{"id":"W2547638683","doi":"10.1109/ccece.2016.7726682","title":"Frequency domain analysis for statistical assessment of wind resources","year":2016,"lang":"en","type":"article","venue":"","topic":"Wind Energy Research and Development","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Frequency domain; Spectral density; Wind speed; Probability density function; Logarithm; Mathematics; Probability distribution; White noise; Time–frequency analysis; Noise (video); Statistics; Computer science; Mathematical analysis; Meteorology; Physics; Telecommunications; Radar","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":[0.0001744692,0.00006226916,0.000143565,0.0001269469,0.00001841065,0.000008507252,0.00007786338,0.00002809046,0.0006870542],"category_scores_gemma":[0.00002207787,0.000037324,0.00004916132,0.0001678866,0.00002861051,0.00003602854,0.00001356599,0.00002187152,0.000004533466],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004825986,"about_ca_system_score_gemma":0.00002487576,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002223142,"about_ca_topic_score_gemma":0.00005136783,"domain_scores_codex":[0.9993631,0.00001230118,0.0001646779,0.00009380926,0.0001712565,0.0001948663],"domain_scores_gemma":[0.999578,0.0001749436,0.000009976869,0.0001162855,0.00003459493,0.00008622945],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00005028573,0.0002029933,0.3652179,0.0003173611,0.006422203,0.00002572599,0.0005093347,0.0118959,0.1191702,0.4378332,0.01841023,0.03994467],"study_design_scores_gemma":[0.001735022,0.0002168123,0.9235323,0.00003962074,0.0001506818,0.000001308022,0.0002020592,0.00813468,0.01069389,0.03921794,0.01557636,0.0004993019],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2193974,0.00001807305,0.7564797,0.0000889918,0.00002346271,0.00005945091,0.00005382425,0.00004338241,0.02383575],"genre_scores_gemma":[0.8402657,0.00001012039,0.1592899,0.000005719955,0.00001944528,0.00001426645,0.000008315537,0.000006984601,0.0003795354],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6208683,"threshold_uncertainty_score":0.7522762,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01215707400225166,"score_gpt":0.2740568156963154,"score_spread":0.2618997416940637,"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."}}