{"id":"W2320477252","doi":"10.1021/ie202352f","title":"Mean-Squared-Error Methods for Selecting Optimal Parameter Subsets for Estimation","year":2012,"lang":"en","type":"article","venue":"Industrial & Engineering Chemistry Research","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Orthogonalization; Ranking (information retrieval); Robustness (evolution); Mean squared error; Selection (genetic algorithm); Sensitivity (control systems); Computer science; Estimation theory; Rank (graph theory); Model selection; Mathematical optimization; Mathematics; Statistics; Algorithm; Data mining; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0028435,0.0002481178,0.0003215835,0.0001274102,0.0001575877,0.0001260423,0.000231647,0.0004451262,0.00003053237],"category_scores_gemma":[0.002313064,0.0002717825,0.0001560266,0.000410266,0.00002321239,0.0002134562,0.0000289592,0.0006960445,0.000009489158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003399389,"about_ca_system_score_gemma":0.00004807605,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001172173,"about_ca_topic_score_gemma":3.319176e-7,"domain_scores_codex":[0.9979497,0.0000568207,0.0004125675,0.0002602806,0.0002870612,0.001033621],"domain_scores_gemma":[0.9976948,0.001552545,0.00003826934,0.0002705188,0.000175705,0.0002681861],"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":[0.00007787059,0.00002332225,0.00002047622,0.0003562784,0.0001149776,2.816024e-7,0.0002114228,0.1698448,0.8037569,0.00002062107,0.001361709,0.0242114],"study_design_scores_gemma":[0.0007356667,0.00002625919,0.00000263851,0.00003990024,0.00001363411,0.000009038398,0.00007203373,0.5289851,0.4460125,0.000005398156,0.02392,0.0001777379],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4789364,0.0009867351,0.5125918,0.0001100779,0.002758171,0.002689688,0.00007807351,0.001310182,0.0005387976],"genre_scores_gemma":[0.9792921,0.000002454139,0.01659202,0.0000023951,0.002233666,0.001367751,0.00004588507,0.0001133642,0.000350375],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5003557,"threshold_uncertainty_score":0.9999734,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1394518016639621,"score_gpt":0.4176157889181895,"score_spread":0.2781639872542274,"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."}}