{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000340596,0.0001543286,0.0002803303,0.0001775359,0.00006983947,0.0001371128,0.00012014,0.00006572156,0.0000257691],"category_scores_gemma":[0.00007906486,0.0001312777,0.00008261935,0.00007252538,0.0000370669,0.0003970994,0.000005323819,0.0001804639,0.000001821366],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007874527,"about_ca_system_score_gemma":0.0000433816,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001411261,"about_ca_topic_score_gemma":0.00000164166,"domain_scores_codex":[0.9988301,0.00006305397,0.0005105412,0.0001089164,0.0003357168,0.0001516814],"domain_scores_gemma":[0.9989352,0.00008731905,0.0002351635,0.00002904282,0.000615288,0.0000979653],"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":[0.0001756821,0.00002536354,0.0001857756,0.00001623383,0.0002243134,0.0000242593,0.0001640758,0.863381,0.006251764,0.0002654189,0.00001394471,0.1292721],"study_design_scores_gemma":[0.003359271,0.0001412969,0.0002299264,0.0001624869,0.00004943053,0.000401981,0.000131278,0.9943092,0.0003037176,0.000379344,0.0003800688,0.0001519342],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02174725,0.003497673,0.9725667,0.00007265544,0.0005549663,0.0001497869,0.000005410142,0.00004813497,0.001357398],"genre_scores_gemma":[0.9981468,0.00003401308,0.001418861,0.0001017505,0.0002499756,0.000007757048,4.60296e-7,0.00001991651,0.00002046292],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9763995,"threshold_uncertainty_score":0.5353348,"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."}}