{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:HJ4JLWV7YPISL26DW33CHNLHYD","short_pith_number":"pith:HJ4JLWV7","canonical_record":{"source":{"id":"2411.04588","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-11-07T10:17:40Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"642773c319be89d4759cfed26ab5a9876ad8265ca234eab4c6bd3719c07857ad","abstract_canon_sha256":"b34ccdebea1450fafbf827e33d166443cfe7a9b7de109b5428f498041fc2fc27"},"schema_version":"1.0"},"canonical_sha256":"3a7895dabfc3d125ebc3b6f623b567c0db135f194ec5368feb4877070abd5a93","source":{"kind":"arxiv","id":"2411.04588","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.04588","created_at":"2026-07-05T09:32:22Z"},{"alias_kind":"arxiv_version","alias_value":"2411.04588v1","created_at":"2026-07-05T09:32:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.04588","created_at":"2026-07-05T09:32:22Z"},{"alias_kind":"pith_short_12","alias_value":"HJ4JLWV7YPIS","created_at":"2026-07-05T09:32:22Z"},{"alias_kind":"pith_short_16","alias_value":"HJ4JLWV7YPISL26D","created_at":"2026-07-05T09:32:22Z"},{"alias_kind":"pith_short_8","alias_value":"HJ4JLWV7","created_at":"2026-07-05T09:32:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:HJ4JLWV7YPISL26DW33CHNLHYD","target":"record","payload":{"canonical_record":{"source":{"id":"2411.04588","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-11-07T10:17:40Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"642773c319be89d4759cfed26ab5a9876ad8265ca234eab4c6bd3719c07857ad","abstract_canon_sha256":"b34ccdebea1450fafbf827e33d166443cfe7a9b7de109b5428f498041fc2fc27"},"schema_version":"1.0"},"canonical_sha256":"3a7895dabfc3d125ebc3b6f623b567c0db135f194ec5368feb4877070abd5a93","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:32:22.496710Z","signature_b64":"mUU2qsaAVLDadAHHrZY8gDoJ3PCu0FIO1w+OccV6rqWmpATvNHT4VjYi75NEW+emd647oXXBuGJINW8i2otMBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3a7895dabfc3d125ebc3b6f623b567c0db135f194ec5368feb4877070abd5a93","last_reissued_at":"2026-07-05T09:32:22.496245Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:32:22.496245Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2411.04588","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":"2026-07-05T09:32:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uqfiB12kNBuVvkYL8K4KmUJdVEbpel+TA4Ixk+3CHXEKO8LOwXR2THj44YJLHT/pidZKl2Ulr5W7ZE1by6jyDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T06:58:35.805067Z"},"content_sha256":"fbbf6935e90babb67ce0c79547818ec41467473b2ef5ce4cfb57204a60cba316","schema_version":"1.0","event_id":"sha256:fbbf6935e90babb67ce0c79547818ec41467473b2ef5ce4cfb57204a60cba316"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:HJ4JLWV7YPISL26DW33CHNLHYD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Tibyan Corpus: Balanced and Comprehensive Error Coverage Corpus Using ChatGPT for Arabic Grammatical Error Correction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Ahlam Alrehili, Areej Alhothali","submitted_at":"2024-11-07T10:17:40Z","abstract_excerpt":"Natural language processing (NLP) utilizes text data augmentation to overcome sample size constraints. Increasing the sample size is a natural and widely used strategy for alleviating these challenges. In this study, we chose Arabic to increase the sample size and correct grammatical errors. Arabic is considered one of the languages with limited resources for grammatical error correction (GEC). Furthermore, QALB-14 and QALB-15 are the only datasets used in most Arabic grammatical error correction research, with approximately 20,500 parallel examples, which is considered low compared with other"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.04588","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/2411.04588/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":"2026-07-05T09:32:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"c3EP7cpXBWp7mbsOm3PcVq2tnkt2H5DtvUE0Bwn8tMk2cdNf6znnox7PWKjbyJAXqDBvlqnnHPID6cSWRirJBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T06:58:35.805391Z"},"content_sha256":"f2c1c2eee91b065103874517596c1bc2e07a8be01a23bf88b0e62337159b1bcd","schema_version":"1.0","event_id":"sha256:f2c1c2eee91b065103874517596c1bc2e07a8be01a23bf88b0e62337159b1bcd"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HJ4JLWV7YPISL26DW33CHNLHYD/bundle.json","state_url":"https://pith.science/pith/HJ4JLWV7YPISL26DW33CHNLHYD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HJ4JLWV7YPISL26DW33CHNLHYD/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":"2026-08-14T06:58:35Z","links":{"resolver":"https://pith.science/pith/HJ4JLWV7YPISL26DW33CHNLHYD","bundle":"https://pith.science/pith/HJ4JLWV7YPISL26DW33CHNLHYD/bundle.json","state":"https://pith.science/pith/HJ4JLWV7YPISL26DW33CHNLHYD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HJ4JLWV7YPISL26DW33CHNLHYD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:HJ4JLWV7YPISL26DW33CHNLHYD","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":"b34ccdebea1450fafbf827e33d166443cfe7a9b7de109b5428f498041fc2fc27","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-11-07T10:17:40Z","title_canon_sha256":"642773c319be89d4759cfed26ab5a9876ad8265ca234eab4c6bd3719c07857ad"},"schema_version":"1.0","source":{"id":"2411.04588","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.04588","created_at":"2026-07-05T09:32:22Z"},{"alias_kind":"arxiv_version","alias_value":"2411.04588v1","created_at":"2026-07-05T09:32:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.04588","created_at":"2026-07-05T09:32:22Z"},{"alias_kind":"pith_short_12","alias_value":"HJ4JLWV7YPIS","created_at":"2026-07-05T09:32:22Z"},{"alias_kind":"pith_short_16","alias_value":"HJ4JLWV7YPISL26D","created_at":"2026-07-05T09:32:22Z"},{"alias_kind":"pith_short_8","alias_value":"HJ4JLWV7","created_at":"2026-07-05T09:32:22Z"}],"graph_snapshots":[{"event_id":"sha256:f2c1c2eee91b065103874517596c1bc2e07a8be01a23bf88b0e62337159b1bcd","target":"graph","created_at":"2026-07-05T09:32:22Z","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/2411.04588/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Natural language processing (NLP) utilizes text data augmentation to overcome sample size constraints. Increasing the sample size is a natural and widely used strategy for alleviating these challenges. In this study, we chose Arabic to increase the sample size and correct grammatical errors. Arabic is considered one of the languages with limited resources for grammatical error correction (GEC). Furthermore, QALB-14 and QALB-15 are the only datasets used in most Arabic grammatical error correction research, with approximately 20,500 parallel examples, which is considered low compared with other","authors_text":"Ahlam Alrehili, Areej Alhothali","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-11-07T10:17:40Z","title":"Tibyan Corpus: Balanced and Comprehensive Error Coverage Corpus Using ChatGPT for Arabic Grammatical Error Correction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.04588","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:fbbf6935e90babb67ce0c79547818ec41467473b2ef5ce4cfb57204a60cba316","target":"record","created_at":"2026-07-05T09:32:22Z","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":"b34ccdebea1450fafbf827e33d166443cfe7a9b7de109b5428f498041fc2fc27","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-11-07T10:17:40Z","title_canon_sha256":"642773c319be89d4759cfed26ab5a9876ad8265ca234eab4c6bd3719c07857ad"},"schema_version":"1.0","source":{"id":"2411.04588","kind":"arxiv","version":1}},"canonical_sha256":"3a7895dabfc3d125ebc3b6f623b567c0db135f194ec5368feb4877070abd5a93","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3a7895dabfc3d125ebc3b6f623b567c0db135f194ec5368feb4877070abd5a93","first_computed_at":"2026-07-05T09:32:22.496245Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:32:22.496245Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"mUU2qsaAVLDadAHHrZY8gDoJ3PCu0FIO1w+OccV6rqWmpATvNHT4VjYi75NEW+emd647oXXBuGJINW8i2otMBg==","signature_status":"signed_v1","signed_at":"2026-07-05T09:32:22.496710Z","signed_message":"canonical_sha256_bytes"},"source_id":"2411.04588","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fbbf6935e90babb67ce0c79547818ec41467473b2ef5ce4cfb57204a60cba316","sha256:f2c1c2eee91b065103874517596c1bc2e07a8be01a23bf88b0e62337159b1bcd"],"state_sha256":"248d6f4fd2d69fe1c9dc6a6d8d10783ae2a0e5a199118211c528363dcda54829"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4M7pTRL1h04LZz7/anaDMOKPxWQGtUtTaQbEKVChRLaf6gq9rkydgVWX7nCO5/wAOHpZp1OOr2BixgXP9krVCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-14T06:58:35.807770Z","bundle_sha256":"57d0e5cd856a149108574750a067639737e55d4a78f20fc660f605a4909e281b"}}