{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004122783,0.00009597663,0.0001495747,0.0001156196,0.0001455298,0.0000273814,0.0002694266,0.0000221245,0.0002797136],"category_scores_gemma":[0.0001816705,0.00005578289,0.00008604056,0.000298034,0.0006185832,0.0001715767,0.00005579839,0.0002046917,0.0005310797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007261044,"about_ca_system_score_gemma":0.00003282221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002160414,"about_ca_topic_score_gemma":0.00004022658,"domain_scores_codex":[0.9981626,0.0001592605,0.0001657982,0.0002777135,0.0006714191,0.0005632621],"domain_scores_gemma":[0.9989117,0.0005173621,0.00003715226,0.0002445858,0.0001143785,0.0001748442],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002167684,0.00004535243,0.0866705,0.00005576061,0.00004401885,0.0002797256,0.0004148582,0.00003132067,0.2670174,0.0001282103,0.009467926,0.6356282],"study_design_scores_gemma":[0.0009914705,0.0003431496,0.935362,0.0003881517,0.00001620401,0.0000253226,0.0004715096,0.01891169,0.02971695,0.002878858,0.01032893,0.0005657332],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9909246,0.00006574017,0.0001064458,0.007887018,0.0001747664,0.00008687004,0.00003290201,0.0000185882,0.0007030625],"genre_scores_gemma":[0.9979513,0.00004493998,0.001101809,0.0003419558,0.0003135401,1.054385e-8,0.000006962633,0.00000453454,0.0002350023],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8486915,"threshold_uncertainty_score":0.6826132,"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."}}