{"id":"W4383648186","doi":"10.1016/j.procir.2023.02.146","title":"Multi-level design optimization considering uncertainties in configurations and parameters","year":2023,"lang":"en","type":"article","venue":"Procedia CIRP","topic":"Product Development and Customization","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Configuration design; Tree (set theory); Mathematical optimization; Node (physics); Function (biology); Optimization problem; Multi-objective optimization; Computer science; 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":[0.001429121,0.001424482,0.001583366,0.00105773,0.0005185357,0.001686352,0.00108329,0.001195102,0.002195996],"category_scores_gemma":[0.001770487,0.0008246652,0.001960702,0.001133873,0.0006673291,0.00135387,0.001383392,0.001176395,0.0003760255],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009652582,"about_ca_system_score_gemma":0.001271214,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001732533,"about_ca_topic_score_gemma":0.001656607,"domain_scores_codex":[0.998978,0.0003177559,0.00005544116,0.0001905329,0.0003586475,0.00009956319],"domain_scores_gemma":[0.9994032,0.0003236035,0.00009175193,0.00007747574,0.00008188783,0.00002204435],"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":[0.00001678361,0.00001213355,0.0001792599,0.00006072735,0.00002114906,0.00003514865,0.00002277825,0.9789574,0.001907359,0.003966545,0.0001016714,0.01471898],"study_design_scores_gemma":[0.000004075043,0.00003253333,0.00007915653,0.000009551929,0.00001106592,0.00001988937,0.00001076782,0.9944025,0.0006827714,0.003994161,0.0007459443,0.000007610497],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007362587,0.0002108458,0.9899502,0.00003893517,0.00001657521,0.00003218323,0.00002542904,0.0001178939,0.002245427],"genre_scores_gemma":[0.4840664,0.0005528046,0.5116754,0.00009798484,0.00003509318,0.0003068208,0.000179974,0.0001605247,0.002925038],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002195996,"threshold_uncertainty_score":0.007558048,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09388749274125932,"score_gpt":0.2447599265293889,"score_spread":0.1508724337881296,"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."}}