{"id":"W2000544030","doi":"10.1115/detc2011-47946","title":"Multi-Objective Optimization of a Segmented Lunar Wheel Concept","year":2011,"lang":"en","type":"article","venue":"","topic":"Mechanical Engineering and Vibrations Research","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Canadian Space Agency; Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Conceptual design; Multi-objective optimization; Computer science; MATLAB; Genetic algorithm; Component (thermodynamics); Reliability (semiconductor); Finite element method; Set (abstract data type); Mathematical optimization; Engineering; Structural engineering; Mathematics","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":[0.0006696688,0.0005227263,0.0005037102,0.0006050449,0.0002334074,0.0005720488,0.0005578889,0.0005836947,0.002067601],"category_scores_gemma":[0.0006067415,0.0003385155,0.0005288449,0.0003167554,0.0003536489,0.0004231278,0.0004490491,0.0002885905,0.0002430541],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005525221,"about_ca_system_score_gemma":0.0007114351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001470107,"about_ca_topic_score_gemma":0.002287078,"domain_scores_codex":[0.9998275,0.00005178989,0.000005554794,0.00002131286,0.00006976499,0.00002403795],"domain_scores_gemma":[0.9998294,0.00006707793,0.0000341933,0.00001343014,0.00004224275,0.00001363842],"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":[0.00003029383,0.00002520488,0.0001977072,0.00005535946,0.00001513741,0.00003747981,0.00001706949,0.9788526,0.0060842,0.003680998,0.0001744569,0.01082945],"study_design_scores_gemma":[0.00000635021,0.00007178427,0.0001352572,0.000004983063,0.000005875359,0.00001234726,0.0000102463,0.9972146,0.001087656,0.0008159342,0.0006308885,0.000004125718],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.118352,0.000290126,0.8729778,0.00008359201,0.00002930289,0.00009594423,0.00006066764,0.0001249031,0.007985737],"genre_scores_gemma":[0.8090228,0.0001995275,0.1860544,0.0000365718,0.000009577901,0.0001862889,0.00008157972,0.0000634278,0.004345793],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002067601,"threshold_uncertainty_score":0.006916821,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03265177400446649,"score_gpt":0.2452082285622708,"score_spread":0.2125564545578044,"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."}}