{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:AK5PPC4QGU46KRDEDXBCSSAF4B","short_pith_number":"pith:AK5PPC4Q","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"},"canonical_sha256":"02baf78b903539e544641dc2294805e074aaa9226dee12d0362b355c79687278","source":{"kind":"arxiv","id":"2503.22338","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.22338","created_at":"2026-07-05T10:40:52Z"},{"alias_kind":"arxiv_version","alias_value":"2503.22338v1","created_at":"2026-07-05T10:40:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.22338","created_at":"2026-07-05T10:40:52Z"},{"alias_kind":"pith_short_12","alias_value":"AK5PPC4QGU46","created_at":"2026-07-05T10:40:52Z"},{"alias_kind":"pith_short_16","alias_value":"AK5PPC4QGU46KRDE","created_at":"2026-07-05T10:40:52Z"},{"alias_kind":"pith_short_8","alias_value":"AK5PPC4Q","created_at":"2026-07-05T10:40:52Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:AK5PPC4QGU46KRDEDXBCSSAF4B","target":"record","payload":{"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"},"canonical_sha256":"02baf78b903539e544641dc2294805e074aaa9226dee12d0362b355c79687278","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"},"source_kind":"arxiv","source_id":"2503.22338","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":"2026-07-05T10:40:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gfIppNMTushxo1Oc8V98i6AUy1VRTI5XNmkdnqxtkhBMjOhOHGjDRPp+qvkmdVc/kym9ccK+AsyWRizaSB1NDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T12:40:29.951089Z"},"content_sha256":"656227b602a60700323cabfa5b01daebd44634a5a04c6a2647b7dadc0428c62f","schema_version":"1.0","event_id":"sha256:656227b602a60700323cabfa5b01daebd44634a5a04c6a2647b7dadc0428c62f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:AK5PPC4QGU46KRDEDXBCSSAF4B","target":"graph","payload":{"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:40:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gqcz/+Ga60t3pzGIyK/eHQdWVz1DssFEqxM5wAyuYlIi/YO2TpJSVpwHm6hyxHEUdVs841x1myN8KhCrdyKRCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T12:40:29.951875Z"},"content_sha256":"e4d99a9f3a3c3d094fdc7c372b45f9336d838979e07c62ad8eb72e96d16ef201","schema_version":"1.0","event_id":"sha256:e4d99a9f3a3c3d094fdc7c372b45f9336d838979e07c62ad8eb72e96d16ef201"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/AK5PPC4QGU46KRDEDXBCSSAF4B/bundle.json","state_url":"https://pith.science/pith/AK5PPC4QGU46KRDEDXBCSSAF4B/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/AK5PPC4QGU46KRDEDXBCSSAF4B/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":"2026-08-04T12:40:29Z","links":{"resolver":"https://pith.science/pith/AK5PPC4QGU46KRDEDXBCSSAF4B","bundle":"https://pith.science/pith/AK5PPC4QGU46KRDEDXBCSSAF4B/bundle.json","state":"https://pith.science/pith/AK5PPC4QGU46KRDEDXBCSSAF4B/state.json","well_known_bundle":"https://pith.science/.well-known/pith/AK5PPC4QGU46KRDEDXBCSSAF4B/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:AK5PPC4QGU46KRDEDXBCSSAF4B","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":"1275b8763a9921f708ea6c0cbeb4e0ed3ca22367f7dba2c9b58f7fd4ad34636e","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2025-03-28T11:25:05Z","title_canon_sha256":"996cd20ecf98679991252fabe89a7ea7bad52c0011e64a1deb1386411ad26cf7"},"schema_version":"1.0","source":{"id":"2503.22338","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.22338","created_at":"2026-07-05T10:40:52Z"},{"alias_kind":"arxiv_version","alias_value":"2503.22338v1","created_at":"2026-07-05T10:40:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.22338","created_at":"2026-07-05T10:40:52Z"},{"alias_kind":"pith_short_12","alias_value":"AK5PPC4QGU46","created_at":"2026-07-05T10:40:52Z"},{"alias_kind":"pith_short_16","alias_value":"AK5PPC4QGU46KRDE","created_at":"2026-07-05T10:40:52Z"},{"alias_kind":"pith_short_8","alias_value":"AK5PPC4Q","created_at":"2026-07-05T10:40:52Z"}],"graph_snapshots":[{"event_id":"sha256:e4d99a9f3a3c3d094fdc7c372b45f9336d838979e07c62ad8eb72e96d16ef201","target":"graph","created_at":"2026-07-05T10:40:52Z","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/2503.22338/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"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","authors_text":"Miguel Arevalillo-Herr\\'aez, Pablo Arnau-Gonz\\'alez, Shrikant Malviya, Stamos Katsigiannis","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2025-03-28T11:25:05Z","title":"SKDU at De-Factify 4.0: Natural Language Features for AI-Generated Text-Detection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.22338","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":"sha256:656227b602a60700323cabfa5b01daebd44634a5a04c6a2647b7dadc0428c62f","target":"record","created_at":"2026-07-05T10:40:52Z","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":"1275b8763a9921f708ea6c0cbeb4e0ed3ca22367f7dba2c9b58f7fd4ad34636e","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2025-03-28T11:25:05Z","title_canon_sha256":"996cd20ecf98679991252fabe89a7ea7bad52c0011e64a1deb1386411ad26cf7"},"schema_version":"1.0","source":{"id":"2503.22338","kind":"arxiv","version":1}},"canonical_sha256":"02baf78b903539e544641dc2294805e074aaa9226dee12d0362b355c79687278","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"02baf78b903539e544641dc2294805e074aaa9226dee12d0362b355c79687278","first_computed_at":"2026-07-05T10:40:52.633587Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:40:52.633587Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"4YCtRWZCAHnPrqZrcOZEBQEL4Y1ov8Jk/PzeK6l/hOsW0K4GCg17O9DTVjjdSHUfyV7N5XzQd+oRZ1Pl5tPsBA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:40:52.634092Z","signed_message":"canonical_sha256_bytes"},"source_id":"2503.22338","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:656227b602a60700323cabfa5b01daebd44634a5a04c6a2647b7dadc0428c62f","sha256:e4d99a9f3a3c3d094fdc7c372b45f9336d838979e07c62ad8eb72e96d16ef201"],"state_sha256":"73eb5113242d1e9cffd80b328d3147ab0867082fa75638dff870ce00c83284ef"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"W4s11lR/8aZkS3oaGWLTeFT4aL6iNkndDJqM7RZiaRYer5lQclsfC78Eb7GF4YNbLmNmnz5cz4NZRAOYOdHaAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T12:40:29.957566Z","bundle_sha256":"6b21d8dc9f48e356e652dc8cc610c489237ece1eaab7ecdd2814db6b9dcd434e"}}