{"id":"W2569183511","doi":"10.1175/waf-d-16-0137.1","title":"Calibrated Probabilistic Hub-Height Wind Forecasts in Complex Terrain","year":2017,"lang":"en","type":"article","venue":"Weather and Forecasting","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; BC Hydro","keywords":"Probabilistic logic; Terrain; Ensemble forecasting; Quantitative precipitation forecast; Gaussian; Meteorology; Probabilistic forecasting; Variance (accounting); Wind speed; Environmental science; Computer science; Statistics; Mathematics; Precipitation; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00209467,0.0003960418,0.0007082836,0.0005583165,0.0002322338,0.0004796583,0.0005990472,0.0004296758,0.0004767367],"category_scores_gemma":[0.004996686,0.0002212594,0.0003432556,0.000547556,0.0002629744,0.001161767,0.0004161774,0.0006051579,0.0001048923],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005084852,"about_ca_system_score_gemma":0.0004833489,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01151079,"about_ca_topic_score_gemma":0.006850286,"domain_scores_codex":[0.9995248,0.0001636567,0.00002264062,0.00008904057,0.0001476,0.00005223734],"domain_scores_gemma":[0.9979796,0.001000735,0.0001761954,0.0003206409,0.0004349645,0.00008791385],"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":[0.00003179912,0.00001514544,0.002198516,0.000004421669,0.00001720811,0.00001072478,0.000006624247,0.9920188,0.0004434213,0.0001450892,0.00007162393,0.005036565],"study_design_scores_gemma":[0.000004501035,0.00001438485,0.001707124,8.689739e-7,0.000002687988,0.000002610065,0.000002235485,0.9977356,0.0003393607,0.0001529157,0.00003426309,0.000003431746],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9220743,0.00005904416,0.07563201,0.00004936515,0.00002729566,0.00003092717,0.0003341431,0.0003344409,0.001458557],"genre_scores_gemma":[0.9956831,0.00001382778,0.004049811,0.000003474005,0.000004714092,0.00000539427,0.0001496857,0.000007810276,0.0000820961],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01151079,"threshold_uncertainty_score":0.02288759,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09119212875198267,"score_gpt":0.2582541082663095,"score_spread":0.1670619795143268,"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."}}