{"id":"W2028128604","doi":"10.1002/cjce.20363","title":"Application of support vector regression for developing soft sensors for nonlinear processes","year":2010,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; University of Alberta","keywords":"Support vector machine; Soft sensor; Nonlinear system; Kernel (algebra); Computer science; Soft computing; Feature vector; Process (computing); Field (mathematics); Range (aeronautics); Feature (linguistics); Mathematical optimization; Machine learning; Artificial intelligence; Mathematics; Engineering; Artificial neural network","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.001475328,0.0008628705,0.0006365709,0.000700625,0.0001534334,0.0007115608,0.0005826115,0.0007597955,0.0008229222],"category_scores_gemma":[0.003203794,0.0003534263,0.0005183521,0.0004667379,0.0003776442,0.0006901073,0.0006224907,0.001123924,0.0003452392],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003292111,"about_ca_system_score_gemma":0.000379344,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007742599,"about_ca_topic_score_gemma":0.0006571785,"domain_scores_codex":[0.9992816,0.0001821446,0.00005515715,0.0001360941,0.0003078385,0.00003719902],"domain_scores_gemma":[0.9978853,0.001108828,0.0003657768,0.0001223528,0.0004739809,0.00004377768],"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":[0.0001740383,0.0001555317,0.002674819,0.0002712345,0.00007541421,0.0001672073,0.00007953676,0.7228396,0.07841591,0.003327393,0.0007292418,0.19109],"study_design_scores_gemma":[0.000002284183,0.0000493467,0.0001436429,0.000003924607,0.000003305376,0.00000962789,0.000004613551,0.9888698,0.01037828,0.000306986,0.0002237404,0.000004550761],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05095612,0.0003273106,0.9466465,0.00014277,0.00003031639,0.0000445918,0.00003962795,0.0008628326,0.0009499604],"genre_scores_gemma":[0.7995496,0.0002624076,0.1985742,0.0000671403,0.00002255173,0.00007447399,0.0001011113,0.00006528939,0.001283103],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001475328,"threshold_uncertainty_score":0.007802427,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006615894627329003,"score_gpt":0.2106738912119791,"score_spread":0.2040579965846501,"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."}}