{"id":"W1998354875","doi":"10.1007/s11044-005-4310-0","title":"Multidisciplinary Optimization of Multibody Systems with Application to the Design of Rail Vehicles","year":2005,"lang":"en","type":"article","venue":"Multibody System Dynamics","topic":"Dynamics and Control of Mechanical Systems","field":"Engineering","cited_by":48,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo; Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada; Bombardier","keywords":"Multibody system; Multidisciplinary design optimization; Multidisciplinary approach; Genetic algorithm; Stability (learning theory); Control engineering; Automotive engineering; Engineering; Computer science","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":[0.001683878,0.001473222,0.00171207,0.001367911,0.0008056728,0.001354193,0.000733634,0.001744401,0.002782545],"category_scores_gemma":[0.004585977,0.0008753075,0.001191864,0.0008776819,0.001143182,0.0009451295,0.001844499,0.00125353,0.0002979934],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006517985,"about_ca_system_score_gemma":0.0009523468,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003288793,"about_ca_topic_score_gemma":0.002580036,"domain_scores_codex":[0.9994111,0.0003553207,0.00001916065,0.00005252336,0.0001168826,0.00004499618],"domain_scores_gemma":[0.9986816,0.0008537594,0.000131968,0.00004067911,0.0002097414,0.00008224995],"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":[0.0000192626,0.00002513831,0.0001462446,0.00004808287,0.00002371879,0.00002641378,0.00002166835,0.9870296,0.0003553477,0.005863109,0.0002276356,0.006213859],"study_design_scores_gemma":[0.000004607956,0.00001716995,0.00007096577,0.000006778557,0.000004833216,0.000004559896,0.00001115096,0.9950404,0.0001127595,0.004299162,0.0004237104,0.000003866034],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03282615,0.001702855,0.9540501,0.0008264089,0.0001785418,0.0000667517,0.00004004917,0.00008871597,0.01022046],"genre_scores_gemma":[0.8429559,0.001837778,0.1476736,0.0002086698,0.0002465561,0.0003388757,0.00009543936,0.0002157378,0.006427298],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003288793,"threshold_uncertainty_score":0.009308577,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005733453143066664,"score_gpt":0.2024060059361467,"score_spread":0.1966725527930801,"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."}}