{"id":"W1601612549","doi":"10.5555/1239208.1239212","title":"Applying brain emotional learning algorithm for multivariable control of HVAC systems","year":2006,"lang":"en","type":"article","venue":"Journal of Intelligent & Fuzzy Systems","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Multivariable calculus; Computer science; HVAC; Control (management); Task (project management); Nonlinear system; Satisficing; Control theory (sociology); Controller (irrigation); Artificial intelligence; Control engineering; Algorithm; Air conditioning; 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":[],"consensus_categories":[],"category_scores_codex":[0.001443008,0.0002042572,0.0005633082,0.0002860953,0.000186013,0.000147115,0.0002733417,0.0001113378,0.00000786312],"category_scores_gemma":[0.0006154974,0.0001639984,0.000293062,0.0002500084,0.00005096022,0.0002503202,0.00002425401,0.0003209382,0.000008162911],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001551976,"about_ca_system_score_gemma":0.00008018137,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001913067,"about_ca_topic_score_gemma":0.000001233124,"domain_scores_codex":[0.9970716,0.0003270308,0.001355242,0.0002546854,0.0006646812,0.0003267121],"domain_scores_gemma":[0.9963481,0.001387822,0.001523487,0.000136219,0.0005071205,0.0000972459],"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":[0.0002560963,0.0003924244,0.001439921,0.0005112538,0.0001034384,0.00004485971,0.00008350943,0.3387004,0.622368,0.02185401,0.003784235,0.01046185],"study_design_scores_gemma":[0.001616634,0.0009123007,0.0001318498,0.0007087821,0.00005831113,0.0006937784,0.0004998388,0.9342841,0.02014979,0.0006348498,0.0400198,0.0002899455],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04107359,0.0009467385,0.9479684,0.000202449,0.006602623,0.001868536,0.0001039884,0.00004355883,0.001190165],"genre_scores_gemma":[0.9955432,0.00002582277,0.0004435564,0.00006082304,0.001209729,0.00006364081,0.000006204209,0.00003396711,0.002613099],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9544696,"threshold_uncertainty_score":0.668766,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0227480463438248,"score_gpt":0.2567668002097929,"score_spread":0.2340187538659681,"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."}}