{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002203724,0.0001194287,0.0001538122,0.0003207314,0.00004530543,0.00001673209,0.000226288,0.0001549632,0.00003454222],"category_scores_gemma":[0.0002665384,0.0001223917,0.00004487728,0.0004609477,0.00001603245,0.0001168452,0.000013486,0.0001189087,0.000005730487],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007315006,"about_ca_system_score_gemma":0.0001469206,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002002397,"about_ca_topic_score_gemma":0.00037478,"domain_scores_codex":[0.9993232,0.000004327076,0.0002209399,0.0001041228,0.0001437355,0.000203704],"domain_scores_gemma":[0.999352,0.00002376379,0.0001826961,0.00009793782,0.0002487779,0.00009478674],"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":[0.000009455788,0.0001710651,0.1204203,0.001122514,0.0004597556,3.115935e-7,0.005302174,0.7515171,0.03881023,0.03818364,0.04328351,0.0007199672],"study_design_scores_gemma":[0.0006132795,0.00009607693,0.08289579,0.0005830621,0.0001790437,0.00001401752,0.001633485,0.8173817,0.09168604,0.000313803,0.003731077,0.0008725672],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5936108,0.003143165,0.1337356,0.002043891,0.01908024,0.007366578,0.00009975819,0.004349223,0.2365707],"genre_scores_gemma":[0.9843903,0.00001640211,0.01385955,0.000004742607,0.00003807858,0.00005314305,0.000001616046,0.00005687957,0.001579294],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3907794,"threshold_uncertainty_score":0.4990988,"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."}}