{"id":"W3158898378","doi":"10.1109/tgrs.2021.3074075","title":"Spatial–Temporal Convolutional Gated Recurrent Unit Network for Significant Wave Height Estimation From Shipborne Marine Radar Data","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Ocean Waves and Remote Sensing","field":"Earth and Planetary Sciences","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Convolutional neural network; Remote sensing; Computer science; Radar; Artificial intelligence; Deep learning; Radar imaging; Pattern recognition (psychology); Significant wave height; Wind wave; Geology; Telecommunications","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":[0.0004161064,0.0008579735,0.000497516,0.0004710242,0.0001661289,0.0003377008,0.0009170596,0.0004106353,0.0008530335],"category_scores_gemma":[0.001101972,0.000349972,0.0004972443,0.0006206195,0.0001760522,0.0005943176,0.0004566179,0.0006718417,0.0003286789],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004803968,"about_ca_system_score_gemma":0.0007113104,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02185022,"about_ca_topic_score_gemma":0.02475327,"domain_scores_codex":[0.9998388,0.00002325598,0.00001022204,0.00004674731,0.00004703871,0.00003383273],"domain_scores_gemma":[0.9997758,0.00007100872,0.00004015369,0.00003193306,0.00006842643,0.00001268783],"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":[0.0002619005,0.0001674979,0.003751228,0.00008591339,0.0001381829,0.0002053323,0.00005746978,0.6803015,0.0200912,0.001212655,0.002929947,0.2907971],"study_design_scores_gemma":[0.00000182871,0.00001332077,0.000394188,0.000001626352,0.00000694068,0.000006548688,0.000003183441,0.9979528,0.001354081,0.0001706188,0.00009172761,0.000003144727],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3235813,0.001705403,0.6654065,0.000358886,0.0001622595,0.00007340476,0.0009279262,0.004470538,0.003313868],"genre_scores_gemma":[0.9241275,0.0004228793,0.07112647,0.00009696416,0.00003915846,0.00005500887,0.001520562,0.0000751164,0.002536444],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02185022,"threshold_uncertainty_score":0.04344606,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04459770768000582,"score_gpt":0.243208463287992,"score_spread":0.1986107556079862,"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."}}