{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005392792,0.001970123,0.001701613,0.002412274,0.001050281,0.003870042,0.001521007,0.00124461,0.02875094],"category_scores_gemma":[0.005164078,0.0009464821,0.0009009831,0.002039921,0.001156423,0.001364053,0.001894689,0.002161657,0.01296903],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001659251,"about_ca_system_score_gemma":0.004541744,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01614009,"about_ca_topic_score_gemma":0.05629985,"domain_scores_codex":[0.9989662,0.0002247704,0.00006221691,0.0002152725,0.0003903397,0.0001412758],"domain_scores_gemma":[0.9955834,0.00056199,0.00008280235,0.0003501994,0.002507305,0.0009143385],"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":[0.000489577,0.0001279876,0.0009596006,0.0005813119,0.0001033149,0.00026076,0.0001550268,0.002285534,0.007000728,0.002570462,0.6660651,0.3194007],"study_design_scores_gemma":[0.0001659747,0.0004152592,0.009585773,0.0005807213,0.0002875053,0.001914569,0.0006535885,0.02921231,0.0247693,0.01217731,0.9201044,0.0001330968],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.04561062,0.1555549,0.5702481,0.05398342,0.06616444,0.001439318,0.01287703,0.01005072,0.08407144],"genre_scores_gemma":[0.08803844,0.08603999,0.2836877,0.002241126,0.0217906,0.0004997498,0.01252459,0.003540582,0.5016373],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.02875094,"threshold_uncertainty_score":0.09618145,"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."}}