{"id":"W2289379992","doi":"10.1002/nme.5229","title":"Three‐dimensional superconvergent gradient recovery on tetrahedral meshes","year":2016,"lang":"en","type":"article","venue":"International Journal for Numerical Methods in Engineering","topic":"Advanced Numerical Methods in Computational Mathematics","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"China Postdoctoral Science Foundation; Natural Science Foundation of Shaanxi Province; National Natural Science Foundation of China","keywords":"Superconvergence; Finite element method; Bounded function; Mathematics; Polygon mesh; Delaunay triangulation; Laplace operator; Norm (philosophy); Domain (mathematical analysis); Tetrahedron; Boundary (topology); Mathematical analysis; Dirichlet boundary condition; Geometry; Applied mathematics; Structural engineering; Engineering","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":[0.0002687899,0.000197984,0.0003159193,0.0002852312,0.0002159142,0.0005663597,0.000488643,0.0003804762,0.001103675],"category_scores_gemma":[0.00115444,0.0001465175,0.0002416142,0.0002176324,0.0006895212,0.0003992827,0.0006222504,0.0003566876,0.0002364997],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002531047,"about_ca_system_score_gemma":0.000323291,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002137109,"about_ca_topic_score_gemma":0.001426037,"domain_scores_codex":[0.9998411,0.00003569642,0.000007238832,0.00002017523,0.00007979687,0.00001598467],"domain_scores_gemma":[0.999645,0.0001138421,0.00003875757,0.00009652202,0.00008486473,0.00002100872],"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":[0.0000835663,0.00002787888,0.001763156,0.00009142904,0.00002119725,0.0003489545,0.0003252856,0.8570973,0.02388908,0.03678947,0.001730483,0.07783227],"study_design_scores_gemma":[0.000002931917,0.000007596131,0.0001404254,0.000003981984,9.464664e-7,0.00005032215,0.00002485979,0.9913657,0.00352006,0.003407314,0.001471688,0.000004126481],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1836591,0.0002120256,0.8069933,0.0002102394,0.00005678317,0.00004162821,0.00006107926,0.0004184053,0.008347542],"genre_scores_gemma":[0.8844068,0.0001061238,0.1126325,0.00004021792,0.00001160235,0.00003136276,0.0001115753,0.0001189452,0.002540843],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002137109,"threshold_uncertainty_score":0.004249394,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03821346753291645,"score_gpt":0.3687909678417913,"score_spread":0.3305775003088749,"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."}}