{"id":"W4389894955","doi":"10.5194/wes-2023-157","title":"Data-driven surrogate model for wind turbine damage equivalent load","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada","keywords":"Aeroelasticity; Turbine; Wind power; Wake; Computer science; Range (aeronautics); Surrogate model; Component (thermodynamics); Set (abstract data type); Engineering; Simulation; Marine engineering; Aerodynamics; Aerospace engineering; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003710488,0.0004366498,0.0004515311,0.0001129771,0.00006182264,0.0001117786,0.001059577,0.0003173893,0.00006165884],"category_scores_gemma":[0.00006580037,0.0004301787,0.0001538968,0.00008997077,0.00002632936,0.0001442007,0.001773276,0.0005164169,0.00007926219],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001345498,"about_ca_system_score_gemma":0.0001136607,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007349555,"about_ca_topic_score_gemma":0.0005214036,"domain_scores_codex":[0.9980685,0.00001240592,0.0004495786,0.0006514382,0.0002828477,0.0005352783],"domain_scores_gemma":[0.9981676,0.0001218602,0.00006739524,0.001423464,0.00008254862,0.0001370987],"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":[0.000006381107,0.00001036631,0.00001925827,0.0005994532,0.0001507049,0.00000849839,0.0001601658,0.9773999,0.0002049585,0.0003818999,0.02035858,0.0006998103],"study_design_scores_gemma":[0.0003042652,0.00001032251,0.00002103009,0.0002551933,0.00007163259,0.000001187623,0.00001334384,0.9905794,0.000225405,0.001269025,0.006769271,0.0004799524],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1005665,0.0009774998,0.8278221,0.0004929815,0.009500845,0.001701745,0.0129815,0.007100433,0.03885635],"genre_scores_gemma":[0.8802425,0.0007018216,0.06342507,0.0001159144,0.001688877,0.0001603868,0.01279028,0.0006733992,0.04020176],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.779676,"threshold_uncertainty_score":0.999815,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1252322605216672,"score_gpt":0.3010136671974522,"score_spread":0.1757814066757849,"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."}}