{"id":"W4398206491","doi":"10.2214/ajr.24.31465","title":"Beyond the <i>AJR</i>: Unpredictably Unequal Effects of Artificial Intelligence Augmentation","year":2024,"lang":"en","type":"letter","venue":"American Journal of Roentgenology","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Centre for Global Health Research","funders":"","keywords":"Medicine; Medical physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005788688,0.0003680936,0.0009355542,0.0005343879,0.004394493,0.005937773,0.001837496,0.04416982,0.006886461],"category_scores_gemma":[0.04375705,0.0003940381,0.0009629265,0.0005177581,0.007213311,0.005620753,0.002197981,0.03890273,0.003178597],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00529737,"about_ca_system_score_gemma":0.005095928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005060243,"about_ca_topic_score_gemma":0.01307355,"domain_scores_codex":[0.9957365,0.001159605,0.0003586531,0.0006241192,0.00153513,0.00058607],"domain_scores_gemma":[0.9697264,0.02401221,0.0008421473,0.001149331,0.002694585,0.001575322],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001275749,0.00004117754,0.0011864,0.00003613719,0.00003348684,0.00216144,0.000324415,0.0001586825,0.000364476,0.04825629,0.9266318,0.020678],"study_design_scores_gemma":[0.0001143981,0.00006688691,0.001820243,0.0002142229,0.00005337854,0.00293122,0.001019713,0.001214469,0.0008539291,0.1923109,0.7993103,0.0000902433],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.0008878299,0.0004416232,0.0001737498,0.9866017,0.004361116,0.000003070464,0.00001644955,0.00001771857,0.007496809],"genre_scores_gemma":[0.02600185,0.0005881463,0.0005310638,0.9370867,0.02750694,0.00002209487,0.00001388007,0.00006239229,0.00818688],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.04416982,"threshold_uncertainty_score":0.03843534,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05018764838473591,"score_gpt":0.3723211745480396,"score_spread":0.3221335261633037,"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."}}