{"id":"W4304185479","doi":"10.1038/s42254-022-00518-3","title":"On scientific understanding with artificial intelligence","year":2022,"lang":"en","type":"review","venue":"Nature Reviews Physics","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":348,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canadian Institute for Advanced Research; Vector Institute; University of Toronto","funders":"University of Toronto; Austrian Science Fund; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Multidisciplinary approach; Computer science; Function (biology); Perspective (graphical); Field (mathematics); Data science; Oracle; Artificial intelligence; Management science; Engineering; Sociology; Mathematics; Social science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","research_integrity","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.01414148,0.0006954543,0.002458359,0.0007224291,0.001108251,0.002541406,0.003918497,0.0002441629,0.001041166],"category_scores_gemma":[0.003074784,0.0004011357,0.00098611,0.008744098,0.0003178317,0.0002667337,0.00107426,0.002316497,0.00266723],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006122593,"about_ca_system_score_gemma":0.0003955682,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001203599,"about_ca_topic_score_gemma":0.000006022866,"domain_scores_codex":[0.9896944,0.001172746,0.001794002,0.002702133,0.00406351,0.0005731747],"domain_scores_gemma":[0.990475,0.003404656,0.001702259,0.004125671,0.0001181873,0.0001742151],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000003031652,0.00007099153,4.599142e-8,0.0008171424,0.00002392132,0.00001072589,0.00003497341,0.0000334226,6.450261e-9,0.1530985,0.04021062,0.8056967],"study_design_scores_gemma":[0.00001506777,0.00006066692,1.665022e-8,0.003938459,0.0002233708,0.000007601244,0.00009798418,0.0001321763,1.479904e-7,0.1026747,0.8924258,0.0004239807],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[9.791893e-8,0.9434854,0.04323506,0.00004666188,0.00477653,0.001461805,0.0001004097,0.00007049627,0.006823564],"genre_scores_gemma":[0.00000815114,0.9963854,0.0007362617,0.0001379685,0.0004381682,0.00007185461,0.0002680343,0.0000509394,0.001903229],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.8522152,"threshold_uncertainty_score":0.9999852,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6312757480339506,"score_gpt":0.506654083983842,"score_spread":0.1246216640501085,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}