{"id":"W2030570659","doi":"10.1021/ie048811p","title":"Iterative Learning Control for Final Batch Product Quality Using Partial Least Squares Models","year":2005,"lang":"en","type":"article","venue":"Industrial & Engineering Chemistry Research","topic":"Iterative Learning Control Systems","field":"Engineering","cited_by":71,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; McMaster University","keywords":"Partial least squares regression; Iterative learning control; Latent variable; Variable (mathematics); Computer science; Trajectory; Product (mathematics); Process (computing); Mathematical optimization; Quality (philosophy); Least-squares function approximation; Batch processing; Control theory (sociology); Algorithm; Control (management); Mathematics; Artificial intelligence; Statistics; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00272798,0.0004408014,0.0006224968,0.0001608578,0.0003276207,0.0003509379,0.0004044224,0.0003799273,0.00006714434],"category_scores_gemma":[0.001677796,0.0004838053,0.0001811783,0.0004663319,0.00008957181,0.0004632456,0.00006386827,0.002253682,0.00001717064],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006949337,"about_ca_system_score_gemma":0.0001844717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005128272,"about_ca_topic_score_gemma":0.000001938477,"domain_scores_codex":[0.9963216,0.0002636249,0.0007510954,0.0006155375,0.0007998496,0.001248279],"domain_scores_gemma":[0.9979236,0.0008226136,0.00008680573,0.0004069681,0.0004989724,0.000261038],"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":[0.000113924,0.00002044503,0.0001396531,0.0001368663,0.00009132492,0.000003453903,0.0002955011,0.5957845,0.4018249,0.00005331394,0.000259852,0.001276266],"study_design_scores_gemma":[0.002607042,0.00005457797,0.00001638805,0.0001820266,0.00001793481,0.0000126971,0.00009375108,0.8156405,0.1676401,0.00001021612,0.01329912,0.0004256816],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9474098,0.0008850324,0.04674935,0.0004242413,0.000635003,0.001896776,0.0001204509,0.0009577686,0.000921571],"genre_scores_gemma":[0.992115,0.000003672713,0.0002968095,0.00000416025,0.006249771,0.0003208354,0.00004293517,0.0001314233,0.0008353669],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2341848,"threshold_uncertainty_score":0.9997613,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2090661831334004,"score_gpt":0.3764119933380143,"score_spread":0.167345810204614,"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."}}