{"id":"W2521012645","doi":"10.1002/2016gl070799","title":"Radar imaging of intense nonlinear Ekman divergence","year":2016,"lang":"en","type":"article","venue":"Geophysical Research Letters","topic":"Ocean Waves and Remote Sensing","field":"Earth and Planetary Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bedford Institute of Oceanography; Fisheries and Oceans Canada; Dalhousie University","funders":"Canadian Space Agency; National Oceanic and Atmospheric Administration; National Natural Science Foundation of China; Nanjing University of Information Science and Technology; Government of Jiangsu Province; Natural Science Foundation of Jiangsu Province; Startup Foundation for Introducing Talent of Nanjing University of Information Science and Technology; Marine Environmental Observation Prediction and Response Network","keywords":"Geology; Front (military); Sea surface temperature; Synthetic aperture radar; Ekman transport; Ocean dynamics; Backscatter (email); Radar; Gulf Stream; Divergence (linguistics); Geophysics; Ocean current; Climatology; Oceanography; Remote sensing","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0001841665,0.0001799092,0.0001241027,0.0004528977,0.00006740667,0.0002498287,0.000104744,0.0002195806,0.0007505006],"category_scores_gemma":[0.000362911,0.0001076903,0.00007391917,0.0002462937,0.0001191518,0.0002303997,0.0002501035,0.0002170896,0.0001475275],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000717878,"about_ca_system_score_gemma":0.00006543493,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001998061,"about_ca_topic_score_gemma":0.0002582636,"domain_scores_codex":[0.9999485,0.00001057457,0.000003016641,0.00001206975,0.0000134355,0.00001240865],"domain_scores_gemma":[0.9998102,0.00005160175,0.00003910942,0.00002363223,0.00004785351,0.00002763336],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0004632185,0.00009633203,0.0444731,0.0001503309,0.00007858725,0.00151842,0.0002946186,0.004658226,0.8832645,0.002003436,0.00115145,0.06184785],"study_design_scores_gemma":[0.0001618989,0.000804722,0.4518973,0.00008725745,0.0002459833,0.008889241,0.0005710548,0.2251138,0.3004418,0.004240416,0.007455266,0.00009117334],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9820427,0.0002764788,0.01351409,0.00007925188,0.00001477973,0.00001070974,0.0001402434,0.0001039698,0.003817803],"genre_scores_gemma":[0.9934171,0.00009509184,0.00603315,0.00002924155,0.00001628699,0.000003098298,0.00007827221,0.000007279935,0.0003203689],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007505006,"threshold_uncertainty_score":0.002510726,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02578032110862408,"score_gpt":0.2746911443553116,"score_spread":0.2489108232466875,"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."}}