{"id":"W3109527796","doi":"10.1139/cjce-2020-0100","title":"Prediction of pressure coefficient on setback building by artificial neural network","year":2020,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Wind and Air Flow Studies","field":"Environmental Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Setback; Lift (data mining); Wind tunnel; Drag coefficient; Drag; Aerodynamics; Artificial neural network; Mathematics; Computational fluid dynamics; Mechanics; Mean squared error; Structural engineering; Simulation; Engineering; Statistics; Computer science; Physics; Artificial intelligence; Civil engineering; Data mining","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002655159,0.0007540844,0.0003533658,0.0005840793,0.0002124068,0.0004280172,0.000382232,0.0005077058,0.0008516249],"category_scores_gemma":[0.0008032316,0.000296266,0.0004418327,0.0003784183,0.0001892482,0.000544065,0.0002267381,0.0004432022,0.0001535378],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003634148,"about_ca_system_score_gemma":0.0003948541,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0143497,"about_ca_topic_score_gemma":0.01003154,"domain_scores_codex":[0.9998764,0.00001662064,0.00000684047,0.0000293199,0.0000472227,0.00002363799],"domain_scores_gemma":[0.9997382,0.0001194098,0.00002855395,0.00001653279,0.00008192215,0.00001539903],"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":[0.00006190217,0.0000719114,0.007071535,0.00002966476,0.00001588367,0.00006292031,0.0000187364,0.9643058,0.004550661,0.0001814741,0.0002040569,0.02342536],"study_design_scores_gemma":[0.000001010461,0.00001065829,0.001126682,8.905338e-7,0.000001707386,0.000002360446,0.000003282303,0.9981511,0.0006463714,0.00002980939,0.00002328307,0.000002741029],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.916274,0.0001156034,0.08073618,0.00007190352,0.00003969193,0.00002853321,0.0001546189,0.0004114294,0.002168062],"genre_scores_gemma":[0.9945509,0.00005354076,0.004753516,0.000006863263,0.000002919244,0.00001488294,0.0001055168,0.000009982315,0.0005018568],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0143497,"threshold_uncertainty_score":0.02853233,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01114941258998354,"score_gpt":0.1719204821905564,"score_spread":0.1607710696005729,"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."}}