{"id":"W4388845584","doi":"10.1016/j.enbuild.2023.113781","title":"Defining generation parameters with an adaptable data-driven approach to construct typical meteorological year weather files","year":2023,"lang":"en","type":"article","venue":"Energy and Buildings","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Building energy simulation; Weighting; Construct (python library); Representativeness heuristic; Data file; Weather Research and Forecasting Model; Weather station; Extreme weather; Model output statistics; Meteorology; Computer science; Database; Engineering; Efficient energy use; Climate change; Energy performance; Geography","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":[],"consensus_categories":[],"category_scores_codex":[0.0001103538,0.0001343852,0.0001428859,0.00009105323,0.0001057036,0.0000691544,0.0001616484,0.0001044038,0.0000176517],"category_scores_gemma":[0.00001233809,0.000110462,0.00001346659,0.0002759675,0.00004036561,0.0002336021,0.000070984,0.00007662772,0.000002371408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001168015,"about_ca_system_score_gemma":0.000009194481,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003038252,"about_ca_topic_score_gemma":0.00002228438,"domain_scores_codex":[0.9992399,0.00002357173,0.0001134024,0.000303781,0.0001058983,0.0002134215],"domain_scores_gemma":[0.9996123,0.00002859971,0.00001715191,0.0002340175,0.00001442531,0.00009348083],"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":[0.0000295895,0.00001033698,0.0002059272,0.000004419821,0.00003272894,0.000002057039,0.00005138727,0.9468325,0.0004797424,0.04618091,0.001627189,0.00454326],"study_design_scores_gemma":[0.000206735,0.00009963175,0.0001815463,0.0000109179,0.00002170291,0.00001544249,0.00006860544,0.9914045,0.000902941,0.0001833042,0.006687277,0.0002173489],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.840374,0.00009577244,0.157466,0.00003729782,0.0001102188,0.00005108581,0.00002020541,0.0005254027,0.001320085],"genre_scores_gemma":[0.8385944,0.00006314083,0.1606792,0.00009726138,0.00006538013,0.0000242999,0.000362138,0.00002752469,0.00008661037],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0459976,"threshold_uncertainty_score":0.4504508,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02717582222101982,"score_gpt":0.2160451576117089,"score_spread":0.1888693353906891,"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."}}