{"id":"W2055778119","doi":"10.1002/aic.12352","title":"Robust processes through latent variable modeling and optimization","year":2010,"lang":"en","type":"article","venue":"AIChE Journal","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Robustness (evolution); Latent variable; Process control; Process (computing); Computer science; Principal component analysis; Process modeling; Partial least squares regression; Robustness testing; Process engineering; Work in process; Control engineering; Control theory (sociology); Engineering; Control (management); Machine learning; Artificial intelligence; Operations management","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":[0.0001354969,0.00006822111,0.00008136101,0.00002864443,0.00009983184,0.0001152764,0.00004591631,0.00006444883,0.00006424427],"category_scores_gemma":[0.00003835101,0.00005836087,0.00001355694,0.00009222931,0.000005595875,0.0002708925,0.00000579749,0.0002856984,0.000004359887],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001196849,"about_ca_system_score_gemma":0.00001952862,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000114722,"about_ca_topic_score_gemma":0.00001153619,"domain_scores_codex":[0.999587,0.000007793853,0.0001482378,0.00005697538,0.00008461592,0.0001153498],"domain_scores_gemma":[0.9997857,0.00001087547,0.00002402932,0.00005282306,0.00007492401,0.00005166266],"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":[0.000002862254,0.000003967732,0.00004220767,0.00002593686,0.00001676375,0.0000011406,0.0001227395,0.9958009,0.003503561,0.00003991999,0.0001375361,0.0003025343],"study_design_scores_gemma":[0.0002594982,0.00001025145,0.000004649249,0.00002037382,0.00001144941,0.0002370862,0.00005634346,0.9972718,0.0002359222,0.0001541847,0.001664686,0.00007380232],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06143585,0.0005388964,0.9344995,0.00008471445,0.0008970817,0.00005729358,7.483088e-7,0.0001196537,0.002366283],"genre_scores_gemma":[0.9780489,0.0002488687,0.02120178,0.00003969266,0.0003381096,0.000004222371,6.689165e-7,0.00001787413,0.00009983333],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9166131,"threshold_uncertainty_score":0.2379887,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01542440667545609,"score_gpt":0.2035820479082943,"score_spread":0.1881576412328382,"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."}}