{"id":"W3112167241","doi":"10.1109/tcyb.2020.3035800","title":"Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Cybernetics","topic":"Machine Learning and ELM","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Higher Education Discipline Innovation Project; China Scholarship Council; Fundamental Research Funds for the Central Universities; Natural Science Foundation of Hubei Province; National Natural Science Foundation of China","keywords":"Process (computing); Sintering; Fuzzy logic; Kernel (algebra); Time series; Efficient energy use; Computer science; Series (stratigraphy); Process engineering; Cluster analysis; Carbon fibers; Artificial intelligence; Materials science; Machine learning; Algorithm; Engineering; Mathematics; Metallurgy","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":[0.0009712689,0.0007219869,0.0009584717,0.0006488403,0.0004904269,0.0008899019,0.001256368,0.001124952,0.0009371744],"category_scores_gemma":[0.001856867,0.0003576855,0.0008720925,0.0006218152,0.0004817429,0.0008545471,0.0005455543,0.001190902,0.0002195195],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001084196,"about_ca_system_score_gemma":0.001155403,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03421161,"about_ca_topic_score_gemma":0.01538041,"domain_scores_codex":[0.9996036,0.00006697337,0.00002727077,0.0001322048,0.00009979241,0.00007021856],"domain_scores_gemma":[0.9994611,0.0002325741,0.00007022877,0.00002914284,0.0001851443,0.00002185639],"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":[0.00004048304,0.00002758154,0.0006886519,0.0000226237,0.0000239305,0.00003046146,0.00002904469,0.9836908,0.0007443405,0.001129542,0.0002566736,0.01331595],"study_design_scores_gemma":[7.277396e-7,0.000003500537,0.00008766822,7.64679e-7,0.000001770901,0.000001475609,0.000001264414,0.9996637,0.00007277501,0.0001420749,0.00002243757,0.000001814744],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1583532,0.001067173,0.8360159,0.0003501242,0.00007050104,0.00007156293,0.0001571406,0.0006764547,0.003237916],"genre_scores_gemma":[0.9751298,0.0003061786,0.02165589,0.00006133945,0.00001863236,0.0000912158,0.0001687513,0.0000246222,0.002543584],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03421161,"threshold_uncertainty_score":0.06802493,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01383006832707886,"score_gpt":0.236171609389738,"score_spread":0.2223415410626591,"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."}}