{"id":"W4252499335","doi":"10.32920/ryerson.14649783","title":"Development of UAV derivative MDO methodology for flight simulation","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Aircraft Design and Technologies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Multidisciplinary design optimization; Derivative (finance); Process (computing); Flight simulator; Simulation; Computer science; Engineering; Multidisciplinary approach","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":[0.0007751287,0.0005473264,0.0006310639,0.0005347013,0.0003392578,0.0005063863,0.0008545577,0.0005587568,0.003139445],"category_scores_gemma":[0.001229993,0.0003745154,0.0006702709,0.0003365029,0.000291643,0.0003085845,0.0006726483,0.0008918008,0.0005442537],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004599064,"about_ca_system_score_gemma":0.001049054,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003139579,"about_ca_topic_score_gemma":0.002380259,"domain_scores_codex":[0.9997529,0.00006660131,0.00001374615,0.00002628205,0.0001173411,0.00002309772],"domain_scores_gemma":[0.9996363,0.0001358422,0.00003242154,0.00004032521,0.0001380537,0.00001699695],"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":[0.00002410854,0.00003547466,0.0005986501,0.0001647642,0.00002864588,0.0000827371,0.00005603004,0.9188512,0.007160368,0.02686216,0.0006848672,0.04545105],"study_design_scores_gemma":[0.000005272017,0.00001284888,0.00005842705,0.000009297984,0.000002980974,0.000009746936,0.000004578067,0.9945177,0.001168175,0.001578362,0.002629236,0.00000325668],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004654693,0.00009154767,0.9914368,0.0000321205,0.00002253776,0.00008409601,0.00008160251,0.0003425253,0.003254141],"genre_scores_gemma":[0.1613172,0.0002154661,0.8348161,0.00003781466,0.00002094167,0.0005074235,0.000294777,0.0003020369,0.002488207],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003139579,"threshold_uncertainty_score":0.01050246,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1339321546614659,"score_gpt":0.3571032787808954,"score_spread":0.2231711241194295,"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."}}