{"id":"W2592063268","doi":"10.1007/s10546-017-0244-3","title":"Impact of Eddy Characteristics on Turbulent Heat and Momentum Fluxes in the Urban Roughness Sublayer","year":2017,"lang":"en","type":"article","venue":"Boundary-Layer Meteorology","topic":"Wind and Air Flow Studies","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ministry of Education and Child Care","funders":"National Key Research and Development Program of China; Jiangsu Collaborative Innovation Center for Climate Change; Nanjing University; National Natural Science Foundation of China","keywords":"Eddy covariance; Sensible heat; Turbulence; Atmospheric sciences; Momentum (technical analysis); Mean flow; Environmental science; Meteorology; Heat flux; Anemometer; Roughness length; Wind speed; Mechanics; Geology; Geography; Wind profile power law; Physics; Heat transfer","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0004340626,0.0002136026,0.0003511992,0.00004285648,0.0004771115,0.00009922186,0.0004694494,0.00008986505,0.0002814595],"category_scores_gemma":[0.00008236579,0.0001318446,0.00008857988,0.00005601334,0.0007961881,0.0001726166,0.0002571639,0.0001968153,0.00004947637],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007778815,"about_ca_system_score_gemma":0.00001829994,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009383587,"about_ca_topic_score_gemma":0.0001548195,"domain_scores_codex":[0.998658,0.0001319242,0.0002611463,0.0003300484,0.0002313845,0.0003874872],"domain_scores_gemma":[0.9991293,0.0001090498,0.0001249474,0.0005696487,0.000009134729,0.00005794819],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001981794,0.0003184763,0.98112,0.00001204137,0.00006757282,0.00005227077,0.002312673,0.00005133476,0.006466306,0.0002378681,0.005786668,0.003376553],"study_design_scores_gemma":[0.0005436491,0.000628633,0.9891941,0.000009994278,0.00002171676,0.00002038571,0.00005812793,0.0001342178,0.000262782,0.0005682032,0.008414512,0.0001436767],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9949748,0.0001169044,0.00001049455,0.002037206,0.0001934024,0.0002139896,0.00002258652,0.000009148586,0.00242146],"genre_scores_gemma":[0.9991508,0.00006556239,0.00004032426,0.0003548116,0.0001004908,0.00002516322,0.000005152952,0.00001247756,0.0002452332],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008074044,"threshold_uncertainty_score":0.5376467,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01821696496081813,"score_gpt":0.2756806737137858,"score_spread":0.2574637087529676,"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."}}