{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:ZKMLQ2DZAF32WTEBXL55GW6INW","short_pith_number":"pith:ZKMLQ2DZ","schema_version":"1.0","canonical_sha256":"ca98b868790177ab4c81bafbd35bc86d81851a3fc1081696cfd2da9956c766cd","source":{"kind":"arxiv","id":"2605.18408","version":1},"attestation_state":"computed","paper":{"title":"Historical Knowledge Graphs for Global Maritime Estimated Time of Arrival","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Neofytos Dimitriou","submitted_at":"2026-05-18T13:47:57Z","abstract_excerpt":"Accurate vessel estimated-time-of-arrival forecasts are critical for port operations and decarbonization, yet global-scale travel-time prediction remains difficult without costly contextual data. Herein, I present a methodology for constructing a historical maritime knowledge graph using only Automatic Identification System (AIS) data. First, segmented trajectories are extracted from noisy AIS data using a Gaussian-mixture-model-based preprocessing pipeline. The graph is then constructed by iteratively processing the trajectories and storing speed distributions stratified by vessel type, time "},"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":"2605.18408","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-18T13:47:57Z","cross_cats_sorted":[],"title_canon_sha256":"f7e71d78bbff75bb5647de0f9b233945e1d2345285685112418e150cfacad134","abstract_canon_sha256":"e5ed8f943f36fb6bcaae8747c05370e50000589585e206ad4f9301d685f1f3f7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:05:59.279729Z","signature_b64":"YwqXo1IiSGL5kUGiqP0ZSQxjvOdJFfGxr59UygF7ZTz6vnM/VG0DWaQgdFxkntL8HImPvCSzLYyvnUgyB1xIBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ca98b868790177ab4c81bafbd35bc86d81851a3fc1081696cfd2da9956c766cd","last_reissued_at":"2026-05-20T00:05:59.279005Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:05:59.279005Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Historical Knowledge Graphs for Global Maritime Estimated Time of Arrival","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Neofytos Dimitriou","submitted_at":"2026-05-18T13:47:57Z","abstract_excerpt":"Accurate vessel estimated-time-of-arrival forecasts are critical for port operations and decarbonization, yet global-scale travel-time prediction remains difficult without costly contextual data. Herein, I present a methodology for constructing a historical maritime knowledge graph using only Automatic Identification System (AIS) data. First, segmented trajectories are extracted from noisy AIS data using a Gaussian-mixture-model-based preprocessing pipeline. The graph is then constructed by iteratively processing the trajectories and storing speed distributions stratified by vessel type, time "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.18408","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/2605.18408/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"cited_work_retraction","ran_at":"2026-05-19T23:51:58.479683Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"citation_quote_validity","ran_at":"2026-05-19T23:49:59.959715Z","status":"skipped","version":"0.1.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T23:33:29.262750Z","status":"skipped","version":"1.0.0","findings_count":0},{"name":"external_links","ran_at":"2026-05-19T23:31:38.967252Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T23:21:58.708243Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"f4aa7a5b4073bc6b80594f531260e594cb22971755fa7f45027bc921b2835eed"},"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":"2605.18408","created_at":"2026-05-20T00:05:59.279113+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.18408v1","created_at":"2026-05-20T00:05:59.279113+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.18408","created_at":"2026-05-20T00:05:59.279113+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZKMLQ2DZAF32","created_at":"2026-05-20T00:05:59.279113+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZKMLQ2DZAF32WTEB","created_at":"2026-05-20T00:05:59.279113+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZKMLQ2DZ","created_at":"2026-05-20T00:05:59.279113+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/ZKMLQ2DZAF32WTEBXL55GW6INW","json":"https://pith.science/pith/ZKMLQ2DZAF32WTEBXL55GW6INW.json","graph_json":"https://pith.science/api/pith-number/ZKMLQ2DZAF32WTEBXL55GW6INW/graph.json","events_json":"https://pith.science/api/pith-number/ZKMLQ2DZAF32WTEBXL55GW6INW/events.json","paper":"https://pith.science/paper/ZKMLQ2DZ"},"agent_actions":{"view_html":"https://pith.science/pith/ZKMLQ2DZAF32WTEBXL55GW6INW","download_json":"https://pith.science/pith/ZKMLQ2DZAF32WTEBXL55GW6INW.json","view_paper":"https://pith.science/paper/ZKMLQ2DZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.18408&json=true","fetch_graph":"https://pith.science/api/pith-number/ZKMLQ2DZAF32WTEBXL55GW6INW/graph.json","fetch_events":"https://pith.science/api/pith-number/ZKMLQ2DZAF32WTEBXL55GW6INW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZKMLQ2DZAF32WTEBXL55GW6INW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZKMLQ2DZAF32WTEBXL55GW6INW/action/storage_attestation","attest_author":"https://pith.science/pith/ZKMLQ2DZAF32WTEBXL55GW6INW/action/author_attestation","sign_citation":"https://pith.science/pith/ZKMLQ2DZAF32WTEBXL55GW6INW/action/citation_signature","submit_replication":"https://pith.science/pith/ZKMLQ2DZAF32WTEBXL55GW6INW/action/replication_record"}},"created_at":"2026-05-20T00:05:59.279113+00:00","updated_at":"2026-05-20T00:05:59.279113+00:00"}