{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005191723,0.0002659221,0.0002874974,0.0001710791,0.0007457993,0.0002652582,0.0001374898,0.000101933,0.00008906063],"category_scores_gemma":[0.00005767846,0.0001967801,0.00006653593,0.00036298,0.000650533,0.000410716,0.00002353742,0.0001995473,0.00004136312],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001433941,"about_ca_system_score_gemma":0.00005617148,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01317809,"about_ca_topic_score_gemma":0.000223908,"domain_scores_codex":[0.9979595,0.0001385357,0.000301889,0.000638995,0.0004255511,0.0005355439],"domain_scores_gemma":[0.9991163,0.0001468123,0.0001886676,0.0002816173,0.00006142478,0.0002051508],"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":[0.00005467036,0.000004041884,0.0007305844,0.00001448919,0.00001524118,0.0001419632,0.0005510266,0.0009346292,0.277566,1.995817e-7,0.0005364307,0.7194507],"study_design_scores_gemma":[0.0003419987,0.0001042589,0.034831,0.0001707978,0.00005064107,0.000353625,0.000103669,0.9218704,0.0410377,0.0003413214,0.0004009338,0.0003936662],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9532723,0.00003795484,0.0422954,0.001294574,0.002324063,0.0001313997,0.0000199608,0.00005954713,0.000564814],"genre_scores_gemma":[0.4775128,0.00003011408,0.5193597,0.001931549,0.0009012409,8.077879e-11,0.00004183162,0.00001196706,0.0002107506],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9209358,"threshold_uncertainty_score":0.9933932,"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."}}