{"id":"W4210378622","doi":"10.23952/asvao.4.2022.1.04","title":"A modified inertial projection and contraction method for solving bilevel split variational inequality problems","year":2022,"lang":"en","type":"article","venue":"Applied Set-Valued Analysis and Optimization","topic":"Contact Mechanics and Variational Inequalities","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Variational inequality; Contraction (grammar); Inertial frame of reference; Mathematics; Inequality; Projection method; Applied mathematics; Mathematical analysis; Mathematical optimization; Classical mechanics; Physics; Dykstra's projection algorithm; Medicine","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":[0.001804089,0.0001570827,0.0002928044,0.0003907266,0.0007099179,0.0002758998,0.0001386493,0.0000638358,0.00002863139],"category_scores_gemma":[0.00006242022,0.0001600858,0.00008488101,0.0009051309,0.000005905213,0.0003400888,0.0001627131,0.0001171288,1.452846e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009395262,"about_ca_system_score_gemma":0.00008303736,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004313953,"about_ca_topic_score_gemma":0.00003407638,"domain_scores_codex":[0.9983415,0.0001422817,0.0004513426,0.000528472,0.0003534956,0.0001828798],"domain_scores_gemma":[0.9989681,0.00025643,0.0003411376,0.0001930526,0.0001830856,0.00005816819],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003492986,0.00004162898,0.00003568191,0.00001538121,0.000191866,7.415819e-8,0.0006752465,0.4731646,0.0007645243,0.5221313,0.000008120135,0.002936595],"study_design_scores_gemma":[0.0007338824,0.00006673249,0.0007678612,0.000001893852,0.0002744609,0.000002311542,0.0001373418,0.9824957,0.00007931474,0.01515713,0.0000859654,0.0001973923],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002358322,0.00003100906,0.9963039,0.000406578,0.0001022094,0.0005902527,0.00006382583,0.00007921096,0.00006467667],"genre_scores_gemma":[0.6609846,0.00001475442,0.3377351,0.0002145684,0.0000659724,0.0004971886,0.00042555,0.00000958795,0.00005266756],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6586263,"threshold_uncertainty_score":0.6528108,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03919089083651029,"score_gpt":0.2891342411408889,"score_spread":0.2499433503043786,"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."}}