{"id":"W165434262","doi":"","title":"Pareto Analysis Of Pareto Design","year":2006,"lang":"en","type":"article","venue":"Proceedings of the Joint CIB W78, W102, ICCCBE, ICCC, and DMUCE International Conference on Computing and Decision Making in Civil and Building Engineering, Montreal, Canada, 14-16 June","topic":"Design Education and Practice","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Compromise; Pareto principle; Mathematical optimization; Optimal design; Computer science; Mathematical economics; Mathematics; Economics; Statistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006438975,0.0002965359,0.000493806,0.00057509,0.00009713216,0.0001562636,0.0002709829,0.0001026716,0.00001468695],"category_scores_gemma":[0.0003492931,0.0002565102,0.00007673935,0.0005400563,0.00005707036,0.0001689315,0.0001126997,0.0002902278,1.528436e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001411168,"about_ca_system_score_gemma":0.0000770904,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01609553,"about_ca_topic_score_gemma":0.02805117,"domain_scores_codex":[0.9981544,0.00001493033,0.0006625284,0.0003727835,0.0005143352,0.0002809945],"domain_scores_gemma":[0.9986424,0.0005424052,0.0002903805,0.0001348548,0.0002965062,0.00009340139],"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":[0.0004172577,0.0003043199,0.03880217,0.0007648001,0.001201623,0.00001992043,0.001392121,0.7851664,0.01709467,0.04910214,0.02768691,0.07804761],"study_design_scores_gemma":[0.0005829638,0.00005021699,0.1074725,0.001101765,0.0001755293,0.00002676381,0.0002834058,0.883976,0.001736663,0.002801212,0.001374037,0.0004190317],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9496599,0.0009472364,0.04353989,0.0004064224,0.0009379505,0.0002317386,0.00003359692,0.00009064333,0.004152646],"genre_scores_gemma":[0.9955445,0.0002283613,0.003968289,0.00005886866,0.00008604919,0.00000659226,0.000003677908,0.00002480393,0.00007884918],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0988095,"threshold_uncertainty_score":0.9999887,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01710224591148796,"score_gpt":0.2461152549689596,"score_spread":0.2290130090574716,"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."}}