{"id":"W3202810764","doi":"10.1109/icjece.2021.3091832","title":"Gaussian Mixture Model for the Estimation of Multiyear Solar Irradiance Probability Density","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Solar Radiation and Photovoltaics","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Khalifa University of Science, Technology and Research","keywords":"Kernel density estimation; Density estimation; Solar irradiance; Probability density function; Parametric statistics; Computer science; Mixture model; Gaussian function; Statistics; Photovoltaic system; Goodness of fit; Nonparametric statistics; Multivariate kernel density estimation; Variable kernel density estimation; Mathematics; Gaussian; Kernel method; Support vector machine; Artificial intelligence; Engineering; Meteorology; Geography; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030337,0.001064001,0.00162264,0.001494168,0.0005445765,0.001172876,0.00247765,0.001665708,0.002706864],"category_scores_gemma":[0.007867854,0.0007218585,0.001690345,0.002327592,0.0006920897,0.001806281,0.001044951,0.002462773,0.001671155],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009841715,"about_ca_system_score_gemma":0.001387352,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02645264,"about_ca_topic_score_gemma":0.01627245,"domain_scores_codex":[0.998519,0.0005457252,0.00008315532,0.0003548148,0.0003666891,0.0001305329],"domain_scores_gemma":[0.9979913,0.001212263,0.0001841021,0.0001628276,0.0004171207,0.00003238594],"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":[0.0001674508,0.00008898741,0.004003414,0.0002208779,0.0001707101,0.000150404,0.0001898874,0.8114709,0.002090319,0.02947019,0.003885438,0.1480914],"study_design_scores_gemma":[0.000004178601,0.00001394631,0.0007663841,0.00001462481,0.00001437085,0.00003179687,0.00001494562,0.9931952,0.0003492759,0.004274199,0.001301536,0.0000195087],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004179853,0.0004029409,0.9942535,0.0000756049,0.00004422546,0.00003830896,0.0001306002,0.0004295207,0.0004454288],"genre_scores_gemma":[0.4110848,0.002450709,0.5730091,0.0002201868,0.0001967737,0.0006040983,0.002830129,0.00042533,0.009178914],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02645264,"threshold_uncertainty_score":0.05259734,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01140370428984868,"score_gpt":0.1952812977697228,"score_spread":0.1838775934798741,"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."}}