{"id":"W2809639820","doi":"10.1007/978-3-319-97785-0_4","title":"Dynamic Voting in Multi-view Learning for Radiomics Applications","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Radiomics; Computer science; Random forest; Voting; Personalization; Classifier (UML); Artificial intelligence; Machine learning; World Wide Web","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001162973,0.00031998,0.0005771609,0.0006437932,0.0001992463,0.0000929219,0.0005052074,0.000242199,0.00002521972],"category_scores_gemma":[0.0004889008,0.0002953696,0.0001223299,0.0003184208,0.0006490545,0.00007816636,0.0001985923,0.001357736,0.00001711969],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003893951,"about_ca_system_score_gemma":0.0003440017,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001522993,"about_ca_topic_score_gemma":0.00005688002,"domain_scores_codex":[0.9977365,0.00001971516,0.0004909764,0.0008946325,0.0003581917,0.0005000275],"domain_scores_gemma":[0.9986151,0.0004646637,0.0002241282,0.0003881543,0.0001539396,0.0001539842],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001696561,0.00004512158,0.001230477,0.0003235996,0.00001643554,0.00002760943,0.0004931766,0.03288063,0.0003326737,0.0005806717,0.00001084336,0.9640418],"study_design_scores_gemma":[0.0008143313,0.0001202106,0.0003356549,0.001094574,0.00002630162,0.00008649705,4.665053e-7,0.9823449,0.0000324075,0.003384256,0.01146685,0.0002935184],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0005071304,0.0008973735,0.995841,0.0009275987,0.0004026543,0.000998541,0.00000197066,0.00007256608,0.0003512214],"genre_scores_gemma":[0.1423557,0.0001990728,0.8532089,0.002010389,0.0006785236,0.00007919152,0.00005913438,0.0001110914,0.001297936],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9637483,"threshold_uncertainty_score":0.9999499,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01698740632542033,"score_gpt":0.3106549781257549,"score_spread":0.2936675718003345,"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."}}