{"id":"W2999787543","doi":"10.1063/1.5127202","title":"Machine learning strategies applied to the control of a fluidic pinball","year":2020,"lang":"en","type":"article","venue":"Physics of Fluids","topic":"Fluid Dynamics and Turbulent Flows","field":"Engineering","cited_by":71,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation; University of Calgary","keywords":"Wake; Physics; Reynolds number; Flow control (data); Wind tunnel; Particle image velocimetry; Drag; Fluidics; Control theory (sociology); Flow separation; Mechanics; Aerospace engineering; Artificial intelligence; Computer science; Boundary layer; Turbulence; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007163415,0.0001362928,0.000255258,0.00001856977,0.00002828823,0.0000177504,0.000235974,0.00002947154,0.00001618945],"category_scores_gemma":[0.00001130913,0.0001094817,0.00008228915,0.0002034894,0.00002916733,0.00005244678,0.0000389457,0.000154638,0.0000213158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008875378,"about_ca_system_score_gemma":0.00001688249,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001948552,"about_ca_topic_score_gemma":0.000001558056,"domain_scores_codex":[0.9993162,0.00001227139,0.0002232311,0.0001152823,0.0001750641,0.0001579196],"domain_scores_gemma":[0.9996577,0.0000454851,0.00002592255,0.0001682155,0.00004003527,0.00006262289],"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":[0.00002400088,0.00001596538,0.00008884798,0.00009984062,0.00007808803,4.436106e-7,0.0008788203,0.7042893,0.2497798,0.04236313,0.000200344,0.002181442],"study_design_scores_gemma":[0.000438544,0.0001000177,0.0001559044,0.00001412628,0.00003186339,1.839651e-7,0.00005513779,0.9884702,0.007270584,0.0007045508,0.002629151,0.0001297111],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3504725,0.002915551,0.627632,0.001115153,0.0002757569,0.0008120454,0.0001605928,0.0003771392,0.01623929],"genre_scores_gemma":[0.9993374,0.00006747175,0.0002897692,0.0001142891,0.000128798,0.00001390066,0.00001197986,0.00002968086,0.000006701821],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.648865,"threshold_uncertainty_score":0.4464535,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008520833296263398,"score_gpt":0.1931723074325486,"score_spread":0.1846514741362852,"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."}}