{"id":"W2295028650","doi":"10.1007/s11768-015-4152-0","title":"Gain-scheduling control of a floating offshore wind turbine above rated wind speed","year":2015,"lang":"en","type":"article","venue":"Control Theory and Technology","topic":"Wave and Wind Energy Systems","field":"Engineering","cited_by":61,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"National Renewable Energy Laboratory","keywords":"Turbine; Control theory (sociology); Offshore wind power; Gain scheduling; BARGE; Engineering; Wind power; Wind speed; Scheduling (production processes); Controller (irrigation); Control system; Control engineering; Computer science; Marine engineering; Control (management); Aerospace engineering; Meteorology; Physics","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":[0.0007960792,0.0002257979,0.0005591813,0.0002558107,0.0000482666,0.00001564023,0.0001762687,0.000353269,0.0000144322],"category_scores_gemma":[0.0003026899,0.0002021617,0.00005129313,0.0003106637,0.0001896776,0.00008342733,0.0000260192,0.0002753903,0.00001089236],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002694897,"about_ca_system_score_gemma":0.00002900102,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006544598,"about_ca_topic_score_gemma":0.000002810638,"domain_scores_codex":[0.9988421,0.0001050794,0.0003968021,0.0002130827,0.0001015902,0.0003413954],"domain_scores_gemma":[0.9991764,0.0002018147,0.00009496223,0.0002911103,0.0001359845,0.00009976284],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009256989,0.00009671914,0.006827783,0.0002056303,0.001259331,0.0001247416,0.001097583,0.2178299,0.547625,0.2117145,0.0002498355,0.01204325],"study_design_scores_gemma":[0.04651948,0.001425403,0.0008390283,0.0007020015,0.000724863,0.0004495814,0.008799123,0.6155789,0.1756945,0.1347542,0.0123398,0.00217307],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9758264,0.00484994,0.01492003,0.0002336067,0.0004615412,0.000283957,0.00002323288,0.0004562258,0.002945074],"genre_scores_gemma":[0.999492,0.00001329691,0.000092621,0.0000639793,0.0001625678,0.000002356489,0.000005825426,0.00003625377,0.0001311302],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3977491,"threshold_uncertainty_score":0.8243914,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00797462861011863,"score_gpt":0.1999697751145308,"score_spread":0.1919951465044122,"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."}}