{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:IBB5ZZQR24WDUTIU6YFZQUKDIN","short_pith_number":"pith:IBB5ZZQR","schema_version":"1.0","canonical_sha256":"4043dce611d72c3a4d14f60b9851434360584c0a5cece6e0afa692bbacc18cc7","source":{"kind":"arxiv","id":"1905.09130","version":1},"attestation_state":"computed","paper":{"title":"AI-CARGO: A Data-Driven Air-Cargo Revenue Management System","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AI","authors_text":"Ji Lucas, Jorge-Arnulfo Quiane-Ruiz, Sanjay Chawla, Stefano Giovanni Rizzo, Zoi Kaoudi","submitted_at":"2019-05-22T13:34:45Z","abstract_excerpt":"We propose AI-CARGO, a revenue management system for air-cargo that combines machine learning prediction with decision-making using mathematical optimization methods. AI-CARGO addresses a problem that is unique to the air-cargo business, namely the wide discrepancy between the quantity (weight or volume) that a shipper will book and the actual received amount at departure time by the airline. The discrepancy results in sub-optimal and inefficient behavior by both the shipper and the airline resulting in the overall loss of potential revenue for the airline. AI-CARGO also includes a data cleani"},"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":"1905.09130","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2019-05-22T13:34:45Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"b855e134dff611b5038ea5ec80f0643fa0eca7fb1f3cab06c04dc495abb6e5b5","abstract_canon_sha256":"096ef15cb2cb2981395d13e54a51c68e029d85bcf91f6f3353ac774cebcdbe0a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:45:21.903091Z","signature_b64":"NxE8ykkRFidfX21Yc+kcecyiWE4pxNiP2qllPYF/KjwqQtYLaXZRu36rg42D/bR4fMxcPeWAsmrdpXXWqR5ZBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4043dce611d72c3a4d14f60b9851434360584c0a5cece6e0afa692bbacc18cc7","last_reissued_at":"2026-05-17T23:45:21.902623Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:45:21.902623Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"AI-CARGO: A Data-Driven Air-Cargo Revenue Management System","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AI","authors_text":"Ji Lucas, Jorge-Arnulfo Quiane-Ruiz, Sanjay Chawla, Stefano Giovanni Rizzo, Zoi Kaoudi","submitted_at":"2019-05-22T13:34:45Z","abstract_excerpt":"We propose AI-CARGO, a revenue management system for air-cargo that combines machine learning prediction with decision-making using mathematical optimization methods. AI-CARGO addresses a problem that is unique to the air-cargo business, namely the wide discrepancy between the quantity (weight or volume) that a shipper will book and the actual received amount at departure time by the airline. The discrepancy results in sub-optimal and inefficient behavior by both the shipper and the airline resulting in the overall loss of potential revenue for the airline. AI-CARGO also includes a data cleani"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.09130","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":"1905.09130","created_at":"2026-05-17T23:45:21.902703+00:00"},{"alias_kind":"arxiv_version","alias_value":"1905.09130v1","created_at":"2026-05-17T23:45:21.902703+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.09130","created_at":"2026-05-17T23:45:21.902703+00:00"},{"alias_kind":"pith_short_12","alias_value":"IBB5ZZQR24WD","created_at":"2026-05-18T12:33:18.533446+00:00"},{"alias_kind":"pith_short_16","alias_value":"IBB5ZZQR24WDUTIU","created_at":"2026-05-18T12:33:18.533446+00:00"},{"alias_kind":"pith_short_8","alias_value":"IBB5ZZQR","created_at":"2026-05-18T12:33:18.533446+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/IBB5ZZQR24WDUTIU6YFZQUKDIN","json":"https://pith.science/pith/IBB5ZZQR24WDUTIU6YFZQUKDIN.json","graph_json":"https://pith.science/api/pith-number/IBB5ZZQR24WDUTIU6YFZQUKDIN/graph.json","events_json":"https://pith.science/api/pith-number/IBB5ZZQR24WDUTIU6YFZQUKDIN/events.json","paper":"https://pith.science/paper/IBB5ZZQR"},"agent_actions":{"view_html":"https://pith.science/pith/IBB5ZZQR24WDUTIU6YFZQUKDIN","download_json":"https://pith.science/pith/IBB5ZZQR24WDUTIU6YFZQUKDIN.json","view_paper":"https://pith.science/paper/IBB5ZZQR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1905.09130&json=true","fetch_graph":"https://pith.science/api/pith-number/IBB5ZZQR24WDUTIU6YFZQUKDIN/graph.json","fetch_events":"https://pith.science/api/pith-number/IBB5ZZQR24WDUTIU6YFZQUKDIN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IBB5ZZQR24WDUTIU6YFZQUKDIN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IBB5ZZQR24WDUTIU6YFZQUKDIN/action/storage_attestation","attest_author":"https://pith.science/pith/IBB5ZZQR24WDUTIU6YFZQUKDIN/action/author_attestation","sign_citation":"https://pith.science/pith/IBB5ZZQR24WDUTIU6YFZQUKDIN/action/citation_signature","submit_replication":"https://pith.science/pith/IBB5ZZQR24WDUTIU6YFZQUKDIN/action/replication_record"}},"created_at":"2026-05-17T23:45:21.902703+00:00","updated_at":"2026-05-17T23:45:21.902703+00:00"}