{"id":"W2154063054","doi":"10.1142/s0218202511005052","title":"ENERGY NORM <i>A POSTERIORI</i> ERROR ESTIMATION FOR hp-ADAPTIVE DISCONTINUOUS GALERKIN METHODS FOR ELLIPTIC PROBLEMS IN THREE DIMENSIONS","year":2010,"lang":"en","type":"article","venue":"Mathematical Models and Methods in Applied Sciences","topic":"Advanced Numerical Methods in Computational Mathematics","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Discontinuous Galerkin method; Mathematics; Norm (philosophy); Applied mathematics; Degree of a polynomial; Polygon mesh; Adaptive mesh refinement; Hexahedron; Elliptic curve; A priori and a posteriori; Galerkin method; Finite element method; Mathematical optimization; Polynomial; Mathematical analysis; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00286604,0.0006811974,0.0004696046,0.0008574655,0.0003070786,0.000813603,0.0009330902,0.0008514232,0.0005829312],"category_scores_gemma":[0.006785808,0.0003149979,0.0006020083,0.0004094408,0.00171355,0.001076306,0.00147715,0.001281557,0.0001916644],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005646844,"about_ca_system_score_gemma":0.0005367075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009099998,"about_ca_topic_score_gemma":0.0006185365,"domain_scores_codex":[0.9992179,0.0003159615,0.00005091143,0.00007432285,0.0003124028,0.00002855624],"domain_scores_gemma":[0.9975778,0.001234333,0.0003508912,0.0003212242,0.0004488233,0.00006688592],"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":[0.0001214813,0.00007704103,0.003424288,0.000392721,0.00006462823,0.0001290619,0.000269459,0.7122136,0.03911252,0.1413361,0.000896214,0.1019629],"study_design_scores_gemma":[0.000002269736,0.00001560018,0.0001902489,0.00001052607,0.000003465046,0.00002131225,0.00000704094,0.9848343,0.005694072,0.008864339,0.0003473047,0.000009552547],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01304214,0.0001286683,0.9861745,0.00007685447,0.00001492338,0.00001313981,0.00001064782,0.0000478635,0.0004912418],"genre_scores_gemma":[0.4102936,0.0004330775,0.5873767,0.00008284348,0.00005693504,0.0001451542,0.00008473902,0.0001748177,0.001352209],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00286604,"threshold_uncertainty_score":0.01515728,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07753093081010841,"score_gpt":0.397978201428666,"score_spread":0.3204472706185576,"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."}}