{"id":"W2040269121","doi":"10.1016/j.buildenv.2008.05.010","title":"Optimization of ventilation systems in office environment, Part II: Results and discussions","year":2008,"lang":"en","type":"article","venue":"Building and Environment","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":54,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Hong Kong University of Science and Technology; Public Works and Government Services Canada; Government of Canada","keywords":"Genetic algorithm; Weighting; Artificial neural network; Ventilation (architecture); Fitness function; Indoor air quality; Sensitivity (control systems); Engineering; Multi-objective optimization; Function (biology); Thermal comfort; Computer science; Simulation; Industrial engineering; Operations research; Artificial intelligence; Machine learning; Mechanical engineering; Environmental engineering","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":[0.0004081959,0.000467053,0.001158157,0.0002896909,0.0002740196,0.000588163,0.0003626836,0.0005278171,0.002530136],"category_scores_gemma":[0.00094211,0.0001757895,0.0007143486,0.0005384845,0.0002535482,0.0005061648,0.0003132507,0.0002504464,0.0001692482],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003478901,"about_ca_system_score_gemma":0.0003086292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005145845,"about_ca_topic_score_gemma":0.003381909,"domain_scores_codex":[0.9997572,0.00008620285,0.0000079384,0.00002741192,0.00005064802,0.0000705014],"domain_scores_gemma":[0.9995003,0.0003990753,0.00002376431,0.00002312368,0.00004085114,0.00001293819],"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":[0.0002462021,0.00009385398,0.001171085,0.0001260721,0.00004699854,0.00002529474,0.0000271751,0.9778295,0.006750546,0.0008319836,0.0002934334,0.01255771],"study_design_scores_gemma":[0.00004767754,0.0002470992,0.003636189,0.000009164088,0.00006026707,0.00002155602,0.00008194953,0.9789649,0.01523002,0.00083924,0.0008424009,0.00001964472],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8559387,0.003144321,0.1233332,0.0001357422,0.00002789808,0.00006311451,0.0003516326,0.0001503893,0.01685497],"genre_scores_gemma":[0.9900346,0.0004071846,0.007408965,0.00001171692,0.000009927342,0.0000295759,0.00009727148,0.00004204137,0.001958623],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005145845,"threshold_uncertainty_score":0.01023179,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01064234468699918,"score_gpt":0.1717857755458356,"score_spread":0.1611434308588364,"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."}}