{"id":"W2990334548","doi":"10.1016/j.rser.2019.109570","title":"Exploring solar and wind energy resources in North Korea with COMS MI geostationary satellite data coupled with numerical weather prediction reanalysis variables","year":2019,"lang":"en","type":"article","venue":"Renewable and Sustainable Energy Reviews","topic":"Solar Radiation and Photovoltaics","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Korea Aerospace Research Institute; Korea Meteorological Administration","keywords":"Environmental science; Meteorology; Numerical weather prediction; Geostationary orbit; Solar irradiance; Geostationary Operational Environmental Satellite; Satellite; Renewable energy; Mean squared error; Solar energy; Data assimilation; Atmospheric sciences; Statistics; Geography; Mathematics; Engineering; 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":[0.0007137817,0.0002552583,0.0005007767,0.0002424086,0.000206028,0.0002515087,0.0004165577,0.00006200492,0.00001566827],"category_scores_gemma":[0.00003554641,0.0001888038,0.00002832126,0.001281237,0.00004826741,0.001607159,0.0002655245,0.0001017713,0.000001155016],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006072294,"about_ca_system_score_gemma":0.0001266065,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01077314,"about_ca_topic_score_gemma":0.00204518,"domain_scores_codex":[0.9979061,0.0002220464,0.0004025229,0.0007328149,0.0002950205,0.0004414506],"domain_scores_gemma":[0.9984384,0.0001409219,0.0002008431,0.0009539728,0.0001091254,0.0001567062],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008056245,0.0006215782,0.637843,0.00176994,0.0007712325,0.0005350502,0.007717994,0.1496265,0.0002719598,0.03046587,0.001512806,0.1680584],"study_design_scores_gemma":[0.0008987439,0.0002677349,0.007472834,0.0001555762,0.00007389552,0.00004669366,0.0009375989,0.2341845,0.00005393786,0.0003085418,0.7552043,0.0003956222],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3972264,0.1028091,0.4929169,0.000673914,0.0001670776,0.001209571,0.0000394875,0.0002912061,0.004666349],"genre_scores_gemma":[0.7461314,0.2215232,0.0176872,0.001187347,0.0001961912,0.000248824,0.000830351,0.00010415,0.01209131],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7536915,"threshold_uncertainty_score":0.9958142,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02705645746541777,"score_gpt":0.2112372043967151,"score_spread":0.1841807469312974,"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."}}