{"id":"W4399023489","doi":"10.2139/ssrn.4841929","title":"A Deep Learning Approach for Epistemic Uncertainty Quantification of Turbulent Flow Simulations","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Nuclear Engineering Thermal-Hydraulics","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Uncertainty quantification; Turbulence; Flow (mathematics); Knowledge flow; Epistemology; Computer science; Philosophy; Knowledge management; Machine learning; Mechanics; Physics","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":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0009834829,0.0003385594,0.0004034495,0.000292778,0.00008200474,0.00008354509,0.0003890378,0.000316606,0.000007331528],"category_scores_gemma":[0.00007102459,0.0003586271,0.0003179235,0.0001743127,0.00002675947,0.00004801976,0.000101501,0.004629429,0.00001042847],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001668248,"about_ca_system_score_gemma":0.0004507841,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008952451,"about_ca_topic_score_gemma":0.00003278707,"domain_scores_codex":[0.997583,0.00005109412,0.0005916111,0.0002981389,0.0002396836,0.001236449],"domain_scores_gemma":[0.9992197,0.00008306744,0.0001663145,0.0003298099,0.0001307007,0.00007038724],"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":[0.00001667712,0.00002281427,0.000003299274,0.0006930029,0.0004799997,5.43291e-7,0.0002941926,0.9795972,0.001662848,0.008973758,0.00002606434,0.008229648],"study_design_scores_gemma":[0.0002106774,0.00005418528,0.000008437791,0.0001416789,0.0001680403,0.00007835483,0.0001496843,0.9419618,0.0001147823,0.05626756,0.0005472195,0.0002975978],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03880237,0.009556204,0.9496012,0.000135946,0.0006387458,0.0005392293,0.00001797643,0.0004352115,0.0002730837],"genre_scores_gemma":[0.9915469,0.001625277,0.005812572,0.000002995176,0.0003873934,0.00003917381,0.0001633954,0.000201121,0.0002211906],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9527445,"threshold_uncertainty_score":0.9998866,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01164900979546746,"score_gpt":0.2335951894973544,"score_spread":0.221946179701887,"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."}}