{"id":"W4248042746","doi":"10.23952/jano.3.2021.2.07","title":"A posteriori error control for variational inequalities with linear constraints in an abstract framework","year":2021,"lang":"en","type":"article","venue":"Journal of Applied and Numerical Optimization","topic":"Optimization and Variational Analysis","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"A priori and a posteriori; Variational inequality; Mathematics; Inequality; Control (management); Applied mathematics; Mathematical optimization; Computer science; Mathematical analysis; Artificial intelligence; Philosophy; Epistemology","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":[0.0003359056,0.00009687485,0.0002529704,0.0001226142,0.00006624996,0.0001312438,0.0001200842,0.00007061652,0.00006358344],"category_scores_gemma":[0.0001036531,0.00007834046,0.00004393966,0.0003226849,0.00002887335,0.0004391537,0.00001575243,0.0001194314,4.41505e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002711296,"about_ca_system_score_gemma":0.0001954086,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001285697,"about_ca_topic_score_gemma":7.104245e-7,"domain_scores_codex":[0.9989992,0.00004146763,0.0004388448,0.000167291,0.0002402988,0.0001128421],"domain_scores_gemma":[0.998705,0.0003017827,0.0003326817,0.00008882942,0.0004755063,0.00009623734],"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":[0.0001801739,0.0001463866,0.0004129387,0.000009230017,0.00004446633,0.000006214255,0.0004296563,0.8908691,0.0000835304,0.1061578,0.000004643393,0.001655863],"study_design_scores_gemma":[0.001434907,0.0001548632,0.003423084,0.00002415639,0.00002811348,0.00002672157,0.0001355168,0.9899542,0.0000380431,0.004617071,0.00004545966,0.0001179021],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001533994,0.00001875717,0.9966098,0.001544346,0.00005653864,0.00008905384,0.000008601471,0.000009878502,0.000129039],"genre_scores_gemma":[0.4988254,0.000008143523,0.5005609,0.0005158223,0.00006389698,0.000004165893,0.00001322572,0.000004352712,0.000004170263],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4972914,"threshold_uncertainty_score":0.3194631,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01638038327225282,"score_gpt":0.2677829618036052,"score_spread":0.2514025785313524,"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."}}