{"id":"W2693076760","doi":"10.1016/j.ejor.2018.02.045","title":"Trade-off preservation in inverse multi-objective convex optimization","year":2018,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematical optimization; Inverse; Linear programming; Computer science; Pareto principle; Mathematics; Optimization problem; Convex optimization; Regular polygon","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004661056,0.0001433017,0.0001889772,0.0008113687,0.0003060986,0.0002794615,0.000953532,0.00003843443,0.0001138494],"category_scores_gemma":[0.002116908,0.0001328605,0.00005627911,0.001404623,0.000263592,0.002998414,0.0002547137,0.0006023549,0.00008394566],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003031752,"about_ca_system_score_gemma":0.000475721,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005476863,"about_ca_topic_score_gemma":0.0000117879,"domain_scores_codex":[0.9955562,0.00198141,0.000666739,0.0003446259,0.001116562,0.0003344135],"domain_scores_gemma":[0.9963951,0.0003105939,0.0002312138,0.0002889016,0.002602317,0.0001718993],"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":[0.0001745964,0.000524404,0.001254479,0.000009059152,0.00004543238,0.0002678325,0.007033877,0.9599548,0.001992873,0.00598246,0.001714013,0.02104617],"study_design_scores_gemma":[0.002114149,0.0005704591,0.01819756,0.00005740213,0.000001815214,0.0000701736,0.0002437382,0.975071,0.001060793,0.0001374716,0.002318107,0.0001573114],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003812957,0.00007350089,0.9900653,0.001795746,0.0003079586,0.0002998401,0.00000296989,0.00002243763,0.003619262],"genre_scores_gemma":[0.282466,0.000111038,0.7160032,0.0003356763,0.000460149,0.000006146643,0.000007537426,0.00003186523,0.0005784583],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.278653,"threshold_uncertainty_score":0.5417895,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08503374081602867,"score_gpt":0.3640516131639794,"score_spread":0.2790178723479507,"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."}}