{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005183585,0.0002089901,0.0003199823,0.0002017103,0.000008819143,0.000005517291,0.0002534466,0.0003481468,0.00001722295],"category_scores_gemma":[0.00009532579,0.0001663616,0.00007380184,0.0001884418,0.00003862355,0.00006877329,0.00004346372,0.0002278868,0.00001827265],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009246483,"about_ca_system_score_gemma":0.000006868811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000029806,"about_ca_topic_score_gemma":6.913374e-8,"domain_scores_codex":[0.9989565,0.000002230393,0.0003440165,0.0002268773,0.0001185106,0.0003518538],"domain_scores_gemma":[0.9995655,0.00003327951,0.00002848431,0.0002682662,0.00003390649,0.00007054313],"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":[0.000007337902,0.000009824875,0.00000497556,0.0001076184,0.0000402214,0.000002425016,0.00002124839,0.04813029,0.9471715,0.0001586243,0.000005954881,0.004339948],"study_design_scores_gemma":[0.0003476486,0.000005206139,5.697327e-7,0.00008563742,0.000006246572,0.00001677119,0.000006944958,0.3960311,0.6030978,0.0001251249,0.0001392969,0.0001376272],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9163634,0.0001408973,0.08119628,0.0001048508,0.000198285,0.0001137843,0.000004298796,0.001691747,0.0001864232],"genre_scores_gemma":[0.9990169,0.000004617988,0.0007679731,0.000003785288,0.00006854114,0.00007119498,9.132099e-7,0.00005304661,0.00001305481],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3479008,"threshold_uncertainty_score":0.6784029,"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."}}