{"id":"W2462920463","doi":"10.18063/som.2016.01.004","title":"Assessing the performance of a multi-nested ocean circulation model using satellite remote sensing and in situ observations","year":2016,"lang":"en","type":"article","venue":"Satellite Oceanography and Meteorology","topic":"Oceanographic and Atmospheric Processes","field":"Earth and Planetary Sciences","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Hydrography; Oceanography; Upwelling; Buoy; Climatology; Sea surface temperature; Downscaling; Tide gauge; Forcing (mathematics); Environmental science; Current (fluid); Satellite; Ocean current; Nova scotia; Geology; Sea level; Climate change","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005557191,0.0001925769,0.0002747354,0.0001381699,0.0002286925,0.00004778805,0.0001041603,0.0001284815,0.000003850455],"category_scores_gemma":[0.0000426276,0.0001184611,0.00005695226,0.0007695021,0.0005220987,0.0005899396,0.00001923358,0.0001207641,3.748283e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000268496,"about_ca_system_score_gemma":0.00004450293,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001090108,"about_ca_topic_score_gemma":0.0003410834,"domain_scores_codex":[0.998638,0.000154468,0.0003957305,0.0003348553,0.0001348066,0.0003421533],"domain_scores_gemma":[0.9991468,0.0003233073,0.0001979683,0.0001772972,0.00007724102,0.00007742851],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003387833,0.00000652437,0.8013107,0.00005112294,0.00002054697,0.000002116406,0.0003072907,0.001265023,0.003738483,0.00001352702,1.759808e-7,0.1932507],"study_design_scores_gemma":[0.0003431826,0.00003965466,0.7229452,0.00008795349,0.00003744089,0.00002800999,0.0001561317,0.2746003,0.0004068961,0.001170776,0.00004770859,0.0001367422],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9852642,0.008000999,0.006236103,0.0001385348,0.00006860166,0.0001663072,0.000007245251,0.00002856455,0.00008948952],"genre_scores_gemma":[0.9675776,0.007433807,0.02479783,0.0001491214,0.00001852372,3.693869e-8,0.000009597778,0.000006536812,0.000006981173],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2733353,"threshold_uncertainty_score":0.4830704,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0413110668532462,"score_gpt":0.2525226439680596,"score_spread":0.2112115771148134,"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."}}