{"id":"W4237080054","doi":"10.1109/tmi.2014.2318232","title":"IEEE Transactions on Medical Imaging publication information","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Medical imaging; Computer science; Medical physics; Information retrieval; Artificial intelligence; Medicine","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001128701,0.0009091755,0.0009839672,0.002796147,0.0004716549,0.002168012,0.0009331617,0.002077397,0.3889591],"category_scores_gemma":[0.004916329,0.0004168238,0.0005266535,0.001936873,0.0003052797,0.001692568,0.000946497,0.001351873,0.2434449],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003884938,"about_ca_system_score_gemma":0.001155998,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009187064,"about_ca_topic_score_gemma":0.001619746,"domain_scores_codex":[0.9993885,0.00007683372,0.00005617625,0.00005746693,0.0003684556,0.00005255579],"domain_scores_gemma":[0.997749,0.0004920879,0.00008899799,0.0003093667,0.001160949,0.0001995826],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001338312,0.00005751086,0.0002312118,0.0003742187,0.00001622133,0.0001153349,0.00001137412,0.0003008065,0.003361737,0.002314904,0.7064661,0.2866167],"study_design_scores_gemma":[0.00003873797,0.00004606605,0.0009398913,0.0002439301,0.00004038482,0.0007286998,0.00002257182,0.003008547,0.003075223,0.00547248,0.9863566,0.00002687737],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.005251046,0.04443451,0.1822906,0.03755714,0.06561319,0.0009625027,0.02978362,0.01013032,0.623977],"genre_scores_gemma":[0.03164239,0.04522252,0.05267226,0.008068997,0.01237125,0.0005552834,0.03413461,0.002698148,0.8126346],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.3889591,"threshold_uncertainty_score":0.8715758,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007603488287088895,"score_gpt":0.2789357131475343,"score_spread":0.2713322248604454,"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."}}