{"id":"W2909476721","doi":"10.1109/oceans.2018.8604842","title":"Gaussian Process Regression for Estimating Wind Speed From X-band Marine Radar Images","year":2018,"lang":"en","type":"article","venue":"","topic":"Ocean Waves and Remote Sensing","field":"Earth and Planetary Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Radar; Wind speed; Anemometer; Histogram; Remote sensing; Bin; Radar imaging; Wind direction; Kriging; Computer science; Meteorology; Geology; Artificial intelligence; Mathematics; Algorithm; Statistics; Geography; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":false,"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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001255834,0.0001312995,0.0001530002,0.00003959428,0.0002968737,0.0001005541,0.0001074145,0.00007962516,0.001852224],"category_scores_gemma":[0.00004683591,0.00008062206,0.00004115528,0.00009337402,0.0001052954,0.0001773659,0.000008007995,0.00009119981,0.00007515099],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000001831809,"about_ca_system_score_gemma":0.00002652915,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001484322,"about_ca_topic_score_gemma":0.0003738678,"domain_scores_codex":[0.999131,0.00001724427,0.0001709772,0.000267128,0.0001510435,0.0002625633],"domain_scores_gemma":[0.9995291,0.00009716802,0.00008016647,0.0001341697,0.00005290419,0.000106511],"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":[0.0003745828,0.00003574032,0.07283036,0.0001315717,0.00006233075,0.00003488659,0.001148889,0.0008471345,0.002735596,0.000008969122,0.008327336,0.9134626],"study_design_scores_gemma":[0.00118461,0.0004316697,0.1418919,0.0002511668,0.00004768792,0.00003085336,0.0007121324,0.8134147,0.03233268,0.006699004,0.00247599,0.0005276286],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9451283,0.00005674531,0.0007800558,0.0005997176,0.0009072326,0.0002213947,0.00005262114,0.00006955006,0.05218437],"genre_scores_gemma":[0.8480242,0.000003662656,0.1479444,0.0001998671,0.001213895,1.53025e-9,0.0001802146,0.000006463755,0.002427361],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.912935,"threshold_uncertainty_score":0.9990602,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01420174879582095,"score_gpt":0.2514632273575292,"score_spread":0.2372614785617083,"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."}}