{"id":"W3045686195","doi":"10.1007/978-3-030-00928-1_100","title":"Correction to: Medical Image Computing and Computer Assisted Intervention – MICCAI 2018","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Section (typography); Special section; Information retrieval; Data science; Library science; Operating system; Engineering physics; Engineering","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":[0.001446658,0.002231482,0.001963147,0.004003625,0.001326992,0.003715178,0.001979791,0.003574231,0.2055783],"category_scores_gemma":[0.01287173,0.000821477,0.001168741,0.002119834,0.001399591,0.00246562,0.002048587,0.00458168,0.1088373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001648514,"about_ca_system_score_gemma":0.002772716,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003390873,"about_ca_topic_score_gemma":0.007909136,"domain_scores_codex":[0.9982651,0.000211265,0.000231875,0.000296904,0.0008447038,0.0001503125],"domain_scores_gemma":[0.991544,0.001109664,0.0005093238,0.0009876427,0.005249205,0.0006001708],"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":[0.00004078014,0.000006546188,0.0000500095,0.0001723873,0.00001045222,0.00008239598,0.00000771125,0.0001357379,0.0002821757,0.001788633,0.9562681,0.04115498],"study_design_scores_gemma":[0.00001460906,0.00002088733,0.0004869029,0.000165633,0.000013302,0.0006923866,0.00001826493,0.001008714,0.001075287,0.002796866,0.9936824,0.00002482964],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"other","genre_scores_codex":[0.00109947,0.01955827,0.02003556,0.04365164,0.8577662,0.0001671296,0.00187217,0.00501085,0.05083869],"genre_scores_gemma":[0.01444381,0.02102498,0.01976494,0.01381023,0.165899,0.0002677496,0.002967315,0.005025876,0.7567961],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.2055783,"threshold_uncertainty_score":0.6877278,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01006747564796312,"score_gpt":0.2495182925263927,"score_spread":0.2394508168784296,"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."}}