{"id":"W2293055932","doi":"10.3390/rs8030222","title":"GLASS Daytime All-Wave Net Radiation Product: Algorithm Development and Preliminary Validation","year":2016,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Atmospheric aerosols and clouds","field":"Environmental Science","cited_by":61,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Lawrence Berkeley National Laboratory; Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences; Natural Sciences and Engineering Research Council of Canada; Center for Neuroscience and Regenerative Medicine; University of Virginia; Natural Resources Canada; Università degli Studi della Tuscia; Université Laval; Oak Ridge National Laboratory; Biological and Environmental Research; National Natural Science Foundation of China; Canadian Foundation for Climate and Atmospheric Sciences; Microsoft Research; U.S. Department of Energy; National Science Foundation","keywords":"Algorithm; Satellite; Environmental science; Remote sensing; Root mean square; Daytime; Product (mathematics); Meteorology; Mean squared error; Computer science; Mathematics; Statistics; Atmospheric sciences; Physics; Geology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001872645,0.0001166488,0.00009303154,0.000001842807,0.0001194784,0.0000248435,0.00003511924,0.00004498602,0.00007028168],"category_scores_gemma":[0.00002750931,0.00008672034,0.00001554905,0.00007188824,0.00006753259,0.0001655618,0.00009579123,0.00004208127,0.0001296972],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001807015,"about_ca_system_score_gemma":0.00001342122,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007462783,"about_ca_topic_score_gemma":0.000003847071,"domain_scores_codex":[0.9990686,0.00003741304,0.0001633877,0.0003146631,0.000204882,0.0002111148],"domain_scores_gemma":[0.9996538,0.00002816312,0.00008133933,0.0001537759,0.000007160571,0.00007580588],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000006163247,0.000005295197,0.0001978311,0.000002201699,0.000006460712,0.000009787606,0.0003879747,0.00004483989,0.01583079,0.000001112292,0.0002775185,0.98323],"study_design_scores_gemma":[0.001730894,0.0002814598,0.1288713,0.00023887,0.00009251486,0.0005429617,0.0002364487,0.4396499,0.2829978,0.0008491462,0.1431522,0.00135649],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8887302,0.00005826701,0.1088922,0.0007286766,0.000121232,0.0001800866,7.793733e-7,0.0000499254,0.001238617],"genre_scores_gemma":[0.7700734,0.00004493977,0.2278841,0.0001480677,0.0001121276,4.877202e-8,0.000007624222,0.00001904474,0.001710721],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9818735,"threshold_uncertainty_score":0.3536353,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01348713764059516,"score_gpt":0.2121853575846337,"score_spread":0.1986982199440385,"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."}}