{"id":"W7116401462","doi":"10.1016/j.asoc.2025.114454","title":"MoBLS: A broad learning system with multi-objective optimization","year":2025,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Machine Learning and ELM","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Shandong Province; National University's Basic Research Foundation of China; Dalian University of Technology; National Natural Science Foundation of China","keywords":"Generalization; Convex optimization; Optimization problem; Population; Competition (biology); Regular polygon; Global optimization","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":[0.001340273,0.0009248524,0.0009079389,0.001290009,0.0006478757,0.001090519,0.001697071,0.001287861,0.01703574],"category_scores_gemma":[0.003337272,0.0006254343,0.0006489964,0.001033899,0.0004781956,0.001708634,0.003581269,0.001810304,0.006000543],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004999592,"about_ca_system_score_gemma":0.001197058,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001971551,"about_ca_topic_score_gemma":0.003914569,"domain_scores_codex":[0.9996457,0.0001123277,0.00002075966,0.00007030474,0.0001130377,0.00003782145],"domain_scores_gemma":[0.9993643,0.0002554707,0.00003330602,0.00007998978,0.0001848558,0.00008193167],"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":[0.0006361612,0.0003138907,0.001531636,0.0003606382,0.0002006274,0.0001900723,0.0001264618,0.2809064,0.006158306,0.01240885,0.03954417,0.6576228],"study_design_scores_gemma":[0.00005773617,0.00009851619,0.0002252977,0.00002109276,0.00001727059,0.00004439592,0.00001791588,0.977448,0.002238275,0.00928702,0.01052929,0.00001525517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01240217,0.0004221883,0.9550856,0.0003944739,0.0001472959,0.0001698273,0.0006190604,0.02506028,0.005699082],"genre_scores_gemma":[0.1847775,0.0004153794,0.7905091,0.000829338,0.0001638007,0.0007784177,0.001920757,0.003043937,0.0175618],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01703574,"threshold_uncertainty_score":0.05699027,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005492008602351997,"score_gpt":0.2272519735108464,"score_spread":0.2217599649084944,"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."}}