{"id":"W4387106596","doi":"10.1007/s00348-023-03704-w","title":"Convergence of turbulence statistics: random error of central moments computed from correlated data","year":2023,"lang":"en","type":"article","venue":"Experiments in Fluids","topic":"Wind and Air Flow Studies","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Turbulence; Method of moments (probability theory); Standard deviation; Convergence (economics); Mathematics; Sample (material); Statistics; Grid; Statistical physics; Applied mathematics; Physics; Geometry; Estimator; Meteorology","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001474221,0.000137861,0.0002869985,0.00004186229,0.00004266307,0.000005182773,0.0005833283,0.00004520399,0.001424859],"category_scores_gemma":[0.00006041098,0.0001279888,0.0000232936,0.0003635404,0.0002623512,0.0001490817,0.000819922,0.00007403179,0.0001639492],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000648166,"about_ca_system_score_gemma":0.00001696517,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001047132,"about_ca_topic_score_gemma":0.00001514873,"domain_scores_codex":[0.9984832,0.00006252732,0.000426931,0.000357324,0.0003810453,0.0002889638],"domain_scores_gemma":[0.9992909,0.0001033142,0.00009435251,0.0004430647,0.000008581368,0.00005981588],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003972318,0.0007950282,0.6416269,0.00004140458,0.0002174868,0.00006599326,0.01506997,0.005446016,0.261282,0.00008607738,0.07140173,0.003570194],"study_design_scores_gemma":[0.006373494,0.0001716158,0.6661242,0.0002320403,0.0000362231,6.604241e-7,0.001721971,0.2185832,0.1041032,0.0004132378,0.00176426,0.0004759803],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9960118,0.0002532993,0.001517781,0.00002597596,0.0007196736,0.0002301516,0.0007543177,0.00003334911,0.0004536594],"genre_scores_gemma":[0.9966704,0.0000843739,0.002618386,0.0000263089,0.00001683454,0.000008577124,0.0002823622,0.00001059327,0.0002821243],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2131372,"threshold_uncertainty_score":0.999488,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03769119181020705,"score_gpt":0.2947783099614049,"score_spread":0.2570871181511978,"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."}}