{"id":"W2022188885","doi":"10.1007/s10898-003-2687-y","title":"Full Nuclear Cones and a Relation Between Strong Optimization and Pareto Efficiency","year":2005,"lang":"en","type":"article","venue":"Journal of Global Optimization","topic":"Optimization and Variational Analysis","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Mathematics; Vector optimization; Hausdorff space; Cone (formal languages); Pareto principle; Regular polygon; Relation (database); Hausdorff distance; Locally convex topological vector space; Mathematical optimization; Convex optimization; Type (biology); Conic optimization; Optimization problem; Pareto efficiency; Convex analysis; Pure mathematics; Mathematical analysis; Algorithm; Geometry; Computer science; Multi-swarm optimization","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":[],"consensus_categories":[],"category_scores_codex":[0.0003129992,0.0001299052,0.000222002,0.0001360655,0.0001773001,0.0002714288,0.0001945777,0.00009080346,0.00002925663],"category_scores_gemma":[0.0001155557,0.0001188026,0.00005380342,0.0005636712,0.00003955659,0.001665607,0.00007749069,0.00009152599,0.000002419278],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001194661,"about_ca_system_score_gemma":0.00006140542,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004338711,"about_ca_topic_score_gemma":0.000001920815,"domain_scores_codex":[0.9986804,0.00009028491,0.0005394283,0.0001991833,0.00035387,0.0001368374],"domain_scores_gemma":[0.9987015,0.00005618561,0.0005555488,0.0001182227,0.0004297832,0.000138815],"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":[0.00001635257,0.00003126849,0.004821468,0.000003992452,0.00003222751,0.000001053477,0.000114834,0.9734415,0.000004147256,0.01913553,0.00004771481,0.002349956],"study_design_scores_gemma":[0.0006636261,0.0001626748,0.005708934,0.00001792366,0.00006522658,0.00006728581,0.00003373154,0.9928226,0.000002524665,0.000155408,0.0001793685,0.0001206588],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008160594,0.0002104457,0.9873695,0.003581984,0.00007739825,0.00008198175,0.00000470281,0.00003375809,0.0004796331],"genre_scores_gemma":[0.5779853,0.0001946388,0.4215909,0.0001008767,0.0001065011,4.252474e-7,0.000006753521,0.000005226874,0.000009416993],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5698247,"threshold_uncertainty_score":0.4844631,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009020491527263932,"score_gpt":0.2362717537272629,"score_spread":0.227251262199999,"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."}}