{"id":"W4226019854","doi":"10.23977/jemm.2022.070103","title":"Load Distribution Optimization Method for Fatigue Test of Full-scale Structure of Biaxial Resonant Wind Turbine Blade","year":2022,"lang":"en","type":"article","venue":"Journal of Engineering Mechanics and Machinery","topic":"Wind Energy Research and Development","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Structural engineering; Turbine blade; Turbine; Finite element method; Particle swarm optimization; Wind power; Position (finance); Exciter; Computer science; Control theory (sociology); Engineering; Mechanical engineering; Algorithm","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004154613,0.0001176573,0.0002679247,0.0001339448,0.00004119405,0.000009676842,0.0001105822,0.00004909135,0.00001960501],"category_scores_gemma":[0.0001464875,0.0001052516,0.00007613278,0.0001846029,0.000003553686,0.00006831339,0.00005307829,0.0002297452,1.13617e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009725636,"about_ca_system_score_gemma":0.00006342221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000583174,"about_ca_topic_score_gemma":0.000002564103,"domain_scores_codex":[0.9990271,0.00001569605,0.0004079938,0.0000772195,0.0003107134,0.0001612334],"domain_scores_gemma":[0.9994685,0.0001071726,0.0001264964,0.00007775313,0.0001355802,0.00008451367],"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":[0.00005341887,0.0000204929,0.000009542923,0.0001261608,0.00005004347,0.000002164554,0.00006655166,0.8025678,0.1951878,0.0003306009,0.0001747514,0.001410669],"study_design_scores_gemma":[0.0006579033,0.0003525732,0.0001304582,0.00004471416,0.00002854066,0.0000834099,0.00003679544,0.9784319,0.01881517,0.0002094453,0.00109807,0.0001109764],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06458068,0.000641809,0.9340068,0.00003693902,0.000343224,0.0000881434,0.0002848074,0.00001323637,0.000004418428],"genre_scores_gemma":[0.8949344,0.0001538148,0.1047081,0.000003807672,0.0001059909,0.000003063346,0.00005644794,0.00002541441,0.000008941121],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8303537,"threshold_uncertainty_score":0.4292037,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007449326014852081,"score_gpt":0.2237474029818257,"score_spread":0.2162980769669737,"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."}}