{"id":"W2322769727","doi":"10.1002/we.1934","title":"A stochastic power curve for wind turbines with reduced variability using conditional copula","year":2015,"lang":"en","type":"article","venue":"Wind Energy","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Turbine; Wind power; Wind speed; Copula (linguistics); Meteorology; Wind power forecasting; Wind profile power law; Environmental science; Econometrics; Statistics; Power (physics); Engineering; Mathematics; Electric power system; Geography; Physics; Aerospace engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001747389,0.0002170264,0.0002248415,0.00007142031,0.00007596981,0.00003692371,0.0001070555,0.0001060595,0.00004249339],"category_scores_gemma":[0.00008262358,0.0001931257,0.00005545275,0.0001621579,0.00005866342,0.0002007591,0.00002058875,0.00009022124,0.000002313407],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001057723,"about_ca_system_score_gemma":0.0000762474,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009851324,"about_ca_topic_score_gemma":0.00001988354,"domain_scores_codex":[0.9989999,0.00002799299,0.0002164006,0.0002353326,0.0001871968,0.0003332076],"domain_scores_gemma":[0.9993398,0.0001134755,0.00004327667,0.0001980289,0.0001280884,0.0001773584],"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":[0.00005722156,0.00002589745,0.00008367547,0.0000186518,0.00006816081,0.000004501147,0.0002367081,0.9923199,0.001764563,0.004688515,0.0005992366,0.0001329119],"study_design_scores_gemma":[0.003893587,0.0004650693,0.000667053,0.0002784481,0.000154404,0.0002570622,0.0002804215,0.9489349,0.006496425,0.01217439,0.02495528,0.001442954],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8026457,0.000137802,0.1923148,0.0000407917,0.0009502783,0.0001146684,0.00009353478,0.0002477114,0.003454678],"genre_scores_gemma":[0.9959009,4.076152e-7,0.003320833,0.0000700571,0.0003791976,0.00001178487,0.0001388339,0.00005557535,0.000122416],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1932551,"threshold_uncertainty_score":0.7875438,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02534918813565464,"score_gpt":0.2327827579722705,"score_spread":0.2074335698366158,"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."}}