{"id":"W4234891635","doi":"10.23952/jnva.4.2020.3.05","title":"Efficiency conditions for multiobjective bilevel programming problems via convexificators","year":2020,"lang":"en","type":"article","venue":"Journal of Nonlinear and Variational Analysis","topic":"Optimization and Variational Analysis","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Foundation for Science and Technology Development","keywords":"Bilevel optimization; Multiobjective programming; Mathematical optimization; Multi-objective optimization; Computer science; Mathematics; Optimization problem","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":[0.0004368053,0.0001229593,0.0003440162,0.0004438792,0.0002197655,0.0001523783,0.0002830785,0.00005500766,0.00004618882],"category_scores_gemma":[0.0002603987,0.0001003769,0.000381142,0.001915032,0.00003225646,0.0004636352,0.00004307642,0.0001102021,0.000003690676],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003008726,"about_ca_system_score_gemma":0.0001507522,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007854163,"about_ca_topic_score_gemma":0.000002569028,"domain_scores_codex":[0.9985538,0.00006415455,0.0006128265,0.0002457535,0.0003869251,0.0001365281],"domain_scores_gemma":[0.9977784,0.0002876416,0.0005803181,0.00009497938,0.001066658,0.0001920607],"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":[0.00008940122,0.0009962691,0.01401103,0.00007680578,0.007652501,0.000006566365,0.004810416,0.8674805,0.0009925776,0.09495615,0.0002897308,0.008638086],"study_design_scores_gemma":[0.0005933968,0.0001431025,0.006149074,0.00000408266,0.0007520468,0.000006642961,0.00005405225,0.9897202,0.00004054445,0.001485507,0.0009277826,0.0001235632],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001008114,0.00005504819,0.9930903,0.005553619,0.00005552087,0.0001413816,0.00004982799,0.00002055623,0.00002559915],"genre_scores_gemma":[0.6135673,0.00001883171,0.3854345,0.0006265155,0.0002360978,0.00001143599,0.00006949876,0.0000063466,0.00002947187],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6125591,"threshold_uncertainty_score":0.409325,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0232271809863033,"score_gpt":0.2721182565099743,"score_spread":0.248891075523671,"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."}}