{"id":"W4381186353","doi":"10.11159/cdsr23.207","title":"A Radial Basis Function Neural Network Approach to Filtering Stochastic Wind Speed Data","year":2023,"lang":"en","type":"article","venue":"Proceedings of the International Conference of Control, Dynamic systems, and Robotics","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Radial basis function; Computer science; Artificial neural network; Basis (linear algebra); Wind speed; Radial basis function network; Function (biology); Artificial intelligence; Mathematics; Meteorology; Physics","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":[0.0003218702,0.0001493712,0.000275488,0.0001107837,0.00005831904,0.00009141536,0.0006039614,0.00006195175,0.000002342425],"category_scores_gemma":[0.0001095886,0.0001221203,0.000042026,0.0001941435,0.00004349454,0.0002009289,0.0001963487,0.0001191443,0.000001189231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000290934,"about_ca_system_score_gemma":0.00001826588,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003055041,"about_ca_topic_score_gemma":0.0000054168,"domain_scores_codex":[0.9989179,0.000006341932,0.0003829811,0.0001950685,0.0002967653,0.0002008993],"domain_scores_gemma":[0.9993713,0.00007663308,0.0001506847,0.0001272828,0.0002182299,0.00005589112],"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":[0.00004507813,0.000007308777,0.0006818436,0.0001650794,0.0001404193,1.380427e-7,0.0001208706,0.9885328,0.004045134,0.005478246,0.0003729135,0.0004101553],"study_design_scores_gemma":[0.000370604,0.00002979512,0.001427382,0.0003181774,0.00005766714,0.00001328833,0.000205873,0.9972262,0.00001047957,0.0001603298,0.0000646669,0.0001155288],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8492938,0.0006169687,0.1230687,0.0007364515,0.01258591,0.001172603,0.000411208,0.0004230141,0.01169133],"genre_scores_gemma":[0.9990029,0.00001992274,0.0004340361,0.00001432737,0.0002776634,0.000003158591,0.0000287031,0.00002130767,0.0001979362],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1497091,"threshold_uncertainty_score":0.4979921,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03795606731835914,"score_gpt":0.2315893670222886,"score_spread":0.1936332997039295,"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."}}