{"id":"W2553676216","doi":"10.1109/ijcnn.2016.7727400","title":"Heterogeneous extreme learning machines","year":2016,"lang":"en","type":"article","venue":"","topic":"Machine Learning and ELM","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Computer science; Machine learning; Data mining; Artificial intelligence; Imputation (statistics); Missing data","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":[],"consensus_categories":[],"category_scores_codex":[0.0001548131,0.00008828905,0.00007923246,0.0000513279,0.00010736,0.00006838944,0.0004495685,0.0000273532,0.0002907595],"category_scores_gemma":[0.00006922177,0.00004842001,0.00004626441,0.00009810602,0.00001541897,0.0001725008,0.0001753969,0.00005447262,0.0006894922],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001063128,"about_ca_system_score_gemma":0.00001059331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004046552,"about_ca_topic_score_gemma":0.000007990557,"domain_scores_codex":[0.9992274,0.00006868156,0.0001003678,0.0002502929,0.0001374056,0.0002158278],"domain_scores_gemma":[0.9995019,0.00008191577,0.00003274681,0.000289192,0.00002211814,0.00007205817],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000001816,0.00001583188,0.008040648,0.00000228383,0.000006894083,0.00001589099,0.0000897022,0.000211634,0.002355403,0.01406002,0.000719989,0.9744799],"study_design_scores_gemma":[0.001474822,0.0004388694,0.01328421,0.00006808006,0.000007598954,0.0003657164,0.000007618204,0.3217904,0.007039363,0.0106021,0.6439483,0.0009729246],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05153103,0.00007332822,0.9136903,0.004127489,0.0002164693,0.00003238686,1.253112e-7,0.0007855795,0.02954325],"genre_scores_gemma":[0.9554621,0.00000912345,0.01237463,0.0002663231,0.00007003876,0.000002745394,1.871477e-7,0.000007139095,0.03180769],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.973507,"threshold_uncertainty_score":0.8862257,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01747902680661823,"score_gpt":0.2316950132626477,"score_spread":0.2142159864560295,"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."}}