{"id":"W4400362847","doi":"10.5194/ems2024-1136","title":"Assessing heat stress with a mesoscale model. An application of WRF-comfort to Madrid","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Weather Research and Forecasting Model; Mesoscale meteorology; Heat stress; Meteorology; Environmental science; Extreme heat; Stress (linguistics); Climatology; Atmospheric sciences; Geography; Climate change; Geology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005996776,0.0009661971,0.0006256701,0.0004510403,0.0003148378,0.0008787551,0.001086443,0.001304799,0.001330213],"category_scores_gemma":[0.001221251,0.0002603574,0.0009100917,0.0006231263,0.000402165,0.0004452797,0.0006100101,0.0006521135,0.0001859155],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001256672,"about_ca_system_score_gemma":0.000614877,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04871489,"about_ca_topic_score_gemma":0.01930195,"domain_scores_codex":[0.9998782,0.00003534277,0.000006205373,0.00003212486,0.00002008703,0.00002802702],"domain_scores_gemma":[0.9996,0.0001811748,0.00004558616,0.00005237546,0.00006667004,0.00005425398],"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":[0.00006255392,0.00007973964,0.005865233,0.00002803351,0.00003071464,0.00008099827,0.00002632462,0.9902667,0.0007478678,0.0004519762,0.0003389379,0.002020909],"study_design_scores_gemma":[0.00005174619,0.00003734988,0.003567283,0.000004990348,0.000008918802,0.000008474341,0.00003714789,0.9951187,0.0003677873,0.0003128147,0.0004752634,0.000009466286],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9725311,0.0003671192,0.01498656,0.000322597,0.00009681612,0.00007360453,0.002502191,0.0005400206,0.008580019],"genre_scores_gemma":[0.993278,0.00008889422,0.004608591,0.00003011981,0.00001231231,0.00003589093,0.001062274,0.00006277406,0.0008211013],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04871489,"threshold_uncertainty_score":0.09686273,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01218012042953245,"score_gpt":0.2534120209185929,"score_spread":0.2412319004890605,"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."}}