{"id":"W4310261168","doi":"10.1016/j.buildenv.2022.109875","title":"Representative meteorological data for long-term wind-driven rain obtained from Latin Hypercube Sampling – Application to impact analysis of climate change","year":2022,"lang":"en","type":"article","venue":"Building and Environment","topic":"Wind and Air Flow Studies","field":"Environmental Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Latin hypercube sampling; Environmental science; Wind speed; Facade; Meteorology; Sampling (signal processing); Climate change; Intensity (physics); Replicate; Term (time); Statistics; Mathematics; Computer science; Monte Carlo method; Geography; Geology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003616034,0.0001382623,0.0002937553,0.00005890524,0.000307549,0.00001254707,0.0002778205,0.00002640263,0.0005251361],"category_scores_gemma":[0.00002339939,0.0001181111,0.00008406876,0.0002052154,0.00007616071,0.00008334585,0.001494339,0.00006672185,0.000004918573],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001280296,"about_ca_system_score_gemma":0.000001317583,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003900158,"about_ca_topic_score_gemma":0.00002174772,"domain_scores_codex":[0.9986451,0.00006628837,0.0002161974,0.0005961901,0.0002342633,0.0002420232],"domain_scores_gemma":[0.9992075,0.0001686522,0.0001083948,0.0004335623,0.000001347734,0.00008053118],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001063797,0.0001312562,0.859877,0.000005398074,0.0003389677,0.000001626613,0.001365024,0.08039477,0.02742286,0.00001587013,0.00009057476,0.03025024],"study_design_scores_gemma":[0.0002536524,0.0001535413,0.9745763,0.000003635164,0.0003538799,4.271875e-7,0.0002826971,0.02322864,0.0001390271,0.00008323417,0.0007748119,0.0001501357],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9856756,0.00009895347,0.01213382,0.000426198,0.00001750276,0.0004798241,0.001124057,0.00001460806,0.00002939783],"genre_scores_gemma":[0.9892527,0.0001655575,0.00979419,0.0001319843,0.00004117091,0.0001540449,0.0004393686,0.00001038205,0.00001057636],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1146993,"threshold_uncertainty_score":0.5749872,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0663216888693621,"score_gpt":0.3233419084105069,"score_spread":0.2570202195411448,"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."}}