{"id":"W4213044503","doi":"10.1002/we.2722","title":"Analysis of leading edge protection application on wind turbine performance through energy and power decomposition approaches","year":2022,"lang":"en","type":"article","venue":"Wind Energy","topic":"Wind Energy Research and Development","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wind Energy Institute of Canada","funders":"National Renewable Energy Laboratory; Office of Energy Efficiency; Direktorat Jenderal Pendidikan Tinggi; Office of Energy Efficiency and Renewable Energy; U.S. Department of Energy; Wind Energy Technologies Office; National Science Foundation","keywords":"Wind power; Turbine; Renewable energy; Robustness (evolution); Reliability engineering; Decomposition; Enhanced Data Rates for GSM Evolution; Engineering; Computer science; Electrical engineering; Aerospace engineering; Telecommunications","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":[0.0001260373,0.0001423047,0.0002184373,0.0003465712,0.0001814217,0.00001774926,0.0001035217,0.00005207468,0.00005609568],"category_scores_gemma":[0.000002733443,0.000146022,0.00005786747,0.0008386414,0.00002866538,0.0001356468,0.00006157341,0.0001050207,7.46714e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001485707,"about_ca_system_score_gemma":0.00001637102,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000140443,"about_ca_topic_score_gemma":0.00002327892,"domain_scores_codex":[0.9990034,0.00003997901,0.0002104752,0.0002384478,0.0002831625,0.0002245797],"domain_scores_gemma":[0.9996669,0.0000184886,0.00004616456,0.0001902265,0.00002181876,0.00005634593],"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":[0.00005976226,0.00005854557,0.0002883503,0.00002363094,0.0003291683,0.000001054869,0.0003351078,0.9631408,0.006162851,0.002176488,0.0001079594,0.02731621],"study_design_scores_gemma":[0.0005854921,0.0004023254,0.01296383,0.0000215703,0.0001283444,0.00001432832,0.0002725028,0.8646171,0.06229241,0.0002590333,0.05795872,0.0004843472],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9645263,0.0002758529,0.02485395,0.00007088269,0.0000960313,0.000073845,0.00001242475,0.00009980783,0.009990893],"genre_scores_gemma":[0.9991623,0.00009406272,0.0001869802,0.00004638611,0.00004652579,0.0001023286,0.0001435814,0.00002235394,0.0001955296],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09852377,"threshold_uncertainty_score":0.5954606,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01949581925826014,"score_gpt":0.2178850529134134,"score_spread":0.1983892336551532,"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."}}