{"id":"W2808148181","doi":"10.1175/jtech-d-18-0001.1","title":"An Improved Estimation and Gap-Filling Technique for Sea Surface Wind Speeds Using NARX Neural Networks","year":2018,"lang":"en","type":"article","venue":"Journal of Atmospheric and Oceanic Technology","topic":"Ocean Waves and Remote Sensing","field":"Earth and Planetary Sciences","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Wind speed; Nonlinear autoregressive exogenous model; Artificial neural network; Autoregressive model; Computer science; Environmental science; Storm; Wind direction; Meteorology; Bay; Geology; Statistics; Mathematics; Artificial intelligence; Geography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001026032,0.0005874688,0.0006562062,0.0005071691,0.0002267178,0.0004538015,0.0006043556,0.0005675591,0.0008936678],"category_scores_gemma":[0.002325334,0.0003020586,0.0004219381,0.0004059566,0.0001752389,0.0007522843,0.0005221064,0.000836901,0.0002992855],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000204149,"about_ca_system_score_gemma":0.0004848648,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004557479,"about_ca_topic_score_gemma":0.003771746,"domain_scores_codex":[0.9997064,0.00007922655,0.00002563098,0.00008167398,0.00006926189,0.00003779664],"domain_scores_gemma":[0.9991856,0.0003708447,0.00008316054,0.000080053,0.000257491,0.00002284633],"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":[0.0005051838,0.0001491911,0.005101643,0.0001858389,0.0001054301,0.000218115,0.000284469,0.4423895,0.03257386,0.001676829,0.001287867,0.515522],"study_design_scores_gemma":[0.000005742286,0.00002190037,0.0007151177,0.000005338594,0.000005848366,0.00001046874,0.0000123157,0.9962958,0.00254297,0.0001573478,0.0002219068,0.000005327527],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1181266,0.0003021531,0.8796238,0.00008531167,0.00006258106,0.00003911616,0.00009982776,0.00108281,0.0005777874],"genre_scores_gemma":[0.6597344,0.0001242847,0.3381327,0.0000439004,0.00004249162,0.00006862612,0.0002956548,0.00007330294,0.001484681],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004557479,"threshold_uncertainty_score":0.009061933,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01116115379987893,"score_gpt":0.2369563319630497,"score_spread":0.2257951781631708,"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."}}