{"id":"W2302277568","doi":"10.1016/j.cam.2016.01.050","title":"Adaptive mixed-hybrid and penalty discontinuous Galerkin method for two-phase flow in heterogeneous media","year":2016,"lang":"en","type":"article","venue":"Journal of Computational and Applied Mathematics","topic":"Advanced Numerical Methods in Computational Mathematics","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Fundamental Research Funds for the Central Universities; China Postdoctoral Science Foundation; Natural Science Foundation of Shaanxi Province; National Natural Science Foundation of China","keywords":"Mathematics; Discontinuous Galerkin method; Capillary pressure; Finite element method; Two-phase flow; Conservation of mass; Mathematical analysis; Galerkin method; Flow (mathematics); Mechanics; Geometry; Porous medium; Materials science; Physics","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":[],"consensus_categories":[],"category_scores_codex":[0.0005320226,0.0001886193,0.0004757993,0.0001384345,0.00003734791,0.0000251943,0.00009910526,0.00003583703,0.000006145399],"category_scores_gemma":[0.0001906174,0.0001319575,0.00006356618,0.00008488706,0.00006611985,0.00009741757,0.00003364965,0.0001174853,0.000001150616],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004476339,"about_ca_system_score_gemma":0.00002256969,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.165694e-8,"about_ca_topic_score_gemma":4.942124e-7,"domain_scores_codex":[0.9987141,0.00002682501,0.0007027744,0.000126269,0.000258218,0.0001717571],"domain_scores_gemma":[0.9947996,0.00464619,0.0002611823,0.00006213749,0.0001168712,0.0001140554],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001217517,0.0002120115,0.000003153143,0.0003006009,0.0001195347,0.00001871814,0.0006542573,0.6327811,0.001819567,0.03331776,0.0001034281,0.330548],"study_design_scores_gemma":[0.00172886,0.00007364093,0.00002001157,0.0001203327,0.00002646549,0.0002497894,0.00007304707,0.4713136,0.001134849,0.5250859,0.0000452723,0.0001282828],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05496839,0.0001881289,0.944303,0.00008364404,0.0001113235,0.0002127625,0.00004593232,0.0000220168,0.00006479266],"genre_scores_gemma":[0.2610462,0.00001866153,0.7387725,0.00002257813,0.00009574919,0.00001388518,0.000001744793,0.0000259038,0.000002861893],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4917681,"threshold_uncertainty_score":0.538107,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02451173472834479,"score_gpt":0.3133086066945078,"score_spread":0.288796871966163,"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."}}