{"id":"W2053131509","doi":"10.1016/s0045-7825(02)00393-6","title":"A mixed finite element method for a Ladyzhenskaya model","year":2002,"lang":"en","type":"article","venue":"Computer Methods in Applied Mechanics and Engineering","topic":"Advanced Numerical Methods in Computational Mathematics","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Uniqueness; Finite element method; Mathematics; Compressibility; Applied mathematics; Mixed finite element method; Element (criminal law); Flow (mathematics); Extended finite element method; Mathematical analysis; Approximation error; Calculus (dental); Geometry; Mechanics; Physics; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008587923,0.000294991,0.0004425588,0.0002011827,0.00003852011,0.00003474132,0.0001742752,0.0001144992,0.000005062615],"category_scores_gemma":[0.0000860264,0.0003245144,0.00006650187,0.0002735925,0.000005038131,0.00004573529,0.0001057617,0.0002514499,0.000001547226],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007401711,"about_ca_system_score_gemma":0.000003187283,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":1.458035e-7,"about_ca_topic_score_gemma":1.073922e-7,"domain_scores_codex":[0.9986687,0.00003362804,0.0004541533,0.0003269451,0.0001231552,0.0003934506],"domain_scores_gemma":[0.9978997,0.001693039,0.00004391189,0.0002274115,0.0000252095,0.0001107137],"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":[0.000001879876,0.00001332609,4.655103e-8,0.0001727062,0.00001993789,6.355929e-7,0.0001484068,0.6705927,0.002061357,0.1162333,0.00003880804,0.2107169],"study_design_scores_gemma":[0.0003948255,0.00002235989,4.920641e-7,0.00003693732,0.00001451363,0.000004116291,0.000009581309,0.8485572,0.001232086,0.1477434,0.001686497,0.0002979396],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00002472944,0.0002887702,0.998272,0.00002673921,0.0004845627,0.0004326435,0.000007458314,0.0003233757,0.0001397126],"genre_scores_gemma":[0.001620933,0.00008769979,0.9977076,0.00009696349,0.0001051084,0.0002791023,0.00000278881,0.00009278165,0.000006998827],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.210419,"threshold_uncertainty_score":0.9999207,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05977098565159857,"score_gpt":0.319365276812684,"score_spread":0.2595942911610855,"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."}}