{"id":"W4388650550","doi":"10.48550/arxiv.2311.06112","title":"Turbulence Scaling from Deep Learning Diffusion Generative Models","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Fluid Dynamics and Turbulent Flows","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Alliance de recherche numérique du Canada","keywords":"Scaling; Turbulence; Generative grammar; Statistical physics; Diffusion; Computer science; Artificial intelligence; Physics; Mechanics; Mathematics; Geometry; Thermodynamics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006645768,0.0005879502,0.0005827997,0.0007308011,0.0003433334,0.000966078,0.0006335416,0.0006913626,0.001375511],"category_scores_gemma":[0.002838294,0.0004701318,0.000720517,0.0003891681,0.0008189284,0.001076935,0.0008782751,0.001312238,0.0001766087],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00112047,"about_ca_system_score_gemma":0.0005323351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003539628,"about_ca_topic_score_gemma":0.004546865,"domain_scores_codex":[0.9998697,0.000038856,0.000006102955,0.00002959744,0.00003638836,0.00001942825],"domain_scores_gemma":[0.9990748,0.0006019918,0.0001130408,0.00007399412,0.00007592951,0.00006014751],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001943545,0.0000119483,0.0007160239,0.00001777118,0.00001205529,0.00003257809,0.00002089749,0.9773229,0.0007488061,0.01610539,0.000398652,0.00459356],"study_design_scores_gemma":[0.000001278111,0.000001859098,0.00005457736,0.000001226299,7.86423e-7,0.000002751596,0.000001179831,0.9952638,0.00006831207,0.004555711,0.00004708575,0.000001346683],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2831176,0.0006300306,0.7077187,0.001123632,0.00009261613,0.00005299652,0.0004223415,0.000889089,0.005952987],"genre_scores_gemma":[0.9725873,0.0002626078,0.02418577,0.00008324978,0.0000463915,0.00005826567,0.0003033094,0.0000861334,0.002387167],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003539628,"threshold_uncertainty_score":0.008129537,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04885804415508507,"score_gpt":0.1641676527351653,"score_spread":0.1153096085800803,"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."}}