{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:AK5PPC4QGU46KRDEDXBCSSAF4B","short_pith_number":"pith:AK5PPC4Q","schema_version":"1.0","canonical_sha256":"02baf78b903539e544641dc2294805e074aaa9226dee12d0362b355c79687278","source":{"kind":"arxiv","id":"2503.22338","version":1},"attestation_state":"computed","paper":{"title":"SKDU at De-Factify 4.0: Natural Language Features for AI-Generated Text-Detection","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Miguel Arevalillo-Herr\\'aez, Pablo Arnau-Gonz\\'alez, Shrikant Malviya, Stamos Katsigiannis","submitted_at":"2025-03-28T11:25:05Z","abstract_excerpt":"The rapid advancement of large language models (LLMs) has introduced new challenges in distinguishing human-written text from AI-generated content. In this work, we explored a pipelined approach for AI-generated text detection that includes a feature extraction step (i.e. prompt-based rewriting features inspired by RAIDAR and content-based features derived from the NELA toolkit) followed by a classification module. Comprehensive experiments were conducted on the Defactify4.0 dataset, evaluating two tasks: binary classification to differentiate human-written and AI-generated text, and multi-cla"},"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":"2503.22338","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2025-03-28T11:25:05Z","cross_cats_sorted":[],"title_canon_sha256":"996cd20ecf98679991252fabe89a7ea7bad52c0011e64a1deb1386411ad26cf7","abstract_canon_sha256":"1275b8763a9921f708ea6c0cbeb4e0ed3ca22367f7dba2c9b58f7fd4ad34636e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:40:52.634092Z","signature_b64":"4YCtRWZCAHnPrqZrcOZEBQEL4Y1ov8Jk/PzeK6l/hOsW0K4GCg17O9DTVjjdSHUfyV7N5XzQd+oRZ1Pl5tPsBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"02baf78b903539e544641dc2294805e074aaa9226dee12d0362b355c79687278","last_reissued_at":"2026-07-05T10:40:52.633587Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:40:52.633587Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SKDU at De-Factify 4.0: Natural Language Features for AI-Generated Text-Detection","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Miguel Arevalillo-Herr\\'aez, Pablo Arnau-Gonz\\'alez, Shrikant Malviya, Stamos Katsigiannis","submitted_at":"2025-03-28T11:25:05Z","abstract_excerpt":"The rapid advancement of large language models (LLMs) has introduced new challenges in distinguishing human-written text from AI-generated content. In this work, we explored a pipelined approach for AI-generated text detection that includes a feature extraction step (i.e. prompt-based rewriting features inspired by RAIDAR and content-based features derived from the NELA toolkit) followed by a classification module. Comprehensive experiments were conducted on the Defactify4.0 dataset, evaluating two tasks: binary classification to differentiate human-written and AI-generated text, and multi-cla"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.22338","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/2503.22338/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2503.22338","created_at":"2026-07-05T10:40:52.633647+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.22338v1","created_at":"2026-07-05T10:40:52.633647+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.22338","created_at":"2026-07-05T10:40:52.633647+00:00"},{"alias_kind":"pith_short_12","alias_value":"AK5PPC4QGU46","created_at":"2026-07-05T10:40:52.633647+00:00"},{"alias_kind":"pith_short_16","alias_value":"AK5PPC4QGU46KRDE","created_at":"2026-07-05T10:40:52.633647+00:00"},{"alias_kind":"pith_short_8","alias_value":"AK5PPC4Q","created_at":"2026-07-05T10:40:52.633647+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.20761","citing_title":"Findings of the Counter Turing Test: AI-Generated Text Detection","ref_index":37,"is_internal_anchor":false},{"citing_arxiv_id":"2605.20761","citing_title":"Findings of the Counter Turing Test: AI-Generated Text Detection","ref_index":26,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AK5PPC4QGU46KRDEDXBCSSAF4B","json":"https://pith.science/pith/AK5PPC4QGU46KRDEDXBCSSAF4B.json","graph_json":"https://pith.science/api/pith-number/AK5PPC4QGU46KRDEDXBCSSAF4B/graph.json","events_json":"https://pith.science/api/pith-number/AK5PPC4QGU46KRDEDXBCSSAF4B/events.json","paper":"https://pith.science/paper/AK5PPC4Q"},"agent_actions":{"view_html":"https://pith.science/pith/AK5PPC4QGU46KRDEDXBCSSAF4B","download_json":"https://pith.science/pith/AK5PPC4QGU46KRDEDXBCSSAF4B.json","view_paper":"https://pith.science/paper/AK5PPC4Q","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.22338&json=true","fetch_graph":"https://pith.science/api/pith-number/AK5PPC4QGU46KRDEDXBCSSAF4B/graph.json","fetch_events":"https://pith.science/api/pith-number/AK5PPC4QGU46KRDEDXBCSSAF4B/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AK5PPC4QGU46KRDEDXBCSSAF4B/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AK5PPC4QGU46KRDEDXBCSSAF4B/action/storage_attestation","attest_author":"https://pith.science/pith/AK5PPC4QGU46KRDEDXBCSSAF4B/action/author_attestation","sign_citation":"https://pith.science/pith/AK5PPC4QGU46KRDEDXBCSSAF4B/action/citation_signature","submit_replication":"https://pith.science/pith/AK5PPC4QGU46KRDEDXBCSSAF4B/action/replication_record"}},"created_at":"2026-07-05T10:40:52.633647+00:00","updated_at":"2026-07-05T10:40:52.633647+00:00"}