{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002254393,0.0006345474,0.0004290769,0.000852769,0.0001812243,0.0005506579,0.000215087,0.0003899318,0.0004632887],"category_scores_gemma":[0.007294124,0.0002066068,0.0004350232,0.0004263899,0.0003903122,0.0004375888,0.0004222279,0.0004993028,0.0001154316],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003577553,"about_ca_system_score_gemma":0.0002996232,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002676835,"about_ca_topic_score_gemma":0.001622712,"domain_scores_codex":[0.9995334,0.0001948795,0.00002761394,0.0000696946,0.0001195429,0.00005482691],"domain_scores_gemma":[0.994862,0.003575169,0.0005378618,0.0003256304,0.0005924526,0.0001068323],"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":[0.0004038663,0.000181947,0.025356,0.0001013696,0.00009912573,0.0001297672,0.0001110429,0.8991807,0.02113506,0.0009084542,0.0002528788,0.05213993],"study_design_scores_gemma":[0.000002712762,0.00009676564,0.007740262,0.000007759695,0.00001125416,0.00001310929,0.00002424159,0.9880376,0.003765565,0.0002348898,0.00005930218,0.000006618453],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8695025,0.0001326318,0.1290946,0.00005755692,0.00001306776,0.00003222087,0.00009676992,0.0001584818,0.0009121531],"genre_scores_gemma":[0.9940016,0.00002052531,0.005782437,0.00000491358,0.000001176534,0.00001022212,0.00007209479,0.00000637499,0.0001005453],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002676835,"threshold_uncertainty_score":0.01192254,"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."}}