{"id":"W3096930149","doi":"10.1002/we.2588","title":"A simple method for modelling fatigue spectra of small wind turbine blades","year":2020,"lang":"en","type":"article","venue":"Wind Energy","topic":"Wind Energy Research and Development","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Office of Energy Efficiency; Wind Energy Technologies Office; Office of Energy Efficiency and Renewable Energy; U.S. Department of Energy","keywords":"Aeroelasticity; Wind power; Turbine; Turbine blade; Rotor (electric); Structural engineering; Small wind turbine; Engineering; Computer science; Aerospace engineering; Mechanical engineering; Aerodynamics; Electrical engineering","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":[0.0002552356,0.0005329748,0.000276632,0.0004346817,0.0002410659,0.0003664313,0.0007221805,0.0008976604,0.002901015],"category_scores_gemma":[0.001034183,0.0002945227,0.0004635663,0.0002417746,0.0002055847,0.0004860228,0.0002491211,0.0004262093,0.0007728743],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000212919,"about_ca_system_score_gemma":0.0003793933,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002614446,"about_ca_topic_score_gemma":0.00290302,"domain_scores_codex":[0.9998965,0.00001984283,0.000006464105,0.00001644288,0.00005343236,0.000007372516],"domain_scores_gemma":[0.9995989,0.0001719696,0.00004017592,0.00005280149,0.0001197195,0.00001643122],"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":[0.00005961032,0.00008604056,0.001785437,0.000168173,0.00003197534,0.0001968705,0.0001609813,0.8344789,0.0688343,0.004852308,0.001612019,0.08773337],"study_design_scores_gemma":[0.000005601205,0.00001713835,0.0003669768,0.000004649097,0.00000261241,0.00003835622,0.000005888729,0.9957923,0.002184986,0.0004362849,0.001137779,0.000007505211],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03211995,0.00006868906,0.9637067,0.00003715359,0.00002977609,0.00006452729,0.0001714279,0.0008141396,0.002987634],"genre_scores_gemma":[0.5545574,0.0001520811,0.4381255,0.00005582217,0.00003488089,0.0003212168,0.0003317287,0.0004368302,0.0059845],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002901015,"threshold_uncertainty_score":0.009704888,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05432095687486933,"score_gpt":0.2623454175359248,"score_spread":0.2080244606610555,"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."}}