{"id":"W4388419882","doi":"10.1002/cjce.25126","title":"Soft sensor based on multi‐phase stacking ensemble model with self‐selected primary learner for batch processes","year":2023,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Shenyang Institute of Automation; Youth Innovation Promotion Association of the Chinese Academy of Sciences; Youth Innovation Promotion Association; Institute of Automation, Chinese Academy of Sciences; Chinese Academy of Sciences","keywords":"Stacking; Hyperparameter; Computer science; Soft sensor; Ensemble learning; Ensemble forecasting; Process (computing); Selection (genetic algorithm); Machine learning; Model selection; Artificial intelligence; Gaussian process; Bayesian inference; Bayesian probability; Gaussian","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001107427,0.0009974341,0.001190616,0.0004790131,0.0004853163,0.0009066591,0.001304086,0.0009718502,0.001124262],"category_scores_gemma":[0.001673423,0.0004659678,0.001133294,0.0004082486,0.0005721748,0.001244593,0.0008660978,0.001308452,0.000296768],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005786675,"about_ca_system_score_gemma":0.0009043653,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005965297,"about_ca_topic_score_gemma":0.004174191,"domain_scores_codex":[0.999549,0.0001140502,0.00002370924,0.0001215615,0.0001424809,0.00004920446],"domain_scores_gemma":[0.9993368,0.0003310344,0.00007974407,0.00003837948,0.0001853145,0.00002875807],"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":[0.00006361712,0.00004516075,0.0009996439,0.00005374887,0.000053709,0.00005748501,0.00005625821,0.9627113,0.003557884,0.00260821,0.0003437989,0.02944921],"study_design_scores_gemma":[0.000001141643,0.00001086513,0.00005511236,0.000001533549,0.000004001348,0.000003594339,0.000001909453,0.9991236,0.0004103054,0.0003260795,0.00005867217,0.000003180089],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03937926,0.0004582407,0.9579443,0.0001621945,0.00004836962,0.0000409043,0.0000462101,0.0003084623,0.001612047],"genre_scores_gemma":[0.9301093,0.0004382311,0.06509041,0.0001344385,0.00006087042,0.0001490205,0.0001413352,0.00004992017,0.003826462],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005965297,"threshold_uncertainty_score":0.01186115,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01100522491939427,"score_gpt":0.2046687960110148,"score_spread":0.1936635710916205,"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."}}