{"id":"W4412795561","doi":"10.1109/tgrs.2025.3594633","title":"Ocean Surface Wind Speed Estimation From GNSS-R Data Using Physics-Informed Attention-Aided Convolutional Neural Network","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"GNSS applications; Convolutional neural network; Wind speed; Remote sensing; Computer science; Artificial neural network; Estimation; Meteorology; Artificial intelligence; Geodesy; Environmental science; Global Positioning System; Geology; Physics; Telecommunications; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002617167,0.0002444779,0.0002287596,0.00004655843,0.001153751,0.0001540853,0.0002263104,0.000118936,0.00001278762],"category_scores_gemma":[0.00001810858,0.0002252294,0.00007155154,0.0007555299,0.0005713077,0.0006976231,0.00002617123,0.0002917016,0.00002449323],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001835361,"about_ca_system_score_gemma":0.00008365368,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005052163,"about_ca_topic_score_gemma":0.0005506145,"domain_scores_codex":[0.9980534,0.00007496563,0.0003307277,0.0006854187,0.0004166591,0.0004388814],"domain_scores_gemma":[0.9990141,0.0001978259,0.0001229446,0.0005230386,0.00002726983,0.0001148314],"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":[0.00003139475,0.0000231448,0.0001706818,0.000007221363,0.00002374632,0.000007352884,0.0001360263,0.7170792,0.007868524,0.000001737518,0.000170573,0.2744804],"study_design_scores_gemma":[0.0003727826,0.00001936816,0.009828421,0.0001722036,0.00008965287,0.00002977731,0.000143578,0.9871218,0.001173106,0.0007043505,0.0001038776,0.0002410528],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5991279,0.00002501939,0.398526,0.0002749572,0.001279588,0.0001596867,0.00001172958,0.00005776796,0.0005372936],"genre_scores_gemma":[0.9298682,0.0000314648,0.0690923,0.0004359026,0.0001000107,7.929838e-10,0.00002697539,0.00001350239,0.0004316468],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3307402,"threshold_uncertainty_score":0.9184589,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0273235375392017,"score_gpt":0.271870885974362,"score_spread":0.2445473484351603,"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."}}