{"id":"W3129827918","doi":"10.5194/gmd-14-961-2021","title":"The Vertical City Weather Generator (VCWG v1.3.2)","year":2021,"lang":"en","type":"article","venue":"Geoscientific model development","topic":"Urban Heat Island Mitigation","field":"Environmental Science","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Eidgenössische Technische Hochschule Zürich; Albert-Ludwigs-Universität Freiburg; Government of Ontario; Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas; Ontario Centres of Excellence; University of Guelph","keywords":"Wind speed; Microclimate; Environmental science; Meteorology; Turbulence kinetic energy; Atmospheric sciences; Humidity; Turbulence; Planetary boundary layer; Relative humidity; Wind direction; Mean squared error; Momentum (technical analysis); Diffusion; Mathematics; Geography; Geology; Physics; Statistics; Thermodynamics","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0005245046,0.000137399,0.00009459411,0.00001585322,0.0007901913,0.0001667821,0.0002602921,0.00005801351,0.001300244],"category_scores_gemma":[0.00006974986,0.00009980943,0.00004587204,0.0002706565,0.0001868769,0.0001177443,0.0003386985,0.0001057978,0.001404265],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003073225,"about_ca_system_score_gemma":0.0001618895,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001754423,"about_ca_topic_score_gemma":0.0003137423,"domain_scores_codex":[0.9981136,0.00005175227,0.0002736861,0.0005158948,0.0006024512,0.000442582],"domain_scores_gemma":[0.9993216,0.00003890143,0.0000239887,0.0004333672,0.00003093956,0.0001512127],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004516461,0.0006727083,0.05234888,0.00002119135,0.0000853707,0.00007311533,0.006873153,0.02003504,0.1490983,0.004157783,0.5996368,0.1669525],"study_design_scores_gemma":[0.0003768124,0.000008459206,0.04990183,0.00001297114,0.00001575868,0.00001630101,0.0001027003,0.1490002,0.0894546,0.001950549,0.708717,0.000442802],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9474347,0.00009428103,0.03947886,0.0006120452,0.0008266963,0.0001834968,0.00000814723,0.00005492965,0.01130682],"genre_scores_gemma":[0.8845816,0.00002680264,0.02061208,0.0005498565,0.00004392703,0.00009283563,0.00005192092,0.00002174816,0.09401929],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1665097,"threshold_uncertainty_score":0.9996127,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01972494262406604,"score_gpt":0.2098574998369744,"score_spread":0.1901325572129084,"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."}}