{"id":"W4380421154","doi":"10.20944/preprints202306.0904.v1","title":"Wavelet Transforms and Machine Learning Methods for the Study of Turbulence","year":2023,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Fluid Dynamics and Turbulent Flows","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Turbulence; Wavelet; Energy cascade; Vortex; Large eddy simulation; Eddy; Cascade; Wavelet transform; Statistical physics; Physics; Grid; Turbulence kinetic energy; Computer science; Mechanics; Classical mechanics; Artificial intelligence; Mathematics; Geometry; Engineering","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":[0.002039775,0.0009562834,0.001079981,0.001619275,0.000327589,0.001688736,0.0007597343,0.001335915,0.002135462],"category_scores_gemma":[0.004981272,0.0003193652,0.000918663,0.00293015,0.001506601,0.001794956,0.001240723,0.003308216,0.001007164],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005687534,"about_ca_system_score_gemma":0.0008102256,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001432408,"about_ca_topic_score_gemma":0.0007507413,"domain_scores_codex":[0.9990645,0.0003901678,0.00007820013,0.0001171297,0.000309281,0.00004070635],"domain_scores_gemma":[0.998136,0.001358258,0.0001416644,0.0001599278,0.0001619007,0.00004214025],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006612636,0.00006598349,0.001586609,0.0009666068,0.0002081844,0.0002187449,0.0002249595,0.1417316,0.003868738,0.5424478,0.01077991,0.2978346],"study_design_scores_gemma":[0.00002136341,0.00006119531,0.0008956972,0.0001939843,0.00003000486,0.000130703,0.00007041409,0.4647144,0.001093508,0.4936871,0.03905804,0.00004355692],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003541582,0.031978,0.956681,0.001920218,0.0006813282,0.00004357256,0.0001658211,0.000249974,0.004738565],"genre_scores_gemma":[0.2135434,0.0858317,0.6793755,0.0008361234,0.004999359,0.0004667132,0.0008851055,0.0002504472,0.01381165],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002135462,"threshold_uncertainty_score":0.01078749,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1157255283206512,"score_gpt":0.3536592630220479,"score_spread":0.2379337347013968,"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."}}