{"id":"W192244400","doi":"10.24908/pceea.v0i0.3940","title":"REVIEW OF METAMODELING TECHNIQUES FOR PRODUCT DESIGN WITH COMPUTATION-INTENSIVE PROCESSES","year":2011,"lang":"en","type":"article","venue":"Proceedings of the Canadian Engineering Education Association (CEEA)","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Metamodeling; Computer science; Computation; Automotive industry; Product (mathematics); Systems engineering; Risk analysis (engineering); Engineering; Software engineering; Business; Algorithm; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003485366,0.0001457954,0.0002179056,0.0001931309,0.0000660537,0.00002296038,0.0001830331,0.00006314425,0.000006875714],"category_scores_gemma":[0.001010624,0.0001257204,0.00004677183,0.0004311933,0.00001202916,0.0002239945,0.000007151116,0.00009426189,3.765643e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003982256,"about_ca_system_score_gemma":0.0004475809,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005620645,"about_ca_topic_score_gemma":0.0002382736,"domain_scores_codex":[0.999194,0.000003116094,0.0003107961,0.0001419865,0.0001664766,0.0001836036],"domain_scores_gemma":[0.9966859,0.00003969808,0.0003010241,0.00006835713,0.00283281,0.00007224637],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00008273971,0.0003701875,0.01211944,0.3177667,0.001921057,1.952855e-7,0.0167232,0.4594922,0.00482598,0.00684462,0.1549048,0.02494887],"study_design_scores_gemma":[0.0006860463,0.0002464489,0.006124299,0.03187834,0.00104717,0.00001733262,0.0008341093,0.1311177,0.8060074,0.00155019,0.01873536,0.001755533],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04859982,0.06000992,0.8163758,0.009389068,0.006104576,0.02777712,0.0003965915,0.00370474,0.02764235],"genre_scores_gemma":[0.8828157,0.001021197,0.1150738,0.00029222,0.00009577024,0.00042585,0.00002001126,0.00008103014,0.0001744347],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8342158,"threshold_uncertainty_score":0.5126728,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.018554832752037,"score_gpt":0.210749256646233,"score_spread":0.192194423894196,"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."}}