{"id":"W4396513748","doi":"10.1007/s40032-024-01040-4","title":"Improvements in Fitting Accuracy of Weibull Distribution for Wind Data by Capturing Monthly and Diurnal Variability in Wind Speeds","year":2024,"lang":"en","type":"article","venue":"Journal of The Institution of Engineers (India) Series C","topic":"Wind Energy Research and Development","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Weibull distribution; Environmental science; Wind speed; Meteorology; Statistics; Distribution (mathematics); Atmospheric sciences; Climatology; Mathematics; Geography; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001338369,0.0001240823,0.0002414517,0.0001578648,0.00003404884,0.00003543906,0.0002932993,0.00007524061,0.000003690949],"category_scores_gemma":[0.0007116343,0.00009880794,0.00005359543,0.0002883269,0.00007297514,0.0007604688,0.0001042157,0.0003123797,1.073789e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000236282,"about_ca_system_score_gemma":0.0001621425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003761742,"about_ca_topic_score_gemma":0.00001106167,"domain_scores_codex":[0.9987357,0.00003104524,0.000639451,0.0001153075,0.0002627604,0.0002157386],"domain_scores_gemma":[0.9994166,0.0001601873,0.0001167529,0.0001780763,0.00006343184,0.00006494219],"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":[0.0006051802,0.0002634119,0.02171331,0.003652269,0.0006285828,0.0000556056,0.002546822,0.8712535,0.07681195,0.002952323,0.003044656,0.01647238],"study_design_scores_gemma":[0.007377392,0.0007181975,0.3035772,0.006642994,0.0002022802,0.0001949962,0.001926508,0.3685004,0.2674301,0.002465395,0.03976997,0.001194555],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9929844,0.0008738223,0.004607157,0.0002013038,0.0006750498,0.0002041563,0.000321061,0.00001080913,0.000122201],"genre_scores_gemma":[0.9989636,0.0001634244,0.0007288616,0.000003511787,0.00007312272,0.000001852665,0.00004205382,0.000009882862,0.00001367593],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5027531,"threshold_uncertainty_score":0.4029271,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0117523842788924,"score_gpt":0.2383581147242907,"score_spread":0.2266057304453983,"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."}}