{"id":"W2811455149","doi":"10.1109/lgrs.2018.2845698","title":"Wind Speed Estimation From X-Band Marine Radar Images Using Support Vector Regression Method","year":2018,"lang":"en","type":"article","venue":"IEEE Geoscience and Remote Sensing Letters","topic":"Ocean Waves and Remote Sensing","field":"Earth and Planetary Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; Defence Research and Development Canada","keywords":"Support vector machine; Wind speed; Radar; Histogram; Anemometer; Remote sensing; Computer science; Radar imaging; Synthetic aperture radar; Mean squared error; Artificial intelligence; Meteorology; Mathematics; Geology; Image (mathematics); Geography; Statistics","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":[0.0003560662,0.0005417063,0.0005810114,0.0004388365,0.0001258778,0.0003534361,0.0004498331,0.0004292318,0.0005180743],"category_scores_gemma":[0.001287148,0.000283027,0.0003955247,0.0005017134,0.0001142712,0.000534962,0.0002247299,0.0006622404,0.0003128913],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001031952,"about_ca_system_score_gemma":0.0002635412,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002442627,"about_ca_topic_score_gemma":0.001541923,"domain_scores_codex":[0.9997877,0.00004334548,0.00001976869,0.00005234424,0.00007652591,0.00002022233],"domain_scores_gemma":[0.9995635,0.0001406828,0.00008991828,0.00003402692,0.0001588249,0.00001294658],"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":[0.0001889052,0.00009444208,0.005882515,0.0001667922,0.0001059033,0.0001886785,0.00007602861,0.4749463,0.07110947,0.001304283,0.001433727,0.4445029],"study_design_scores_gemma":[0.000003814137,0.00002021922,0.0009397213,0.000003320955,0.000005273795,0.00002236107,0.000005473064,0.9951397,0.003550192,0.0001232956,0.0001804369,0.000006115451],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05222864,0.0002020925,0.9464384,0.00004518243,0.00002449419,0.00001662841,0.00005917967,0.0006237854,0.0003615172],"genre_scores_gemma":[0.6417906,0.0004154578,0.3556761,0.00004362546,0.00005005663,0.00006601756,0.0003683334,0.0000722003,0.00151751],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002442627,"threshold_uncertainty_score":0.004856765,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01735442525381397,"score_gpt":0.2566022976996353,"score_spread":0.2392478724458214,"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."}}