{"id":"W2317943281","doi":"10.1021/ie401556r","title":"Nonlinear Process Monitoring Using Supervised Locally Linear Embedding Projection","year":2013,"lang":"en","type":"article","venue":"Industrial & Engineering Chemistry Research","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; University of British Columbia","funders":"","keywords":"Nonlinear system; Computer science; Embedding; Process (computing); Projection (relational algebra); Process control; Stability (learning theory); Model predictive control; Principal component analysis; Control theory (sociology); Control engineering; Control (management); Algorithm; Artificial intelligence; Machine learning; Engineering","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":[0.0006120227,0.0008789917,0.0006893412,0.0005051382,0.000239924,0.0005001992,0.0007493986,0.0005692518,0.0008497904],"category_scores_gemma":[0.002034135,0.0004503875,0.0005323302,0.000454984,0.0006187417,0.001052802,0.0008422045,0.0008822978,0.0003208249],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003675224,"about_ca_system_score_gemma":0.000710585,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002937998,"about_ca_topic_score_gemma":0.003102849,"domain_scores_codex":[0.9994699,0.000171282,0.00002296052,0.0001601862,0.0001252692,0.00005034495],"domain_scores_gemma":[0.9989375,0.0004422505,0.0002111911,0.0001392761,0.0002295708,0.00004017133],"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":[0.0003670061,0.0002346182,0.002431213,0.0001429744,0.0001092036,0.0001626261,0.000196333,0.5994549,0.02634189,0.003354895,0.001890944,0.3653134],"study_design_scores_gemma":[0.000003552987,0.00002689705,0.0002187187,0.000001532462,0.000003102743,0.00001088435,0.000005214201,0.9970613,0.001794053,0.0007641114,0.0001066555,0.000003942148],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03712521,0.00009824614,0.9610822,0.00008652894,0.00001335877,0.00003239032,0.00003337394,0.00102063,0.00050806],"genre_scores_gemma":[0.7646956,0.000130059,0.2320494,0.0000683979,0.00004176349,0.0001128449,0.0002772645,0.0001209173,0.002503763],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002937998,"threshold_uncertainty_score":0.005841792,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08134462792668941,"score_gpt":0.3455201334666195,"score_spread":0.2641755055399301,"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."}}