{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:QF6A6STGDCNUPAZ7BXR5X3OF7O","short_pith_number":"pith:QF6A6STG","canonical_record":{"source":{"id":"2507.20008","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-07-26T16:55:16Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"5d23a9a9f60a50b3db57fc7a25937e754116294650762b1919a75a5f38674289","abstract_canon_sha256":"d367399b87ec8f247eb5e13459166c1250b5fd0bec9ac06cce03b9e2c6d1f87e"},"schema_version":"1.0"},"canonical_sha256":"817c0f4a66189b47833f0de3dbedc5fbaa74dbb1222c332615b1995ae515546e","source":{"kind":"arxiv","id":"2507.20008","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.20008","created_at":"2026-07-05T11:43:46Z"},{"alias_kind":"arxiv_version","alias_value":"2507.20008v1","created_at":"2026-07-05T11:43:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.20008","created_at":"2026-07-05T11:43:46Z"},{"alias_kind":"pith_short_12","alias_value":"QF6A6STGDCNU","created_at":"2026-07-05T11:43:46Z"},{"alias_kind":"pith_short_16","alias_value":"QF6A6STGDCNUPAZ7","created_at":"2026-07-05T11:43:46Z"},{"alias_kind":"pith_short_8","alias_value":"QF6A6STG","created_at":"2026-07-05T11:43:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:QF6A6STGDCNUPAZ7BXR5X3OF7O","target":"record","payload":{"canonical_record":{"source":{"id":"2507.20008","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-07-26T16:55:16Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"5d23a9a9f60a50b3db57fc7a25937e754116294650762b1919a75a5f38674289","abstract_canon_sha256":"d367399b87ec8f247eb5e13459166c1250b5fd0bec9ac06cce03b9e2c6d1f87e"},"schema_version":"1.0"},"canonical_sha256":"817c0f4a66189b47833f0de3dbedc5fbaa74dbb1222c332615b1995ae515546e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:43:46.431041Z","signature_b64":"OpxR4K6gWXfs7WIWM92OW8dnuD0YSGW44Q6BJo9Ej9Vxo6BKPOS1mXBhiK0S6xREXAscMWZFdhkNCN2FyguyBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"817c0f4a66189b47833f0de3dbedc5fbaa74dbb1222c332615b1995ae515546e","last_reissued_at":"2026-07-05T11:43:46.430530Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:43:46.430530Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2507.20008","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":"2026-07-05T11:43:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SHvg6yCPo/85CV884IrX70/21C3GSZJSaH0KC7kTWVExRBLA0s51RHH+ywvHl6cujjv2M/sN0P8AlcDKXjIcBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T22:50:21.208305Z"},"content_sha256":"5a7c3d1927b3ba30023567e77dbf40694aea1ae6136d699d05b73ee3e4dfcd7c","schema_version":"1.0","event_id":"sha256:5a7c3d1927b3ba30023567e77dbf40694aea1ae6136d699d05b73ee3e4dfcd7c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:QF6A6STGDCNUPAZ7BXR5X3OF7O","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Robust Taxi Fare Prediction Under Noisy Conditions: A Comparative Study of GAT, TimesNet, and XGBoost","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Padmavathi Moorthy (SUNY Buffalo)","submitted_at":"2025-07-26T16:55:16Z","abstract_excerpt":"Precise fare prediction is crucial in ride-hailing platforms and urban mobility systems. This study examines three machine learning models-Graph Attention Networks (GAT), XGBoost, and TimesNet to evaluate their predictive capabilities for taxi fares using a real-world dataset comprising over 55 million records. Both raw (noisy) and denoised versions of the dataset are analyzed to assess the impact of data quality on model performance. The study evaluated the models along multiple axes, including predictive accuracy, calibration, uncertainty estimation, out-of-distribution (OOD) robustness, and"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.20008","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/2507.20008/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":"2026-07-05T11:43:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"S8ApMjJlHavy6AU0eVZmBZ63owDWgRmLIQRx862+lSAYmMTKXn54EKJQLcb+UeSurPd2HzOJgVFqNmBokNcaCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T22:50:21.208973Z"},"content_sha256":"af5a04b751f5ba8a357816a3a8edad9bcba14dd6b6da1e045799103b1dbe68bc","schema_version":"1.0","event_id":"sha256:af5a04b751f5ba8a357816a3a8edad9bcba14dd6b6da1e045799103b1dbe68bc"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QF6A6STGDCNUPAZ7BXR5X3OF7O/bundle.json","state_url":"https://pith.science/pith/QF6A6STGDCNUPAZ7BXR5X3OF7O/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QF6A6STGDCNUPAZ7BXR5X3OF7O/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":"2026-08-08T22:50:21Z","links":{"resolver":"https://pith.science/pith/QF6A6STGDCNUPAZ7BXR5X3OF7O","bundle":"https://pith.science/pith/QF6A6STGDCNUPAZ7BXR5X3OF7O/bundle.json","state":"https://pith.science/pith/QF6A6STGDCNUPAZ7BXR5X3OF7O/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QF6A6STGDCNUPAZ7BXR5X3OF7O/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:QF6A6STGDCNUPAZ7BXR5X3OF7O","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":"d367399b87ec8f247eb5e13459166c1250b5fd0bec9ac06cce03b9e2c6d1f87e","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-07-26T16:55:16Z","title_canon_sha256":"5d23a9a9f60a50b3db57fc7a25937e754116294650762b1919a75a5f38674289"},"schema_version":"1.0","source":{"id":"2507.20008","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.20008","created_at":"2026-07-05T11:43:46Z"},{"alias_kind":"arxiv_version","alias_value":"2507.20008v1","created_at":"2026-07-05T11:43:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.20008","created_at":"2026-07-05T11:43:46Z"},{"alias_kind":"pith_short_12","alias_value":"QF6A6STGDCNU","created_at":"2026-07-05T11:43:46Z"},{"alias_kind":"pith_short_16","alias_value":"QF6A6STGDCNUPAZ7","created_at":"2026-07-05T11:43:46Z"},{"alias_kind":"pith_short_8","alias_value":"QF6A6STG","created_at":"2026-07-05T11:43:46Z"}],"graph_snapshots":[{"event_id":"sha256:af5a04b751f5ba8a357816a3a8edad9bcba14dd6b6da1e045799103b1dbe68bc","target":"graph","created_at":"2026-07-05T11:43:46Z","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/2507.20008/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Precise fare prediction is crucial in ride-hailing platforms and urban mobility systems. This study examines three machine learning models-Graph Attention Networks (GAT), XGBoost, and TimesNet to evaluate their predictive capabilities for taxi fares using a real-world dataset comprising over 55 million records. Both raw (noisy) and denoised versions of the dataset are analyzed to assess the impact of data quality on model performance. The study evaluated the models along multiple axes, including predictive accuracy, calibration, uncertainty estimation, out-of-distribution (OOD) robustness, and","authors_text":"Padmavathi Moorthy (SUNY Buffalo)","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-07-26T16:55:16Z","title":"Robust Taxi Fare Prediction Under Noisy Conditions: A Comparative Study of GAT, TimesNet, and XGBoost"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.20008","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":"sha256:5a7c3d1927b3ba30023567e77dbf40694aea1ae6136d699d05b73ee3e4dfcd7c","target":"record","created_at":"2026-07-05T11:43:46Z","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":"d367399b87ec8f247eb5e13459166c1250b5fd0bec9ac06cce03b9e2c6d1f87e","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-07-26T16:55:16Z","title_canon_sha256":"5d23a9a9f60a50b3db57fc7a25937e754116294650762b1919a75a5f38674289"},"schema_version":"1.0","source":{"id":"2507.20008","kind":"arxiv","version":1}},"canonical_sha256":"817c0f4a66189b47833f0de3dbedc5fbaa74dbb1222c332615b1995ae515546e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"817c0f4a66189b47833f0de3dbedc5fbaa74dbb1222c332615b1995ae515546e","first_computed_at":"2026-07-05T11:43:46.430530Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:43:46.430530Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"OpxR4K6gWXfs7WIWM92OW8dnuD0YSGW44Q6BJo9Ej9Vxo6BKPOS1mXBhiK0S6xREXAscMWZFdhkNCN2FyguyBg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:43:46.431041Z","signed_message":"canonical_sha256_bytes"},"source_id":"2507.20008","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5a7c3d1927b3ba30023567e77dbf40694aea1ae6136d699d05b73ee3e4dfcd7c","sha256:af5a04b751f5ba8a357816a3a8edad9bcba14dd6b6da1e045799103b1dbe68bc"],"state_sha256":"cc7bc24ddba2d0bb3079ba1a3c6f2dbc2f1316dbff3849ddcd72a1d44149801d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jUjDbN57aJ25QTm6FxjH5r05UPhQJZ5ZLclPt5aRxbAa6hcsQeweMidjjL/ZGMfpVt9hV6kK7mbP02AdUu0WAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T22:50:21.213861Z","bundle_sha256":"93ab117a0d4f339b136403d3f4f5f3b0380a743238619882ec0cea0bbad5b156"}}