{"id":"W4229988500","doi":"10.23952/jano.1.2019.3.08","title":"Representation of the Pareto front for heterogeneous multi-objective optimization","year":2019,"lang":"en","type":"article","venue":"Journal of Applied and Numerical Optimization","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Deutsche Forschungsgemeinschaft","keywords":"Representation (politics); Multi-objective optimization; Front (military); Pareto principle; Computer science; Mathematical optimization; Mathematical economics; Mathematics; Engineering; Political science; Mechanical engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001877654,0.0001477216,0.0003392973,0.0001196229,0.00009788469,0.00005304107,0.0003199475,0.00008129677,0.00001341133],"category_scores_gemma":[0.0001207013,0.0001091922,0.0001251723,0.0003934857,0.00005108417,0.0004591617,0.00009808566,0.0001180323,9.129012e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007880884,"about_ca_system_score_gemma":0.00006512684,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002235668,"about_ca_topic_score_gemma":2.052779e-7,"domain_scores_codex":[0.998677,0.00006071843,0.0005507531,0.0002623399,0.0002984598,0.0001507327],"domain_scores_gemma":[0.9979927,0.000172839,0.0009429472,0.0002501536,0.0005696256,0.00007172318],"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":[0.0001172131,0.00008933517,0.0001443469,0.00001226904,0.00003705624,3.436551e-7,0.0004987759,0.9947791,0.0003382391,0.0004932543,0.00001115096,0.003478928],"study_design_scores_gemma":[0.001616516,0.000173394,0.0003068739,0.00002078888,0.00002547213,0.00001886756,0.00009362863,0.9919469,0.005316003,0.0003264735,0.00003320071,0.0001219003],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0006594934,0.00006024087,0.9979388,0.0001474026,0.0003622619,0.0006547985,0.000003670137,0.00001881449,0.0001545055],"genre_scores_gemma":[0.2469659,0.00005984442,0.7527613,0.0001062715,0.00004123957,0.00001413213,0.000004242946,0.0000157321,0.00003132449],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2463064,"threshold_uncertainty_score":0.445273,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01168561794993419,"score_gpt":0.2564947285716973,"score_spread":0.2448091106217631,"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."}}