{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001270301,0.0005676886,0.0002671747,0.001172707,0.0001935999,0.0008175911,0.0004005947,0.000445918,0.0006125231],"category_scores_gemma":[0.006439689,0.0003875282,0.0003138447,0.0004994884,0.0008879483,0.001013949,0.0006869939,0.0003364008,0.00012739],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006039675,"about_ca_system_score_gemma":0.0002948807,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008357359,"about_ca_topic_score_gemma":0.0008608764,"domain_scores_codex":[0.9993394,0.0001556682,0.0000322838,0.0001325325,0.0003019381,0.00003804069],"domain_scores_gemma":[0.9968773,0.00203027,0.0004744813,0.0001983525,0.000322227,0.00009723146],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001364101,0.0001913633,0.04168701,0.0005674898,0.0001949502,0.001078738,0.0006411274,0.2295668,0.5253423,0.01111184,0.0003953795,0.1878588],"study_design_scores_gemma":[0.00003715615,0.0007845453,0.142266,0.00006814538,0.0001333126,0.002191352,0.0001138308,0.6942765,0.1483704,0.009851831,0.001797245,0.0001097427],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6681467,0.0004373119,0.3294444,0.00006081224,0.00001304308,0.00005417885,0.00006584438,0.000302957,0.00147473],"genre_scores_gemma":[0.951929,0.0001589928,0.04740288,0.00002345848,0.0000175058,0.00002770466,0.0000999895,0.00005070035,0.0002896713],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001270301,"threshold_uncertainty_score":0.00671804,"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."}}