{"id":"W2810450991","doi":"10.1029/2017jc013731","title":"Measuring the Dissipation Rate of Turbulent Kinetic Energy in Strongly Stratified, Low‐Energy Environments: A Case Study From the Arctic Ocean","year":2018,"lang":"en","type":"article","venue":"Journal of Geophysical Research Oceans","topic":"Oceanographic and Atmospheric Processes","field":"Earth and Planetary Sciences","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"University of British Columbia; Canada Foundation for Innovation; ArcticNet; Government of Canada; Marine Environmental Observation Prediction and Response Network; Alfred P. Sloan Foundation","keywords":"Turbulence; Dissipation; Turbulence kinetic energy; Stratification (seeds); Mechanics; Shear (geology); Kinetic energy; Atmospheric sciences; Geology; Physics; Thermodynamics; Classical mechanics","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":[0.001375495,0.0001457279,0.0002455165,0.00005141526,0.0002797367,0.0000959326,0.0005889849,0.00003989376,0.0001692446],"category_scores_gemma":[0.0002540652,0.00007449451,0.00009805763,0.0005630663,0.0005418739,0.0002651359,0.00004138978,0.0004604763,0.000005230992],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001758298,"about_ca_system_score_gemma":0.0001516705,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0214762,"about_ca_topic_score_gemma":0.0079181,"domain_scores_codex":[0.9970558,0.0008470415,0.000511356,0.0002093022,0.0009938703,0.0003826047],"domain_scores_gemma":[0.9978564,0.001266749,0.0002516069,0.0002802128,0.000195792,0.0001492189],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001676777,0.00274251,0.9003758,0.00005360661,0.0005734412,0.00295664,0.009860457,0.008048527,0.0005997679,0.0005661137,0.0008834438,0.07166284],"study_design_scores_gemma":[0.001140164,0.003200902,0.9575794,0.0001757237,0.00006815049,0.0001114707,0.01436648,0.0170454,0.0004761882,0.005122191,0.0005275296,0.0001864299],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.99845,0.0004189464,0.0003589946,0.0004900554,0.0000852541,0.0001100019,0.00001031464,0.000002516,0.00007395517],"genre_scores_gemma":[0.9991925,0.0001932278,0.00003136242,0.00004969817,0.000454568,6.26454e-7,0.000003732244,0.000005756439,0.00006851513],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07147641,"threshold_uncertainty_score":0.9850399,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03292118709190299,"score_gpt":0.2673661673444664,"score_spread":0.2344449802525634,"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."}}