{"id":"W2091775056","doi":"10.1118/1.3352586","title":"Anatomical background and generalized detectability in tomosynthesis and cone‐beam CT","year":2010,"lang":"en","type":"article","venue":"Medical Physics","topic":"Digital Radiography and Breast Imaging","field":"Medicine","cited_by":130,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"California State University, Fullerton; National Institutes of Health; National Cancer Institute; California State University; Johns Hopkins University","keywords":"Tomosynthesis; Imaging phantom; Cone beam computed tomography; Physics; Detector; Noise (video); Optics; Computer science; Mammography; Artificial intelligence; Computed tomography; Medicine; Image (mathematics)","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":[0.0003087928,0.0001308702,0.0003150853,0.00004641505,0.00003339156,0.00003181807,0.00006069916,0.0000671231,0.00008422424],"category_scores_gemma":[0.0002469294,0.0001052323,0.00005958231,0.0001935404,0.0005560442,0.0001240789,0.00005765886,0.0004289295,0.000003607873],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000117721,"about_ca_system_score_gemma":0.00006363316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007401995,"about_ca_topic_score_gemma":0.00004259952,"domain_scores_codex":[0.9989693,0.00002375957,0.0001947855,0.0002756063,0.0003066954,0.0002299237],"domain_scores_gemma":[0.9991786,0.0002161837,0.00002586381,0.0001806697,0.00002321636,0.0003754419],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001620851,0.000568125,0.3377884,0.0001658489,0.00005895197,0.0001985702,0.0001175219,6.271873e-8,0.009573424,0.001358542,0.0001361209,0.6498724],"study_design_scores_gemma":[0.01113377,0.0003646691,0.8622077,0.0004190568,0.0002476529,0.001824839,0.0002105555,0.003726629,0.0843225,0.02991196,0.004694616,0.0009360745],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9972848,0.0001045171,0.0001374537,0.001054345,0.00008532004,0.0001310378,0.000005169511,0.00003715156,0.001160242],"genre_scores_gemma":[0.9989429,0.00002606239,0.0002444096,0.0005713734,0.0001778435,0.000007863122,0.000005111828,0.00001236614,0.00001209153],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6489363,"threshold_uncertainty_score":0.4291247,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01459051640973616,"score_gpt":0.2704184222627238,"score_spread":0.2558279058529876,"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."}}