{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003764612,0.0008527771,0.0005091342,0.0005507032,0.0003404259,0.0005332052,0.001091882,0.0005956168,0.006351711],"category_scores_gemma":[0.001053901,0.0003189176,0.0006037542,0.000394077,0.0001956975,0.0006072526,0.0004891332,0.0008590029,0.002469127],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003661781,"about_ca_system_score_gemma":0.0007561424,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006234076,"about_ca_topic_score_gemma":0.002286339,"domain_scores_codex":[0.9996561,0.00005477417,0.00001087791,0.00008422965,0.0001590454,0.00003502482],"domain_scores_gemma":[0.9996275,0.00008055259,0.00004067812,0.00007304856,0.0001612567,0.00001708083],"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":[0.00005945736,0.00007501048,0.002077242,0.00007955242,0.00002795969,0.00010303,0.00005324013,0.8040548,0.009907548,0.006273269,0.00294716,0.1743416],"study_design_scores_gemma":[0.000004246663,0.00002090823,0.0002838091,0.000003843645,0.000002761649,0.00001986039,0.000004244861,0.9958241,0.001516421,0.0004483268,0.001864868,0.000006629667],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01109227,0.00007741083,0.9818402,0.00004228693,0.0000407821,0.00007971287,0.0003293873,0.001839036,0.00465899],"genre_scores_gemma":[0.5221205,0.0003600649,0.4614378,0.00007994063,0.00008921849,0.0004260637,0.001720612,0.0007995493,0.01296628],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006351711,"threshold_uncertainty_score":0.02124858,"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."}}