{"id":"W2901962248","doi":"10.1002/cjce.23401","title":"Operating performance assessment and non‐optimal cause identification for chemical process","year":2018,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Principal component analysis; Variance (accounting); Identification (biology); Process (computing); Data mining; Similarity (geometry); Feature (linguistics); Computer science; Plot (graphics); Pattern recognition (psychology); Artificial intelligence; Machine learning; Mathematics; Statistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002438919,0.00009665462,0.0001339139,0.00006345622,0.0000734177,0.00009245142,0.0001382639,0.00005843318,0.000005113463],"category_scores_gemma":[0.00005168391,0.00007872343,0.00003480264,0.00008784428,0.00003856222,0.0001250266,0.000003846979,0.0001984305,0.000001080356],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001537703,"about_ca_system_score_gemma":0.00009788221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002833203,"about_ca_topic_score_gemma":0.00001847533,"domain_scores_codex":[0.9993634,0.000003194948,0.0002728629,0.00006500831,0.00009523995,0.0002003],"domain_scores_gemma":[0.9995259,0.00003071783,0.00004351778,0.00007960128,0.0001223882,0.0001978595],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005952244,0.000002814231,0.0001759507,0.000166294,0.00008417934,0.000002456587,0.0007842157,0.04998972,0.9466184,0.00004881756,0.0001721326,0.001949101],"study_design_scores_gemma":[0.0002599275,0.00002348818,0.000183929,0.00006394884,0.00001918016,0.00009619245,0.00003078116,0.7386822,0.2603057,0.000003888519,0.0002373221,0.00009353469],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9933187,0.00007891167,0.005985357,0.00008533034,0.0003335358,0.000112133,0.000002749044,0.00002087321,0.00006236516],"genre_scores_gemma":[0.9989324,0.000001203545,0.0005064546,0.00001703263,0.0004976323,0.00001596433,9.259064e-7,0.00002136577,0.000007072801],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6886924,"threshold_uncertainty_score":0.3210248,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0076436106769416,"score_gpt":0.2290832563342409,"score_spread":0.2214396456572993,"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."}}