{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001038736,0.0003342011,0.0002901097,0.0004646199,0.0001856821,0.0002463867,0.0003041368,0.0002289546,0.001036163],"category_scores_gemma":[0.002920389,0.0001146501,0.0003197799,0.0008501196,0.0001963391,0.0001851209,0.0002328533,0.000242923,0.0001657104],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002896901,"about_ca_system_score_gemma":0.0003592601,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005752485,"about_ca_topic_score_gemma":0.004426436,"domain_scores_codex":[0.9995708,0.0002167114,0.00002502582,0.00007265965,0.00008829688,0.00002655417],"domain_scores_gemma":[0.9985514,0.0007226738,0.0001351728,0.000242258,0.0003136313,0.0000346804],"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":[0.0005188606,0.0002364023,0.06733459,0.0001865805,0.0001655441,0.000252145,0.0002255037,0.8758622,0.01056066,0.001200862,0.001663537,0.04179318],"study_design_scores_gemma":[0.00003669332,0.0001985295,0.08943048,0.0000125614,0.00002621188,0.0001077706,0.0001588444,0.900596,0.006547721,0.0008951632,0.001959992,0.00002991467],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9513744,0.00008180486,0.04368567,0.00003484354,0.00001603335,0.0001566377,0.00300434,0.0002532096,0.001393152],"genre_scores_gemma":[0.97896,0.00004597534,0.01599313,0.000008380385,0.00000590386,0.0001606499,0.004583206,0.00002031841,0.0002224674],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005752485,"threshold_uncertainty_score":0.01143795,"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."}}