{"id":"W2066366643","doi":"10.1002/acs.765","title":"Determining controller benefits via probabilistic optimization","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":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Controller (irrigation); Probabilistic logic; Process (computing); Sensitivity (control systems); Control (management); Computer science; Variance (accounting); Control engineering; Control theory (sociology); Engineering; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007743697,0.001301819,0.001523722,0.002831053,0.0006371855,0.001828581,0.001202652,0.001477835,0.004195333],"category_scores_gemma":[0.03228734,0.001221189,0.001443611,0.001161498,0.001752157,0.002588902,0.001780811,0.001657662,0.0003975696],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002164012,"about_ca_system_score_gemma":0.00176214,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00304525,"about_ca_topic_score_gemma":0.002554024,"domain_scores_codex":[0.9956026,0.002249984,0.0001400737,0.0004365528,0.001244209,0.0003266759],"domain_scores_gemma":[0.9709311,0.02556062,0.001540859,0.0006780548,0.001108328,0.0001808942],"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":[0.00006934247,0.00001545872,0.0006252266,0.00003795807,0.0000409377,0.00002212059,0.00001334852,0.9740826,0.0004126234,0.0157664,0.0002245921,0.008689287],"study_design_scores_gemma":[0.000007250022,0.00002251085,0.0003419217,0.00001244624,0.00001541877,0.00001181975,0.000005493132,0.9848412,0.0003357936,0.01422246,0.0001735604,0.0000101687],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05068535,0.0005087368,0.9391661,0.0005078429,0.00002365043,0.0001119752,0.0001364612,0.000233168,0.008626704],"genre_scores_gemma":[0.9044037,0.0003981346,0.09273519,0.0001483354,0.00006606997,0.0002305694,0.0001774726,0.0001209836,0.001719527],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007743697,"threshold_uncertainty_score":0.0409531,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009940843135797909,"score_gpt":0.2135163298169257,"score_spread":0.2035754866811278,"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."}}