{"id":"W7074635393","doi":"","title":"Abdominal imaging computational and clinical applications ; third international workshop, held in conjunction with MICCAI 2012, Toronto, ON, Canada, September 18 ; revised selected papers","year":2012,"lang":"en","type":"article","venue":"","topic":"Data Analysis with R","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Conjunction (astronomy); Medical imaging; Clinical imaging; High-dynamic-range imaging; Clinical Practice","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004995248,0.00016581,0.0002130083,0.00008144091,0.00009401487,0.0001347565,0.0004148164,0.00004470663,0.0003960311],"category_scores_gemma":[0.00004800079,0.0001388658,0.00003428748,0.0003618466,0.00006708261,0.001119592,0.0001151237,0.0001929073,0.00002481306],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003015414,"about_ca_system_score_gemma":0.0002982128,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01914407,"about_ca_topic_score_gemma":0.251611,"domain_scores_codex":[0.9982666,0.0001097253,0.0004419852,0.0004422395,0.0004503689,0.0002891033],"domain_scores_gemma":[0.9987231,0.0003910966,0.000159724,0.0003229249,0.0002124621,0.0001906254],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002059979,0.0007047587,0.6157997,0.00002039853,0.0004136318,0.00001594385,0.0002188198,0.001776503,0.00007331382,0.02253595,0.2544965,0.1037385],"study_design_scores_gemma":[0.002196868,0.00005386862,0.6710914,0.00007747587,0.00007406699,0.00008425001,0.0002435161,0.2046203,0.00002418246,0.0001113742,0.1208216,0.0006011664],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01300244,0.0004324318,0.9131207,0.005146286,0.0005992542,0.0007260934,0.00002563547,0.0001735003,0.06677363],"genre_scores_gemma":[0.9400054,0.00002225349,0.05337043,0.003563692,0.000263127,0.00007228006,0.0001610764,0.00001591025,0.002525885],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9270029,"threshold_uncertainty_score":0.9873875,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01132821280390747,"score_gpt":0.2827270472029967,"score_spread":0.2713988343990892,"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."}}