{"id":"W2087553274","doi":"10.1002/acs.771","title":"Performance assessment of level controllers","year":2003,"lang":"en","type":"article","venue":"International Journal of Adaptive Control and Signal Processing","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Smoothing; Controller (irrigation); Variance (accounting); Control theory (sociology); Variable (mathematics); Flow control (data); Computer science; Control engineering; Engineering; Control (management); Mathematics; Artificial intelligence","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":[0.0004005322,0.0001107088,0.0002801423,0.0001586984,0.00003740798,0.00004308076,0.0001173216,0.00004266382,0.00002330821],"category_scores_gemma":[0.000023316,0.00009077595,0.00008188407,0.00005609702,0.0000439372,0.000290609,0.000004261783,0.0001670949,5.643536e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006478385,"about_ca_system_score_gemma":0.00008589529,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002245376,"about_ca_topic_score_gemma":0.000001198048,"domain_scores_codex":[0.9989305,0.00004606959,0.000478724,0.00006771659,0.0003679275,0.0001091355],"domain_scores_gemma":[0.9990495,0.00006024332,0.0002556222,0.00002854685,0.0005421048,0.00006393472],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001063552,0.0001922058,0.01392828,0.0001846315,0.002348225,0.00008135589,0.0007770597,0.2417863,0.2978838,0.002261543,0.0002033706,0.4392897],"study_design_scores_gemma":[0.006498567,0.000333079,0.0110159,0.0002855052,0.00006171221,0.0002393823,0.000463391,0.9767994,0.002431677,0.000169405,0.001526908,0.0001750403],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4806588,0.003381523,0.5010823,0.000149971,0.001035095,0.0001850509,0.0000209825,0.00003426697,0.01345203],"genre_scores_gemma":[0.9990126,0.00005918875,0.000703109,0.00004781421,0.0001223732,0.000003094425,2.241572e-7,0.00001056269,0.00004106619],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7350131,"threshold_uncertainty_score":0.3701736,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01723931279611525,"score_gpt":0.2509671906049387,"score_spread":0.2337278778088234,"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."}}