{"id":"W4382134272","doi":"10.1016/j.enconman.2023.117324","title":"Comparative study of feature selection methods for wind speed estimation at ungauged locations","year":2023,"lang":"en","type":"article","venue":"Energy Conversion and Management","topic":"Wind Energy Research and Development","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Japan Aerospace Exploration Agency; Environment and Climate Change Canada; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Overfitting; Wind speed; Feature selection; Lasso (programming language); Kriging; Elastic net regularization; Regression; Computer science; Statistics; Mathematics; Artificial intelligence; Artificial neural network; Meteorology; Geography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002510878,0.0007610578,0.0008205128,0.001606548,0.0003718819,0.0008491821,0.0005526493,0.000495497,0.0008027029],"category_scores_gemma":[0.005807981,0.0001718814,0.0007388995,0.001365209,0.0001815385,0.0008305233,0.000380683,0.000366599,0.0002373726],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002420514,"about_ca_system_score_gemma":0.0004579319,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007377249,"about_ca_topic_score_gemma":0.006111145,"domain_scores_codex":[0.9992146,0.0002836027,0.00008042631,0.0001128115,0.0002177489,0.00009076269],"domain_scores_gemma":[0.9947522,0.003836135,0.0002031703,0.0001786361,0.0009504839,0.00007942844],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003134656,0.0006363072,0.07193059,0.0005310292,0.0006965511,0.0002593361,0.0003143662,0.1549869,0.01475645,0.0007200533,0.002436109,0.7495977],"study_design_scores_gemma":[0.00007957225,0.0006806558,0.09201869,0.00004065421,0.0002604323,0.0001746062,0.0003732471,0.8962401,0.008681056,0.0002981077,0.001110916,0.00004203709],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8444002,0.002546675,0.1499277,0.000208666,0.0001322527,0.00006571454,0.0004796384,0.0005210974,0.001718147],"genre_scores_gemma":[0.9678327,0.000482653,0.02987711,0.00002635295,0.00004792243,0.00003078399,0.000844734,0.0000398016,0.0008179123],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007377249,"threshold_uncertainty_score":0.01466858,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02760528094495658,"score_gpt":0.3215391761485029,"score_spread":0.2939338952035463,"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."}}