01:06Z","title":"Physics-Informed Machine Learning for Short-Term Flood Prediction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.04143","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":"sha256:1282f7d08ca4d12542db7493fdc7fd0560bba7bf86973078f3b24c78a9f68d8b","target":"record","created_at":"2026-06-04T00:06:51Z","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":"f762d904f7e358cea8ca61150883cf46f50b1e89746479657fb9363ccee41444","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-06-02T19:01:06Z","title_canon_sha256":"ad1a6cdba322015e45aff4baa2f925358a4396e692ede48cc6ab0b6e3fd74e3c"},"schema_version":"1.0","source":{"id":"2606.04143","kind":"arxiv","version":1}},"canonical_sha256":"f6ea20a5caed831b5b544bab1a57d7a68746150b7a6d46701d125591cbf9b222","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f6ea20a5caed831b5b544bab1a57d7a68746150b7a6d46701d125591cbf9b222","first_computed_at":"2026-06-04T00:06:51.817777Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-04T00:06:51.817777Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"sMTg+KLFDo473ZLbFo93rPTGV4gzTkv85PcXn0L27+O8DWdoR4MVm54edeNDZ7YhcRKErlcW6Bfh0Z0kCL7iAQ==","signature_status":"signed_v1","signed_at":"2026-06-04T00:06:51.818148Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.04143","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1282f7d08ca4d12542db7493fdc7fd0560bba7bf86973078f3b24c78a9f68d8b","sha256:d076b88f9d7d883fb132a74a446938a2aab5baa8890cd01f995dfe98acae47ca"],"state_sha256":"951e87f7c0749ddbb19a9d82f72ff9221c91ea25c3ac15d1e19ebe7a01ccc4a2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"X+7aWljBIyCK8u+q1qs6F0lu6P/f2qv7ELSwWPeEb5QieTf/nJwtP9D6XdZvDRgdiWTUKmHoz5pnUWHygqVnCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-27T15:30:20.282779Z","bundle_sha256":"ae3ba107066f3240f7e6e94b0e97832a6818d4301f6fde0891bb398089fa2b6e"}}