{"id":"W2027162949","doi":"10.1118/1.2988161","title":"Comparison of model and human observer performance for detection and discrimination tasks using dual‐energy x‐ray images","year":2008,"lang":"en","type":"article","venue":"Medical Physics","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":100,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; Ontario Institute for Cancer Research; University of Toronto","funders":"National Cancer Institute; National Institutes of Health; University of Toronto; Carestream Health","keywords":"Observer (physics); Artificial intelligence; Smoothing; Computer science; Background subtraction; Mathematics; Energy (signal processing); Noise (video); Computer vision; Subtraction; Algorithm; Pattern recognition (psychology); Pixel; Image (mathematics); Physics; Statistics","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":[],"consensus_categories":[],"category_scores_codex":[0.00003897534,0.00007451164,0.0001303444,0.00001580142,0.0001100039,0.000004130403,0.00002547955,0.00003618607,8.605815e-7],"category_scores_gemma":[0.00001018638,0.00007282751,0.00001616738,0.00004166584,0.00009200718,0.0001979968,0.00001638429,0.00007694626,5.675437e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001458742,"about_ca_system_score_gemma":0.000005814235,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008415414,"about_ca_topic_score_gemma":0.000004226863,"domain_scores_codex":[0.9995596,0.000004281082,0.0001270156,0.00008232336,0.0001268084,0.0000999791],"domain_scores_gemma":[0.9998287,0.00002221442,0.00002773848,0.00005310246,0.00002451094,0.00004374341],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002498334,0.0001438173,0.006674125,0.0009403679,0.00004269206,0.000001831901,0.002081871,0.458028,0.2547416,0.000627993,0.000073808,0.2766189],"study_design_scores_gemma":[0.0002402096,0.0000266945,0.003124585,0.00004395254,0.00001465053,0.00000249107,0.00002890597,0.8884271,0.1070585,0.000936602,0.00001576982,0.00008051003],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5347897,0.0001095691,0.4649973,0.000004196641,0.00002471141,0.00002266558,0.000001659347,0.00002009348,0.00003013174],"genre_scores_gemma":[0.9980907,0.00006875503,0.001718118,0.00001121713,0.00007564579,0.000005574119,0.000006499549,0.00001370107,0.00000971934],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4633011,"threshold_uncertainty_score":0.296982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04780491761681299,"score_gpt":0.2994047153244425,"score_spread":0.2515997977076295,"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."}}