{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:EK6BKEFIR5F4HZFR3FUKACGWVD","short_pith_number":"pith:EK6BKEFI","schema_version":"1.0","canonical_sha256":"22bc1510a88f4bc3e4b1d968a008d6a8dd2687aa17e4ca5bfec1010b9820300f","source":{"kind":"arxiv","id":"1611.00356","version":1},"attestation_state":"computed","paper":{"title":"Using Artificial Intelligence to Identify State Secrets","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.LG"],"primary_cat":"cs.CY","authors_text":"Flavio Codeco Coelho, Matthew Connelly, Renato Rocha Souza, Rohan Shah","submitted_at":"2016-11-01T19:59:48Z","abstract_excerpt":"Whether officials can be trusted to protect national security information has become a matter of great public controversy, reigniting a long-standing debate about the scope and nature of official secrecy. The declassification of millions of electronic records has made it possible to analyze these issues with greater rigor and precision. Using machine-learning methods, we examined nearly a million State Department cables from the 1970s to identify features of records that are more likely to be classified, such as international negotiations, military operations, and high-level communications. Ev"},"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":"1611.00356","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CY","submitted_at":"2016-11-01T19:59:48Z","cross_cats_sorted":["cs.CL","cs.LG"],"title_canon_sha256":"f767afc90334a24ebd249d2658ba69edc6771adea48b9fe525de37e0e46e1d0c","abstract_canon_sha256":"8c4ec3f512f8e1e97bf9885799adb473d6b6b8af836fdf98fb6721d17e140ec5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:00:17.443605Z","signature_b64":"YMZn2zv4KWzksjhRZ/+He+/9TL6bTGBjOhr6kVrCiRwFRndxmknggG/K+ZZD1L4F5bqSzJGFAQ+nEUGRvssgAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"22bc1510a88f4bc3e4b1d968a008d6a8dd2687aa17e4ca5bfec1010b9820300f","last_reissued_at":"2026-05-18T01:00:17.442900Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:00:17.442900Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Using Artificial Intelligence to Identify State Secrets","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.LG"],"primary_cat":"cs.CY","authors_text":"Flavio Codeco Coelho, Matthew Connelly, Renato Rocha Souza, Rohan Shah","submitted_at":"2016-11-01T19:59:48Z","abstract_excerpt":"Whether officials can be trusted to protect national security information has become a matter of great public controversy, reigniting a long-standing debate about the scope and nature of official secrecy. The declassification of millions of electronic records has made it possible to analyze these issues with greater rigor and precision. Using machine-learning methods, we examined nearly a million State Department cables from the 1970s to identify features of records that are more likely to be classified, such as international negotiations, military operations, and high-level communications. Ev"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.00356","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":"1611.00356","created_at":"2026-05-18T01:00:17.442990+00:00"},{"alias_kind":"arxiv_version","alias_value":"1611.00356v1","created_at":"2026-05-18T01:00:17.442990+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.00356","created_at":"2026-05-18T01:00:17.442990+00:00"},{"alias_kind":"pith_short_12","alias_value":"EK6BKEFIR5F4","created_at":"2026-05-18T12:30:12.583610+00:00"},{"alias_kind":"pith_short_16","alias_value":"EK6BKEFIR5F4HZFR","created_at":"2026-05-18T12:30:12.583610+00:00"},{"alias_kind":"pith_short_8","alias_value":"EK6BKEFI","created_at":"2026-05-18T12:30:12.583610+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2511.11010","citing_title":"GovScape: A Public Multimodal Search System for 70 Million Pages of Government PDFs","ref_index":25,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EK6BKEFIR5F4HZFR3FUKACGWVD","json":"https://pith.science/pith/EK6BKEFIR5F4HZFR3FUKACGWVD.json","graph_json":"https://pith.science/api/pith-number/EK6BKEFIR5F4HZFR3FUKACGWVD/graph.json","events_json":"https://pith.science/api/pith-number/EK6BKEFIR5F4HZFR3FUKACGWVD/events.json","paper":"https://pith.science/paper/EK6BKEFI"},"agent_actions":{"view_html":"https://pith.science/pith/EK6BKEFIR5F4HZFR3FUKACGWVD","download_json":"https://pith.science/pith/EK6BKEFIR5F4HZFR3FUKACGWVD.json","view_paper":"https://pith.science/paper/EK6BKEFI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1611.00356&json=true","fetch_graph":"https://pith.science/api/pith-number/EK6BKEFIR5F4HZFR3FUKACGWVD/graph.json","fetch_events":"https://pith.science/api/pith-number/EK6BKEFIR5F4HZFR3FUKACGWVD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EK6BKEFIR5F4HZFR3FUKACGWVD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EK6BKEFIR5F4HZFR3FUKACGWVD/action/storage_attestation","attest_author":"https://pith.science/pith/EK6BKEFIR5F4HZFR3FUKACGWVD/action/author_attestation","sign_citation":"https://pith.science/pith/EK6BKEFIR5F4HZFR3FUKACGWVD/action/citation_signature","submit_replication":"https://pith.science/pith/EK6BKEFIR5F4HZFR3FUKACGWVD/action/replication_record"}},"created_at":"2026-05-18T01:00:17.442990+00:00","updated_at":"2026-05-18T01:00:17.442990+00:00"}