{"id":"W4226134322","doi":"10.1109/access.2022.3171230","title":"Optimized Feature Selection Based on a Least-Redundant and Highest-Relevant Framework for a Solar Irradiance Forecasting Model","year":2022,"lang":"en","type":"article","venue":"IEEE Access","topic":"Solar Radiation and Photovoltaics","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Feature selection; Random forest; Computer science; Univariate; Redundancy (engineering); Monotonic function; Solar irradiance; Feature (linguistics); Artificial intelligence; Irradiance; Random variable; Variable (mathematics); Machine learning; Statistics; Data mining; Mathematics; Multivariate statistics","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":[0.0003621538,0.0001745341,0.0001914195,0.000161318,0.0007375326,0.0004335044,0.0006936932,0.00008309479,0.00001036899],"category_scores_gemma":[0.000132058,0.0001792478,0.00007920255,0.000554219,0.00002048707,0.0004751322,0.0001039002,0.0004228393,0.000001216988],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001240832,"about_ca_system_score_gemma":0.0001782376,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002659845,"about_ca_topic_score_gemma":0.000007260949,"domain_scores_codex":[0.9986029,0.00008421419,0.0001714066,0.0005051455,0.0003159869,0.0003203784],"domain_scores_gemma":[0.9989444,0.0003814714,0.0001577766,0.0003191547,0.00007845643,0.0001187155],"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":[0.0002106703,0.00009902962,0.0002840211,0.00003954072,0.00001846368,0.000006364131,0.0005295536,0.9809021,0.0004702831,0.004965836,0.004887572,0.007586527],"study_design_scores_gemma":[0.0008434908,0.0001565136,0.0001627466,0.00002448435,0.00001048756,0.00001520291,0.000008930904,0.983725,0.001207359,0.01119536,0.002430981,0.0002194166],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01436079,0.00006490204,0.981644,0.002390671,0.0007304485,0.0005487346,0.00003677179,0.0001639932,0.00005970654],"genre_scores_gemma":[0.6912159,0.000006103951,0.3047168,0.003422482,0.0001192654,0.0003619176,0.000008409348,0.00002616371,0.0001229315],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6769272,"threshold_uncertainty_score":0.7309513,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05272181282411772,"score_gpt":0.2971710838893885,"score_spread":0.2444492710652708,"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."}}