{"id":"W1996605531","doi":"10.1177/153303460400300401","title":"Detectors for Digital Mammography","year":2004,"lang":"en","type":"review","venue":"Technology in Cancer Research & Treatment","topic":"Digital Radiography and Breast Imaging","field":"Medicine","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Sciences Centre; University of Toronto; Sunnybrook Health Science Centre","funders":"","keywords":"Mammography; Digital radiography; Digital mammography; Fluoroscopy; Computer science; Detector; Medical physics; Radiography; Subtraction; Breast imaging; Flat panel detector; Computer vision; Image resolution; Dynamic range; Artificial intelligence; Medicine; Radiology; Telecommunications","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":[0.00168751,0.001411502,0.001836932,0.004868628,0.0007256627,0.001882556,0.002626406,0.003423114,0.01410737],"category_scores_gemma":[0.003065614,0.0008034387,0.001092989,0.004477895,0.001839749,0.003949611,0.001655901,0.00541032,0.02054295],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001575942,"about_ca_system_score_gemma":0.001268201,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009872002,"about_ca_topic_score_gemma":0.000896276,"domain_scores_codex":[0.9986179,0.0002162631,0.0001453773,0.000319854,0.0006260115,0.00007454846],"domain_scores_gemma":[0.9986456,0.0005052247,0.0001385407,0.0001322153,0.0005069514,0.00007149517],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007685515,0.00009038993,0.0002304231,0.007588099,0.00005443218,0.0004825357,0.000128028,0.0003327781,0.005700646,0.02939243,0.06437268,0.8915508],"study_design_scores_gemma":[0.000008401341,0.00002930112,0.0001350859,0.0006366131,0.00001861698,0.001808514,0.00001826416,0.0000556534,0.001309,0.003373397,0.9925894,0.00001775383],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0002575176,0.9736401,0.005609244,0.001267259,0.001750171,0.00005825469,0.00009100977,0.0001077955,0.01721865],"genre_scores_gemma":[0.004444636,0.9505894,0.01575346,0.002886927,0.001549613,0.0001344432,0.0002806895,0.00004339768,0.02431747],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.01410737,"threshold_uncertainty_score":0.04719388,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1498488972044751,"score_gpt":0.4940678459992623,"score_spread":0.3442189487947873,"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."}}