{"id":"W7084164363","doi":"","title":"Environment Scan of Generative AI Infrastructure for Clinical and Translational Science.","year":2024,"lang":"en","type":"article","venue":"PubMed","topic":"Educational and Organizational Development","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Center for Advancing Translational Sciences; U.S. National Library of Medicine; National Institutes of Health; Georgia Clinical and Translational Science Alliance; U.S. Department of Veterans Affairs","keywords":"Translational research; Corporate governance; Stakeholder; Health care; Translational science; Leverage (statistics); Workforce; Quality (philosophy); Data governance","routes":{"ca_aff":true,"ca_fund":false,"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":[],"consensus_categories":[],"category_scores_codex":[0.0003711208,0.00005394878,0.00006493118,0.0001078871,0.00008337096,0.0001006706,0.00006549226,0.00002086422,0.00008476865],"category_scores_gemma":[0.00004967273,0.00004409158,0.0000219308,0.0001991701,0.000176867,0.0003659003,0.00003077115,0.00003636818,0.000005167384],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001244071,"about_ca_system_score_gemma":0.00008619954,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002105507,"about_ca_topic_score_gemma":0.000001271118,"domain_scores_codex":[0.9993699,0.00000198172,0.0001727077,0.0001745815,0.0001813024,0.00009951794],"domain_scores_gemma":[0.9998094,0.00004011418,0.00003037965,0.00003454708,0.00007281581,0.00001274052],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00002267182,0.00008130947,0.3331976,0.0002146222,0.00006807523,4.507068e-7,0.0001742554,0.0002084638,0.00008986663,0.4416518,0.01986561,0.2044253],"study_design_scores_gemma":[0.0001021258,0.000001114203,0.8929797,0.000004021164,0.00001328245,4.164531e-7,0.00001481371,0.0006741819,0.00007496897,0.03241491,0.07366673,0.00005367629],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9415818,0.001151352,0.01017468,0.03824022,0.002324161,0.001663148,0.00005093026,0.00007110318,0.004742631],"genre_scores_gemma":[0.9961767,0.00001892632,0.001434262,0.001077971,0.0008069568,0.0001537073,0.00004151856,0.000006856028,0.0002830804],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5597821,"threshold_uncertainty_score":0.1798002,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02562043209206168,"score_gpt":0.2593798966474777,"score_spread":0.2337594645554161,"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."}}