{"id":"W4389705551","doi":"10.23952/jnva.7.2023.6.01","title":"A characterization of the $\\varepsilon$-normal set and its application in robust convex optimization problems","year":2023,"lang":"en","type":"article","venue":"Journal of Nonlinear and Variational Analysis","topic":"Optimization and Variational Analysis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"People's Government of Jilin Province; National Research Foundation of Korea; National Natural Science Foundation of China; National Research Foundation; Education Department of Jilin Province; National Science Foundation","keywords":"Characterization (materials science); Set (abstract data type); Mathematics; Regular polygon; Convex set; Convex optimization; Combinatorics; Computer science; Mathematical optimization; Materials science; Geometry; Nanotechnology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003037529,0.0013724,0.001241514,0.001584223,0.0006333871,0.002216162,0.001912617,0.001364467,0.001881716],"category_scores_gemma":[0.006399726,0.0005539295,0.001037444,0.00108557,0.003650171,0.003842213,0.003088203,0.003319059,0.0004122788],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001500565,"about_ca_system_score_gemma":0.001650325,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002822081,"about_ca_topic_score_gemma":0.001635734,"domain_scores_codex":[0.9984388,0.0005129041,0.00008693997,0.0003914525,0.0004571296,0.0001128497],"domain_scores_gemma":[0.9977089,0.0008093569,0.000353555,0.0001792356,0.0006839143,0.0002649949],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00007379671,0.00005354462,0.0009717065,0.0001522411,0.00003212571,0.000203343,0.0002051497,0.1770138,0.006408149,0.7865516,0.001790579,0.02654393],"study_design_scores_gemma":[0.000009582257,0.0000969356,0.0002804782,0.00003662753,0.000006557592,0.0001450477,0.0000815608,0.7879284,0.001754301,0.20551,0.004113962,0.00003652846],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01380876,0.0002995801,0.9799478,0.0004349497,0.00007484105,0.00004471091,0.0001105846,0.00004490585,0.005233852],"genre_scores_gemma":[0.6002731,0.001649093,0.3854288,0.000383083,0.0003335489,0.0003632724,0.0005881929,0.0002241895,0.01075673],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003037529,"threshold_uncertainty_score":0.01606417,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01729990900231733,"score_gpt":0.2377535723818951,"score_spread":0.2204536633795777,"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."}}