{"id":"W2022625409","doi":"10.3390/rs61111031","title":"Surface Daytime Net Radiation Estimation Using Artificial Neural Networks","year":2014,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Climate variability and models","field":"Environmental Science","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Lawrence Berkeley National Laboratory; Fundamental Research Funds for the Central Universities; Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences; Natural Sciences and Engineering Research Council of Canada; Natural Resources Canada; Deutsche Forschungsgemeinschaft; Université Laval; National Science Foundation; University of Washington; Oak Ridge National Laboratory; Biological and Environmental Research; National Natural Science Foundation of China; Canadian Foundation for Climate and Atmospheric Sciences; Microsoft Research; Center for Neuroscience and Regenerative Medicine; U.S. Department of Energy","keywords":"Artificial neural network; Mean squared error; Mode (computer interface); Empirical modelling; Computer science; Regression; Environmental science; Data mining; Statistics; Remote sensing; Machine learning; Mathematics; Geology; Simulation","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":[0.0004923383,0.0001094115,0.0001205765,0.00001535585,0.0002004723,0.0000627372,0.000052783,0.00008395379,0.00006532109],"category_scores_gemma":[0.00008684382,0.0001160647,0.00003975095,0.0001572483,0.00006562504,0.0002140765,0.00006182877,0.0001105005,0.00005905309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001578768,"about_ca_system_score_gemma":0.000004159031,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007315705,"about_ca_topic_score_gemma":0.00004447891,"domain_scores_codex":[0.998975,0.0001316922,0.0002098346,0.0002575408,0.0001703045,0.0002556033],"domain_scores_gemma":[0.9995263,0.00009509631,0.00008822435,0.0002153143,0.000006648312,0.00006836516],"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":[0.00000605765,0.000004198878,0.00005159842,0.000001938977,0.000001227323,6.115671e-7,0.00007033617,0.8207665,0.01160498,0.00002337036,0.00001487612,0.1674543],"study_design_scores_gemma":[0.00008178129,0.00001335564,0.0003162669,0.00001004957,0.00001465575,0.00001272553,0.000006797307,0.9967995,0.0005460532,0.001949225,0.0001193832,0.00013017],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5724639,0.000003369051,0.42655,0.0001119139,0.0001571344,0.00007367967,3.630711e-7,0.00004122584,0.0005983712],"genre_scores_gemma":[0.9555047,0.00000194422,0.04417775,0.0001563479,0.0001184137,2.34221e-9,0.0000135811,0.00001397754,0.00001332546],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3830408,"threshold_uncertainty_score":0.4732981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02638100219574089,"score_gpt":0.2535398761160861,"score_spread":0.2271588739203453,"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."}}