{"id":"W4403712151","doi":"10.1016/j.buildenv.2024.112173","title":"Evaluation of supervised machine learning regression models for CFD-based surrogate modelling in indoor airflow field reconstruction","year":2024,"lang":"en","type":"article","venue":"Building and Environment","topic":"Wind and Air Flow Studies","field":"Environmental Science","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computational fluid dynamics; Airflow; Regression analysis; Computer science; Field (mathematics); Machine learning; Environmental science; Regression; Surrogate model; Artificial intelligence; Engineering; Marine engineering; Aerospace engineering; Statistics; Mathematics; Mechanical 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":[0.001029081,0.0001131427,0.0001335885,0.00005727428,0.0001013158,0.00001628167,0.00004438528,0.00005590755,0.0001136661],"category_scores_gemma":[0.00002213889,0.00009469528,0.00004633521,0.0000644602,0.00004808196,0.0001411905,0.00005097736,0.0001071521,0.000002900448],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001275436,"about_ca_system_score_gemma":0.000007195463,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001544242,"about_ca_topic_score_gemma":0.00001114587,"domain_scores_codex":[0.9989769,0.00006953302,0.0002034094,0.0002919975,0.0003061566,0.000152004],"domain_scores_gemma":[0.9997475,0.0001004727,0.00003847277,0.00007859137,0.000003543537,0.00003137834],"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":[0.00002035883,0.00002067679,0.004814853,0.00003232231,0.000007589089,4.929043e-7,0.0002476442,0.7979121,0.003787356,0.00001884814,0.00001258581,0.1931252],"study_design_scores_gemma":[0.0005112897,0.00007873888,0.0004772115,0.00021839,0.00004312248,0.000001306576,0.00007647812,0.98958,0.00624504,0.002366309,0.00029572,0.0001063717],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9388719,0.001285978,0.05899831,0.0003086708,0.00008431146,0.0002479826,0.000004910325,0.00002055725,0.0001773827],"genre_scores_gemma":[0.9892565,0.0003658701,0.01023147,0.00001573682,0.00002197928,0.00004955921,0.000005633126,0.00001137662,0.00004185358],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1930188,"threshold_uncertainty_score":0.3861561,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03466271137491,"score_gpt":0.2537843480872262,"score_spread":0.2191216367123162,"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."}}