{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:RHXMCIO3PPNKF4QYVGEPDEQ6J3","short_pith_number":"pith:RHXMCIO3","schema_version":"1.0","canonical_sha256":"89eec121db7bdaa2f218a988f1921e4ec13168207d7439534f8e41da0b2ea332","source":{"kind":"arxiv","id":"2312.03769","version":1},"attestation_state":"computed","paper":{"title":"GPT vs Human for Scientific Reviews: A Dual Source Review on Applications of ChatGPT in Science","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Alan John Varghese, Chenxi Wu, George Em Karniadakis, Vivek Oommen","submitted_at":"2023-12-05T21:41:52Z","abstract_excerpt":"The new polymath Large Language Models (LLMs) can speed-up greatly scientific reviews, possibly using more unbiased quantitative metrics, facilitating cross-disciplinary connections, and identifying emerging trends and research gaps by analyzing large volumes of data. However, at the present time, they lack the required deep understanding of complex methodologies, they have difficulty in evaluating innovative claims, and they are unable to assess ethical issues and conflicts of interest. Herein, we consider 13 GPT-related papers across different scientific domains, reviewed by a human reviewer"},"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":"2312.03769","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2023-12-05T21:41:52Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"bc9799215c951c4f7a9ac5c5b62be22e651ec6d526d3d2f7852e33eabe8f089c","abstract_canon_sha256":"a1559f72cbd623fa8cfb6f92a3ed4a8f41e505690b914b3a45c89b7526afa7d7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:21:14.669992Z","signature_b64":"FX5Ejb+apg2GZsLE60EgdDBv5sDGY1nSj08q2TSl9iCi9Zr8KoreJafjqFil52LlJTj668RL6zMSIRjSOSIxBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"89eec121db7bdaa2f218a988f1921e4ec13168207d7439534f8e41da0b2ea332","last_reissued_at":"2026-07-05T07:21:14.669487Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:21:14.669487Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GPT vs Human for Scientific Reviews: A Dual Source Review on Applications of ChatGPT in Science","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Alan John Varghese, Chenxi Wu, George Em Karniadakis, Vivek Oommen","submitted_at":"2023-12-05T21:41:52Z","abstract_excerpt":"The new polymath Large Language Models (LLMs) can speed-up greatly scientific reviews, possibly using more unbiased quantitative metrics, facilitating cross-disciplinary connections, and identifying emerging trends and research gaps by analyzing large volumes of data. However, at the present time, they lack the required deep understanding of complex methodologies, they have difficulty in evaluating innovative claims, and they are unable to assess ethical issues and conflicts of interest. Herein, we consider 13 GPT-related papers across different scientific domains, reviewed by a human reviewer"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.03769","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/2312.03769/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":"2312.03769","created_at":"2026-07-05T07:21:14.669547+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.03769v1","created_at":"2026-07-05T07:21:14.669547+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.03769","created_at":"2026-07-05T07:21:14.669547+00:00"},{"alias_kind":"pith_short_12","alias_value":"RHXMCIO3PPNK","created_at":"2026-07-05T07:21:14.669547+00:00"},{"alias_kind":"pith_short_16","alias_value":"RHXMCIO3PPNKF4QY","created_at":"2026-07-05T07:21:14.669547+00:00"},{"alias_kind":"pith_short_8","alias_value":"RHXMCIO3","created_at":"2026-07-05T07:21:14.669547+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2512.03476","citing_title":"ATHENA: Agentic Team for Hierarchical Evolutionary Numerical Algorithms","ref_index":13,"is_internal_anchor":false},{"citing_arxiv_id":"2601.01972","citing_title":"Hidden State Poisoning Attacks against Mamba-based Language Models","ref_index":21,"is_internal_anchor":false},{"citing_arxiv_id":"2605.11117","citing_title":"GRAFT-ATHENA: Self-Improving Agentic Teams for Autonomous Discovery and Evolutionary Numerical Algorithms","ref_index":17,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/RHXMCIO3PPNKF4QYVGEPDEQ6J3","json":"https://pith.science/pith/RHXMCIO3PPNKF4QYVGEPDEQ6J3.json","graph_json":"https://pith.science/api/pith-number/RHXMCIO3PPNKF4QYVGEPDEQ6J3/graph.json","events_json":"https://pith.science/api/pith-number/RHXMCIO3PPNKF4QYVGEPDEQ6J3/events.json","paper":"https://pith.science/paper/RHXMCIO3"},"agent_actions":{"view_html":"https://pith.science/pith/RHXMCIO3PPNKF4QYVGEPDEQ6J3","download_json":"https://pith.science/pith/RHXMCIO3PPNKF4QYVGEPDEQ6J3.json","view_paper":"https://pith.science/paper/RHXMCIO3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.03769&json=true","fetch_graph":"https://pith.science/api/pith-number/RHXMCIO3PPNKF4QYVGEPDEQ6J3/graph.json","fetch_events":"https://pith.science/api/pith-number/RHXMCIO3PPNKF4QYVGEPDEQ6J3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RHXMCIO3PPNKF4QYVGEPDEQ6J3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RHXMCIO3PPNKF4QYVGEPDEQ6J3/action/storage_attestation","attest_author":"https://pith.science/pith/RHXMCIO3PPNKF4QYVGEPDEQ6J3/action/author_attestation","sign_citation":"https://pith.science/pith/RHXMCIO3PPNKF4QYVGEPDEQ6J3/action/citation_signature","submit_replication":"https://pith.science/pith/RHXMCIO3PPNKF4QYVGEPDEQ6J3/action/replication_record"}},"created_at":"2026-07-05T07:21:14.669547+00:00","updated_at":"2026-07-05T07:21:14.669547+00:00"}