{"id":"W3095658164","doi":"10.3390/e22111257","title":"Sparse Multicategory Generalized Distance Weighted Discrimination in Ultra-High Dimensions","year":2020,"lang":"en","type":"article","venue":"Entropy","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Feature selection; Penalty method; Consistency (knowledge bases); Lasso (programming language); Uniqueness; Classifier (UML); Pattern recognition (psychology); Mathematical optimization; Operator (biology); Algorithm; Artificial intelligence; Computer science","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.00005448169,0.0001041639,0.0001266701,0.00005095101,0.00006536191,0.0000551343,0.0002689059,0.00004234529,0.00007056336],"category_scores_gemma":[0.00004872599,0.00008860382,0.00003815382,0.0002781792,0.00002353724,0.000353343,0.00005388835,0.0001099974,0.0002270833],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002597279,"about_ca_system_score_gemma":0.00001832276,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004206056,"about_ca_topic_score_gemma":0.00001089165,"domain_scores_codex":[0.9990162,0.00008555439,0.0001909499,0.0003198313,0.0001873084,0.0002002099],"domain_scores_gemma":[0.9995462,0.00004481388,0.00005581125,0.000203752,0.0000336355,0.0001158464],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001245623,0.0004249206,0.001764079,0.00004872516,0.00002110459,0.0001323247,0.00639193,0.0006579889,0.6460445,0.3182503,0.01394615,0.01219341],"study_design_scores_gemma":[0.004768796,0.0001689154,0.0112157,0.0001277953,0.00002337588,0.000005523929,0.0003046775,0.5634972,0.3779916,0.02689939,0.01422664,0.0007703686],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4459729,0.000129777,0.5429099,0.009779818,0.000360259,0.0002580109,0.000008813686,0.0002017895,0.0003786949],"genre_scores_gemma":[0.9767396,0.00006460361,0.02194998,0.001067616,0.0000508006,0.00002482179,0.00003041732,0.000006980869,0.00006513592],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5628393,"threshold_uncertainty_score":0.3613159,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02239314600473118,"score_gpt":0.2415080484927961,"score_spread":0.2191149024880649,"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."}}