{"id":"W4399505006","doi":"10.1016/j.jfluidstructs.2024.104141","title":"Use of machine learning to optimize actuator configuration on an airfoil","year":2024,"lang":"en","type":"article","venue":"Journal of Fluids and Structures","topic":"Fluid Dynamics and Turbulent Flows","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Lakehead University","funders":"Alliance de recherche numérique du Canada","keywords":"Airfoil; Actuator; Computer science; Engineering; Structural engineering; Control engineering; Mechanical engineering; Artificial intelligence; Aerospace engineering; Control theory (sociology); Control (management)","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":[0.00009420931,0.0001010549,0.0001690929,0.0001611901,0.00002725584,0.0001046023,0.00005393379,0.00004880202,0.00005560933],"category_scores_gemma":[0.00003303998,0.00007330517,0.00005343281,0.00006702479,0.00001126761,0.0001752944,0.000007267088,0.0002097783,7.071534e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001783021,"about_ca_system_score_gemma":0.00001255652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008723332,"about_ca_topic_score_gemma":0.000003294456,"domain_scores_codex":[0.9994367,0.00001700782,0.0002465957,0.00006894125,0.0001458447,0.00008485111],"domain_scores_gemma":[0.9997265,0.00004919578,0.00002499304,0.00006176408,0.00004615176,0.00009138657],"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":[0.0001068036,0.00001043093,0.0001316334,0.0001085866,0.000135167,0.0000419074,0.0009054539,0.8098519,0.1371958,0.006090126,0.001050295,0.04437195],"study_design_scores_gemma":[0.0002132505,0.0005813377,0.003098668,0.0001121322,0.00003669704,0.00006387932,0.00002434465,0.9827461,0.00552921,0.0004073655,0.007057442,0.0001296092],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9952215,0.001032205,0.002997396,0.00008297991,0.0004758577,0.00004067904,0.00001565368,0.00002499095,0.0001087447],"genre_scores_gemma":[0.9958905,0.0003444894,0.003513089,0.00003467378,0.0001390454,3.615899e-7,0.000003807845,0.00001747308,0.00005655224],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1728942,"threshold_uncertainty_score":0.2989298,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01097356340148867,"score_gpt":0.2285306354485908,"score_spread":0.2175570720471021,"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."}}