{"id":"W2155830601","doi":"10.24908/pceea.v0i0.3914","title":"MULTIDISCIPLINARY DESIGN OPTIMIZATION OF AEROSPACE SYSTEMS","year":2011,"lang":"en","type":"article","venue":"Proceedings of the Canadian Engineering Education Association (CEEA)","topic":"Spacecraft Design and Technology","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of Toronto","funders":"","keywords":"Aerospace; Multidisciplinary design optimization; Multidisciplinary approach; Guideline; Systems engineering; Computer science; Supersonic speed; Engineering; Aerospace engineering; Management science; Medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.003202885,0.001145686,0.001114264,0.001259021,0.000474436,0.001721371,0.0005594507,0.00104072,0.002298177],"category_scores_gemma":[0.006312056,0.0004742377,0.0007495568,0.0008030281,0.0008362178,0.0007842818,0.001629001,0.0008347736,0.0002569173],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001024507,"about_ca_system_score_gemma":0.001473907,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002416341,"about_ca_topic_score_gemma":0.001944876,"domain_scores_codex":[0.9982343,0.001157681,0.00005111718,0.0001094194,0.0003618943,0.00008553711],"domain_scores_gemma":[0.9978487,0.001598162,0.0001337212,0.0001109348,0.0002351496,0.00007324461],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001754678,0.00001931457,0.0002285662,0.00006374018,0.00003223349,0.00001521291,0.00001563658,0.9774415,0.0002566594,0.008773526,0.0001885491,0.01294746],"study_design_scores_gemma":[0.000008221377,0.000030722,0.000137314,0.00001872944,0.000008263786,0.000006615526,0.00001970178,0.9872277,0.0003069732,0.0110869,0.001144798,0.000004050301],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03882239,0.001729219,0.9396656,0.0006742053,0.0001135844,0.000135258,0.0001203135,0.0001041167,0.01863534],"genre_scores_gemma":[0.7182079,0.001379144,0.2739522,0.0002175648,0.00008776567,0.0005671149,0.0002269371,0.0001476018,0.005213771],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003202885,"threshold_uncertainty_score":0.01693869,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01218791079264442,"score_gpt":0.1775320560088276,"score_spread":0.1653441452161832,"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."}}