{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007259394,0.0006577073,0.0003899816,0.0005936442,0.0001492233,0.0004566653,0.0003605919,0.0003644846,0.0005816012],"category_scores_gemma":[0.001638075,0.0001949549,0.0003745576,0.0006509254,0.00009668851,0.0005770805,0.0002645666,0.0003354257,0.0001741193],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003845523,"about_ca_system_score_gemma":0.0002303158,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007106824,"about_ca_topic_score_gemma":0.007551407,"domain_scores_codex":[0.9997916,0.00006540953,0.00001466169,0.00006251572,0.00004658107,0.00001921292],"domain_scores_gemma":[0.9995054,0.0002526375,0.00006605835,0.00002985222,0.0001368907,0.000009140003],"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":[0.00008375847,0.00005422536,0.01094188,0.00004449584,0.0000839878,0.00003290845,0.00002355102,0.8933561,0.003924964,0.0002069459,0.0003271917,0.09092004],"study_design_scores_gemma":[0.000002016975,0.00001164276,0.002438468,0.000002984907,0.000005618745,0.00000428275,0.000005270776,0.996469,0.0008575876,0.000123602,0.00007452687,0.00000495992],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6117646,0.0006143425,0.3824898,0.0001096769,0.00005705856,0.00004446608,0.0004188848,0.001203247,0.003298011],"genre_scores_gemma":[0.9554246,0.0001287865,0.04294192,0.00001815116,0.00001195519,0.00002778885,0.0003487857,0.00002512846,0.001072886],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007106824,"threshold_uncertainty_score":0.01413089,"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."}}