{"id":"W2329397361","doi":"10.1021/ie503783p","title":"Dual Updating Strategy for Moving-Window Partial Least-Squares Based on Model Performance Assessment","year":2015,"lang":"en","type":"article","venue":"Industrial & Engineering Chemistry Research","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China; University of Alberta","keywords":"Computer science; Dual (grammatical number); Partial least squares regression; Process (computing); Data mining; Artificial intelligence; Algorithm; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001405514,0.0002750849,0.0002875662,0.0001262803,0.0001284391,0.0001909111,0.0002629992,0.0003453473,0.00002756487],"category_scores_gemma":[0.000397797,0.0002938582,0.00009008374,0.0003165888,0.00003313509,0.0001423333,0.00003653187,0.001121984,0.000008809993],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005226872,"about_ca_system_score_gemma":0.0003834746,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002005975,"about_ca_topic_score_gemma":9.392844e-7,"domain_scores_codex":[0.9975945,0.00003705571,0.0003965393,0.0003389983,0.000863201,0.0007697212],"domain_scores_gemma":[0.9988302,0.000219257,0.00003473235,0.0003625082,0.0001970506,0.0003562557],"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":[0.00008937901,0.00002529272,0.00004730972,0.0001096195,0.00002623819,0.000003880988,0.00002385898,0.8644716,0.1317497,0.00002007989,0.001775327,0.001657773],"study_design_scores_gemma":[0.002090762,0.0001429705,0.000008244882,0.00009182834,0.000005421607,0.000003669654,0.0001422803,0.8564786,0.1368081,0.000002853352,0.003976224,0.0002490377],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9845073,0.00004613623,0.005381527,0.0002203611,0.0007954055,0.0009188007,0.00009644083,0.000806053,0.007227978],"genre_scores_gemma":[0.9975313,0.00000195048,0.00009790433,0.000007552683,0.001466367,0.0004189271,0.00004315178,0.00007765363,0.0003552188],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01302398,"threshold_uncertainty_score":0.9999514,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1459898618367568,"score_gpt":0.3507182448751892,"score_spread":0.2047283830384324,"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."}}