{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002618315,0.001032103,0.001977376,0.001161548,0.0007538236,0.002534259,0.001772298,0.002041186,0.003071961],"category_scores_gemma":[0.008398205,0.0004877797,0.001272903,0.001234898,0.001431603,0.001744332,0.002085167,0.001789575,0.0008395099],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008057043,"about_ca_system_score_gemma":0.0005114351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009353662,"about_ca_topic_score_gemma":0.0006726996,"domain_scores_codex":[0.9981877,0.0007671568,0.0001151546,0.0004042209,0.0003345463,0.0001911671],"domain_scores_gemma":[0.9972307,0.001721885,0.0002480643,0.0002872086,0.0003880747,0.000124072],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001058946,0.00007390232,0.001331427,0.0001015334,0.0001031242,0.0001829681,0.00008864778,0.8921025,0.0006409981,0.04316182,0.002017068,0.06009003],"study_design_scores_gemma":[0.000008548021,0.00001829532,0.0001281744,0.000008798991,0.000007416507,0.00002135655,0.0000122697,0.9664196,0.0001622816,0.03256027,0.000646287,0.000006766214],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02406668,0.0006454268,0.9690313,0.0004894118,0.0000942014,0.0000540769,0.0001260679,0.0003198225,0.005173095],"genre_scores_gemma":[0.8361698,0.0008234711,0.1529111,0.0005093041,0.0003806216,0.000279364,0.0007676022,0.0001031805,0.008055676],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003071961,"threshold_uncertainty_score":0.01384711,"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."}}