{"id":"W2271320187","doi":"10.1002/ceat.201400433","title":"Multivariate Modeling of a Chemical Toner Manufacturing Process","year":2016,"lang":"en","type":"article","venue":"Chemical Engineering & Technology","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; University of Waterloo","funders":"","keywords":"Principal component analysis; Process (computing); Multivariate statistics; Latent variable; Process engineering; Process control; Computer science; Process modeling; Partial least squares regression; Identification (biology); Product (mathematics); Matrix (chemical analysis); Batch processing; Process analytical technology; Unit operation; Process optimization; Work in process; Engineering; Artificial intelligence; Machine learning; Mathematics; Chemistry; Chromatography","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":[0.000833297,0.0006186739,0.0005900737,0.0004368097,0.000350462,0.0008188339,0.0005725897,0.0005201795,0.001140774],"category_scores_gemma":[0.001147317,0.0002818416,0.0006993102,0.0004408655,0.0004046927,0.0004016566,0.0003616857,0.0007012642,0.0001823077],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000650752,"about_ca_system_score_gemma":0.0006901319,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01090898,"about_ca_topic_score_gemma":0.004322691,"domain_scores_codex":[0.9995696,0.0001374617,0.0000164992,0.0001126037,0.0001128051,0.0000510607],"domain_scores_gemma":[0.9994435,0.0003103703,0.00009782223,0.00004551933,0.00008291747,0.00001994085],"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":[0.00006140934,0.00005664825,0.00111415,0.0000221461,0.00002341148,0.00004788566,0.00002721711,0.986774,0.004535188,0.001467924,0.00009038019,0.005779574],"study_design_scores_gemma":[0.000001281055,0.00001362499,0.0002814988,3.589079e-7,0.000001929993,0.000002889215,0.000001540535,0.9991997,0.0003434894,0.000113161,0.00003825959,0.000002242],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3821342,0.0001602781,0.6140125,0.0002207058,0.00002728717,0.00007813743,0.0003215569,0.0007936958,0.002251652],"genre_scores_gemma":[0.9815667,0.0001053086,0.01628555,0.00001578787,0.00001043103,0.00005620783,0.000148685,0.00003427531,0.001777214],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01090898,"threshold_uncertainty_score":0.02169096,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004886595472889019,"score_gpt":0.196832852572438,"score_spread":0.191946257099549,"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."}}