{"id":"W4253000400","doi":"10.32920/ryerson.14665638.v1","title":"A multirotor vehicle performance prediction method","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Aerodynamics and Fluid Dynamics Research","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Centres of Excellence","keywords":"Multirotor; Rotor (electric); Aerodynamics; Flight test; Power (physics); Performance prediction; Interpolation (computer graphics); Computer science; Simulation; Aerospace engineering; Automotive engineering; Control theory (sociology); Engineering; Physics; Artificial intelligence; Mechanical 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":[0.0002590259,0.0002131354,0.0002382516,0.0001019051,0.00004915939,0.0001492565,0.0002457692,0.000319495,0.0002166568],"category_scores_gemma":[0.00001438156,0.0002185781,0.0001079943,0.0001220233,0.00002091326,0.00008753357,0.0004364237,0.0008747109,0.00002781649],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001996529,"about_ca_system_score_gemma":0.00005978363,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007505083,"about_ca_topic_score_gemma":0.00008134875,"domain_scores_codex":[0.9988235,0.00003319673,0.0002472154,0.0003237621,0.0002795243,0.0002927503],"domain_scores_gemma":[0.9992631,0.00002882794,0.00001755608,0.0004866638,0.0001045287,0.0000993036],"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":[0.000005598488,0.0000331847,0.001436893,0.0007693015,0.000136455,0.00001051237,0.0002388048,0.9465979,0.02520244,0.0001400771,0.0003076935,0.02512113],"study_design_scores_gemma":[0.0001250771,0.00001205743,0.01366876,0.00006170888,0.00001046859,0.000003072064,0.00003776142,0.9849262,0.000704193,0.00002421305,0.0002322008,0.0001943056],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7474692,0.0002718945,0.2419211,0.00002855842,0.0008038662,0.0003946228,0.0000676905,0.0005215634,0.008521449],"genre_scores_gemma":[0.9434097,0.001153394,0.05271361,0.0000136069,0.0001573557,0.0001863429,0.0002528944,0.00007769597,0.002035414],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1959404,"threshold_uncertainty_score":0.8913356,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01455863612741841,"score_gpt":0.2658040249380453,"score_spread":0.2512453888106269,"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."}}