{"id":"W2073080006","doi":"10.1118/1.4740191","title":"Sci—Fri AM: Imaging — 05: Cone‐beam computed tomography for breast biopsy analysis: Simulations","year":2012,"lang":"en","type":"article","venue":"Medical Physics","topic":"Digital Radiography and Breast Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Laurentian University","funders":"","keywords":"Cone beam computed tomography; Imaging phantom; Computed radiography; Nuclear medicine; Materials science; Optics; Medical imaging; Magnification; X-ray; Cylinder; Contrast (vision); Detective quantum efficiency; Biopsy; Physics; Medicine; Computed tomography; Radiology; Image quality; Geometry; Mathematics; Computer science; Artificial intelligence","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":[0.0002954981,0.0002462466,0.0005148287,0.0003726778,0.0001679621,0.00006152385,0.0001708176,0.00007574414,0.0001018221],"category_scores_gemma":[0.00007895975,0.0002111201,0.0006721237,0.002466273,0.000362213,0.0004304173,0.00005851507,0.0002464261,0.00001611004],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002961692,"about_ca_system_score_gemma":0.00008115422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001786773,"about_ca_topic_score_gemma":0.000001128913,"domain_scores_codex":[0.9979488,0.00002406176,0.0003701301,0.0003141952,0.0007085885,0.0006342774],"domain_scores_gemma":[0.9983281,0.0002479052,0.0001090332,0.0003503473,0.0002120539,0.0007525512],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001829996,0.002718218,0.7003435,0.0002550728,0.003279499,0.00003816988,0.0004281109,0.0001981651,0.0004905255,0.00209291,0.005025317,0.2849475],"study_design_scores_gemma":[0.009952732,0.0002629976,0.8226184,0.0008974661,0.01025182,0.00116173,0.0004076853,0.1360786,0.002485772,0.005553225,0.00858619,0.001743475],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3645163,0.00128502,0.6182929,0.006246017,0.001287244,0.001272799,0.0008981195,0.0008027828,0.005398757],"genre_scores_gemma":[0.9959716,0.000002183938,0.0008755911,0.001342467,0.001266901,0.00001892238,0.0004550625,0.00003212919,0.0000351528],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6314552,"threshold_uncertainty_score":0.8609229,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01386862680793074,"score_gpt":0.28084593899621,"score_spread":0.2669773121882792,"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."}}