{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001937904,0.00009192439,0.0001719156,0.00008037206,0.00003508692,0.0000180079,0.0001549795,0.00008535296,0.000002408144],"category_scores_gemma":[0.0003228137,0.0000699743,0.00007072616,0.0001094673,0.00001742151,0.00005187743,0.00000225778,0.0001760803,5.37507e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006185249,"about_ca_system_score_gemma":0.0002163369,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004530431,"about_ca_topic_score_gemma":0.0002043893,"domain_scores_codex":[0.999403,0.000001870607,0.0002923873,0.00005206702,0.00007819208,0.0001725001],"domain_scores_gemma":[0.9993694,0.0001251963,0.0000809964,0.00008547512,0.0002061325,0.0001328247],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001975174,0.000002399355,0.00002011454,0.0005590685,0.00005074579,8.135748e-7,0.0002268395,0.02169506,0.9741392,0.0002056283,0.0002620894,0.00281828],"study_design_scores_gemma":[0.000375494,0.00002234274,0.00001219396,0.00008583863,0.00002220315,0.00006306112,0.00001495317,0.3055813,0.6644925,0.00003770283,0.02917285,0.0001195926],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8741884,0.0001971359,0.1232845,0.0005115963,0.001156567,0.0005176415,0.00004994858,0.0000608239,0.00003339595],"genre_scores_gemma":[0.9953704,8.215704e-7,0.004155086,0.00001134229,0.0003944144,0.00002682411,0.000003891678,0.00002759552,0.000009554858],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3096467,"threshold_uncertainty_score":0.2853469,"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."}}