{"id":"W2782832970","doi":"10.2514/6.2018-1528","title":"Performance Prediction of Multirotor Vehicles Using A Higher Order Potential Flow Method","year":2018,"lang":"en","type":"article","venue":"2018  AIAA Aerospace Sciences Meeting","topic":"Aerodynamics and Fluid Dynamics Research","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Multirotor; Flow (mathematics); Order (exchange); Computer science; Environmental science; Engineering; Mathematics; Business; Aerospace engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003329523,0.000605241,0.0006280676,0.0004374572,0.0004979166,0.0007365205,0.0005287096,0.0008118056,0.001037937],"category_scores_gemma":[0.0008467053,0.000266309,0.0004253022,0.0002913423,0.0004280436,0.000554776,0.0005468738,0.0006244212,0.0001975215],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005496615,"about_ca_system_score_gemma":0.0007057798,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0129703,"about_ca_topic_score_gemma":0.004258752,"domain_scores_codex":[0.9998913,0.00003321048,0.000003994349,0.00001498566,0.00003225618,0.00002410156],"domain_scores_gemma":[0.9995924,0.0001914799,0.00005064561,0.00002796034,0.00008834251,0.00004931201],"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":[0.00004822301,0.00001995024,0.0006171797,0.00001183277,0.000005966448,0.00003273413,0.00001254831,0.9939579,0.001646015,0.0008873778,0.00009708308,0.002663178],"study_design_scores_gemma":[0.000001170543,0.000006522605,0.00007487419,4.027874e-7,3.672159e-7,9.587255e-7,9.831642e-7,0.9997315,0.0001200396,0.00004923344,0.00001303465,9.470186e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7863382,0.0005538476,0.2013845,0.0004373824,0.0001259661,0.00004315138,0.0001438677,0.000506356,0.01046676],"genre_scores_gemma":[0.9927202,0.00006793693,0.00578501,0.0000150514,0.00001635164,0.00001247097,0.0000515611,0.00003216663,0.001299301],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0129703,"threshold_uncertainty_score":0.02578962,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02534577824919076,"score_gpt":0.2839518139206005,"score_spread":0.2586060356714097,"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."}}