{"id":"W2088855121","doi":"10.1007/s10898-004-2692-9","title":"Global Stability Results for the Weak Vector Variational Inequality","year":2005,"lang":"en","type":"article","venue":"Journal of Global Optimization","topic":"Optimization and Variational Analysis","field":"Computer Science","cited_by":103,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"National Natural Science Foundation of China","keywords":"Mathematics; Variational inequality; Stability (learning theory); Euclidean space; Applied mathematics; Euclidean geometry; Inequality; Solution set; Set (abstract data type); Vector optimization; Mathematical analysis; Vector space; Space (punctuation); Mathematical optimization; Pure mathematics; Optimization problem; Geometry","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":[0.001961141,0.0001295672,0.00020596,0.00003462048,0.000222462,0.0002348718,0.0006649275,0.00007576338,0.00004544799],"category_scores_gemma":[0.001348666,0.00009101034,0.0002178757,0.0009510891,0.00002990464,0.001043245,0.00007664057,0.00007039268,0.00000428814],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005568539,"about_ca_system_score_gemma":0.00037802,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001744131,"about_ca_topic_score_gemma":0.00003945833,"domain_scores_codex":[0.9979175,0.0001975939,0.0008848284,0.0002120461,0.0006152854,0.0001727661],"domain_scores_gemma":[0.9968565,0.000396089,0.0008469599,0.0002905486,0.00150433,0.000105543],"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":[0.0001569915,0.0001228105,0.001222339,0.000002644636,0.00007481151,2.038285e-7,0.00004068982,0.8899126,9.116433e-7,0.1047362,0.00242778,0.001301986],"study_design_scores_gemma":[0.001142049,0.00008069861,0.02287241,0.000005685994,0.00005854732,0.00001687997,0.000017244,0.9704631,0.000004866311,0.001871719,0.003366821,0.00009998012],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001656824,0.0000948843,0.9704823,0.02758462,0.0004438075,0.0001674996,0.0001294728,0.00002761484,0.0009041344],"genre_scores_gemma":[0.4213256,0.00003644578,0.5773873,0.0006853592,0.0005026707,0.000005075303,0.00002507986,0.00000336419,0.00002905068],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4211599,"threshold_uncertainty_score":0.3711294,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02314939605882728,"score_gpt":0.2857274072162192,"score_spread":0.2625780111573919,"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."}}