{"id":"W2548780149","doi":"10.1016/j.solener.2016.10.049","title":"Characterization of surface solar-irradiance variability using cloud properties based on satellite observations","year":2016,"lang":"en","type":"article","venue":"Solar Energy","topic":"Solar Radiation and Photovoltaics","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Core Research for Evolutional Science and Technology; Japan Science and Technology Agency; Goddard Space Flight Center; EMS Ingénierie","keywords":"Irradiance; Solar irradiance; Satellite; Environmental science; Remote sensing; Cloud computing; Characterization (materials science); Meteorology; Atmospheric sciences; Computer science; Geology; Astronomy; Optics; Physics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.0002510496,0.000244048,0.0003836617,0.001192453,0.0001267235,0.0004970223,0.000210396,0.0002309808,0.0002219078],"category_scores_gemma":[0.0007663048,0.000102728,0.0002803505,0.00145869,0.0001246469,0.0004433583,0.0001708089,0.0001829419,0.00009804086],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001969939,"about_ca_system_score_gemma":0.0001551273,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003443838,"about_ca_topic_score_gemma":0.005249986,"domain_scores_codex":[0.9998577,0.00002282209,0.0000124806,0.00004167656,0.00004684951,0.00001851315],"domain_scores_gemma":[0.9995882,0.000163782,0.000113242,0.00005556757,0.0000559034,0.00002337505],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003534193,0.0002312496,0.6579241,0.0001467416,0.0002424258,0.0002108109,0.0001312801,0.07481128,0.1007235,0.0005146432,0.0005937998,0.1641168],"study_design_scores_gemma":[0.000009200634,0.00009445937,0.668021,0.000009854172,0.00003768746,0.0001124762,0.00007833011,0.3221152,0.008712539,0.0003436998,0.0004507238,0.00001477465],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.970909,0.0001610435,0.02738631,0.00002329634,0.000005875931,0.00001943048,0.0006604831,0.00009519009,0.0007393082],"genre_scores_gemma":[0.9932048,0.0000712807,0.005787345,0.000003144724,0.000008938261,0.000009387107,0.0008246585,0.000006722676,0.00008365225],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003443838,"threshold_uncertainty_score":0.00684756,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04183284770518991,"score_gpt":0.2192436372535624,"score_spread":0.1774107895483724,"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."}}