{"id":"W4388522033","doi":"10.1109/aeege58828.2023.00019","title":"Novel interpretable wake model using Machine Learning based symbolic regression for wind turbines","year":2023,"lang":"en","type":"article","venue":"","topic":"Wind Energy Research and Development","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Wake; Wind power; Turbine; Computer science; Aerodynamics; Computational fluid dynamics; Marine engineering; Simulation; Engineering; Aerospace 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000189159,0.0001509467,0.0001588372,0.0002059259,0.0001209018,0.00004686362,0.0001145547,0.00006575787,0.00005373301],"category_scores_gemma":[0.00005213634,0.0001195238,0.00005813797,0.0002835308,0.00001127901,0.0001129958,0.00005692721,0.000140678,0.00002116296],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006132665,"about_ca_system_score_gemma":0.00005044573,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004192908,"about_ca_topic_score_gemma":0.00001771905,"domain_scores_codex":[0.9990566,0.000007270367,0.0001607929,0.000169893,0.0001745515,0.0004309309],"domain_scores_gemma":[0.9996459,0.00006268841,0.00001287378,0.0001239354,0.00004159013,0.0001130244],"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":[0.00001655201,0.000007926456,0.0001773394,0.00006375671,0.00001860532,0.000001534199,0.00008368283,0.925652,0.07202663,0.00002851372,0.0008527103,0.001070749],"study_design_scores_gemma":[0.0004329633,0.00001430745,0.00009414084,0.00008118166,0.000003951184,0.000001760033,0.00002894806,0.9760996,0.0202943,0.00004893209,0.002738676,0.0001612722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4772567,0.000145262,0.5152975,0.0001759633,0.0002742355,0.000260413,0.00002241582,0.001305711,0.005261767],"genre_scores_gemma":[0.9614325,0.00002134031,0.0316603,0.00005763438,0.00005929515,0.0000273244,0.00009671941,0.00006659651,0.006578249],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4841758,"threshold_uncertainty_score":0.4874038,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04173269955262452,"score_gpt":0.2738426686134772,"score_spread":0.2321099690608527,"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."}}