{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:FMPJ2HZJNXMAGCZMNUN7PJVTGW","short_pith_number":"pith:FMPJ2HZJ","schema_version":"1.0","canonical_sha256":"2b1e9d1f296dd8030b2c6d1bf7a6b335bf62e5e5dcd2747212cb7478cff0b86b","source":{"kind":"arxiv","id":"2304.10578","version":2},"attestation_state":"computed","paper":{"title":"Quantifying the Benefit of Artificial Intelligence for Scientific Research","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CY","physics.data-an","physics.soc-ph"],"primary_cat":"cs.DL","authors_text":"Dashun Wang, Jian Gao","submitted_at":"2023-04-17T08:08:50Z","abstract_excerpt":"The ongoing artificial intelligence (AI) revolution has the potential to change almost every line of work. As AI capabilities continue to improve in accuracy, robustness, and reach, AI may outperform and even replace human experts across many valuable tasks. Despite enormous effort devoted to understanding the impact of AI on labor and the economy and AI's recent successes in accelerating scientific discovery and progress, we lack a systematic understanding of how AI advances may benefit scientific research across disciplines and fields. Here, drawing from the literature on the future of work "},"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":"2304.10578","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DL","submitted_at":"2023-04-17T08:08:50Z","cross_cats_sorted":["cs.AI","cs.CY","physics.data-an","physics.soc-ph"],"title_canon_sha256":"5f1893929a176419b42f0762295edc7de6dfebfa7ab3ecee8db4c97be99692c2","abstract_canon_sha256":"95abd31b1ee28e29f0496acd033cf70bd25ff668d930d87ec87e2f887b0c825e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:25:48.363881Z","signature_b64":"U9jvwAzldLMhI3V+gxTaNWL0pNU4NKJukdSrVvhvrZ+QSKiq7kKN3QMboGLlFEaweDEtjJdZJ8Y8zuyLKn+kAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2b1e9d1f296dd8030b2c6d1bf7a6b335bf62e5e5dcd2747212cb7478cff0b86b","last_reissued_at":"2026-07-05T08:25:48.363346Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:25:48.363346Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Quantifying the Benefit of Artificial Intelligence for Scientific Research","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CY","physics.data-an","physics.soc-ph"],"primary_cat":"cs.DL","authors_text":"Dashun Wang, Jian Gao","submitted_at":"2023-04-17T08:08:50Z","abstract_excerpt":"The ongoing artificial intelligence (AI) revolution has the potential to change almost every line of work. As AI capabilities continue to improve in accuracy, robustness, and reach, AI may outperform and even replace human experts across many valuable tasks. Despite enormous effort devoted to understanding the impact of AI on labor and the economy and AI's recent successes in accelerating scientific discovery and progress, we lack a systematic understanding of how AI advances may benefit scientific research across disciplines and fields. Here, drawing from the literature on the future of work "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2304.10578","kind":"arxiv","version":2},"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/2304.10578/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":"2304.10578","created_at":"2026-07-05T08:25:48.363428+00:00"},{"alias_kind":"arxiv_version","alias_value":"2304.10578v2","created_at":"2026-07-05T08:25:48.363428+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2304.10578","created_at":"2026-07-05T08:25:48.363428+00:00"},{"alias_kind":"pith_short_12","alias_value":"FMPJ2HZJNXMA","created_at":"2026-07-05T08:25:48.363428+00:00"},{"alias_kind":"pith_short_16","alias_value":"FMPJ2HZJNXMAGCZM","created_at":"2026-07-05T08:25:48.363428+00:00"},{"alias_kind":"pith_short_8","alias_value":"FMPJ2HZJ","created_at":"2026-07-05T08:25:48.363428+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.09628","citing_title":"Bridging AI and Science: Implications from a Large-Scale Literature Analysis of AI4Science","ref_index":7,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FMPJ2HZJNXMAGCZMNUN7PJVTGW","json":"https://pith.science/pith/FMPJ2HZJNXMAGCZMNUN7PJVTGW.json","graph_json":"https://pith.science/api/pith-number/FMPJ2HZJNXMAGCZMNUN7PJVTGW/graph.json","events_json":"https://pith.science/api/pith-number/FMPJ2HZJNXMAGCZMNUN7PJVTGW/events.json","paper":"https://pith.science/paper/FMPJ2HZJ"},"agent_actions":{"view_html":"https://pith.science/pith/FMPJ2HZJNXMAGCZMNUN7PJVTGW","download_json":"https://pith.science/pith/FMPJ2HZJNXMAGCZMNUN7PJVTGW.json","view_paper":"https://pith.science/paper/FMPJ2HZJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2304.10578&json=true","fetch_graph":"https://pith.science/api/pith-number/FMPJ2HZJNXMAGCZMNUN7PJVTGW/graph.json","fetch_events":"https://pith.science/api/pith-number/FMPJ2HZJNXMAGCZMNUN7PJVTGW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FMPJ2HZJNXMAGCZMNUN7PJVTGW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FMPJ2HZJNXMAGCZMNUN7PJVTGW/action/storage_attestation","attest_author":"https://pith.science/pith/FMPJ2HZJNXMAGCZMNUN7PJVTGW/action/author_attestation","sign_citation":"https://pith.science/pith/FMPJ2HZJNXMAGCZMNUN7PJVTGW/action/citation_signature","submit_replication":"https://pith.science/pith/FMPJ2HZJNXMAGCZMNUN7PJVTGW/action/replication_record"}},"created_at":"2026-07-05T08:25:48.363428+00:00","updated_at":"2026-07-05T08:25:48.363428+00:00"}