{"id":"W4323923663","doi":"10.46660/ijeeg.v12i4.78","title":"Using XGBoost Model with Feature Selection Techniques for Wind Speed Forecasting","year":2023,"lang":"en","type":"article","venue":"International Journal of Economic and Environmental Geology","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Renewable energy; Wind power; Fossil fuel; Global warming; Government (linguistics); Environmental economics; Natural resource economics; Environmental science; Climate change; Business; Computer science; Engineering; Economics; Ecology; Waste management","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009841847,0.001015221,0.001385089,0.000991598,0.0005698826,0.0007145763,0.001188257,0.001151337,0.002484642],"category_scores_gemma":[0.001224447,0.0004163147,0.001240111,0.001320138,0.0002283973,0.000715855,0.0004056382,0.001124638,0.0008335214],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003883744,"about_ca_system_score_gemma":0.0008390905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01408721,"about_ca_topic_score_gemma":0.01056716,"domain_scores_codex":[0.9995973,0.00008851565,0.00003338799,0.00008946583,0.00009970417,0.00009169914],"domain_scores_gemma":[0.9996122,0.0001702075,0.00002955496,0.0000217338,0.0001476798,0.00001871184],"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":[0.0004575088,0.0004904484,0.004320266,0.0001629608,0.0002658131,0.0001561542,0.00005074248,0.5324832,0.003341174,0.0008701605,0.01050698,0.4468946],"study_design_scores_gemma":[0.00001425927,0.00004905386,0.00073613,0.000007706278,0.00001870381,0.00001284188,0.000007089085,0.9978855,0.0004946495,0.0002992165,0.0004691455,0.000005620264],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1327862,0.003139109,0.8531935,0.0007989845,0.0007704785,0.0002564321,0.0008329925,0.003943495,0.004278857],"genre_scores_gemma":[0.8370284,0.0008752501,0.148963,0.000431184,0.0003345496,0.0004272096,0.002183473,0.0001598003,0.009597111],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01408721,"threshold_uncertainty_score":0.02801043,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02208260773160919,"score_gpt":0.2278707277491553,"score_spread":0.2057881200175462,"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."}}