{"id":"W4385724715","doi":"10.1177/0309524x231188696","title":"A radial basis function neural network approach to filtering stochastic wind speed data","year":2023,"lang":"en","type":"article","venue":"Wind Engineering","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Control theory (sociology); Wind speed; Filter (signal processing); SIGNAL (programming language); Noise (video); Artificial neural network; Smoothing; Computer science; Radial basis function network; Turbine; Wind power; Basis (linear algebra); Radial basis function; Control engineering; Engineering; Artificial intelligence; Mathematics; Control (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":[0.0007743621,0.0005670573,0.0005606036,0.0004660495,0.0002357379,0.0006371809,0.0008831144,0.0009808649,0.0009362686],"category_scores_gemma":[0.001329868,0.000228935,0.0004649781,0.00062221,0.0003385885,0.0007725224,0.000318735,0.0008796204,0.000432918],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004437511,"about_ca_system_score_gemma":0.0005073684,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004688102,"about_ca_topic_score_gemma":0.004233173,"domain_scores_codex":[0.9996263,0.0001140527,0.00002479303,0.00007271137,0.0001385852,0.00002358097],"domain_scores_gemma":[0.9997702,0.00008969901,0.00002184433,0.00001705613,0.00009464035,0.000006532806],"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":[0.0001016297,0.00007377764,0.0007030448,0.0001795388,0.0000912045,0.0001309811,0.0000812843,0.7234859,0.01111154,0.02993255,0.001685071,0.2324234],"study_design_scores_gemma":[0.000003463231,0.00002596071,0.0001174498,0.000007306748,0.000006682922,0.00002303165,0.000003765462,0.9952044,0.001046871,0.00230353,0.001249575,0.000007997251],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002351745,0.0004104314,0.9957851,0.0000824599,0.00004310797,0.00001569117,0.00001476831,0.0001635298,0.001133193],"genre_scores_gemma":[0.3268585,0.002454969,0.6605798,0.0001969227,0.0002824251,0.0001986063,0.0001699227,0.00008487554,0.009173865],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004688102,"threshold_uncertainty_score":0.00932163,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03161696006842215,"score_gpt":0.2066339126469683,"score_spread":0.1750169525785462,"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."}}