{"id":"W2027671573","doi":"10.2136/vzj2006.0076","title":"Efficient Schemes for Reducing Numerical Dispersion in Modeling Multiphase Transport through Heterogeneous Geological Media","year":2008,"lang":"en","type":"article","venue":"Vadose Zone Journal","topic":"Groundwater flow and contamination studies","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"U.S. Department of Energy","keywords":"Discretization; Grid; Flux limiter; Total variation diminishing; Multiphase flow; Vadose zone; Dispersion (optics); Computer science; Computer simulation; Atmospheric dispersion modeling; Flow (mathematics); Computational science; Mathematical optimization; Applied mathematics; Algorithm; Mechanics; Geology; Simulation; Mathematics; Geotechnical engineering; Geometry; Groundwater; Physics; Mathematical analysis","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":[0.001418932,0.0004071504,0.0004195297,0.0005534323,0.0004937095,0.000549929,0.001078309,0.0008739879,0.0005710759],"category_scores_gemma":[0.004395927,0.0002689679,0.0005053325,0.0005656295,0.000869056,0.0008596641,0.0009222931,0.000870932,0.0001420754],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008374594,"about_ca_system_score_gemma":0.0008526272,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004522923,"about_ca_topic_score_gemma":0.00453383,"domain_scores_codex":[0.9996808,0.0001257623,0.00002545207,0.00002026045,0.0001215168,0.00002616017],"domain_scores_gemma":[0.99866,0.000742776,0.0001522571,0.0002128301,0.0001911826,0.00004095545],"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":[0.00006101732,0.00006539632,0.001622077,0.00007564239,0.00001835005,0.00006166743,0.0001616674,0.9231583,0.008231783,0.0357238,0.0003240698,0.03049614],"study_design_scores_gemma":[0.00001028525,0.00001501327,0.00007411467,0.000004404114,0.000002355249,0.000005799565,0.00000620735,0.9959686,0.001310603,0.002153852,0.000445401,0.000003414679],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08295146,0.0004964308,0.9134356,0.0002274149,0.00007218636,0.0001111803,0.00006934472,0.0002432592,0.002393086],"genre_scores_gemma":[0.475284,0.0004626312,0.5218284,0.00006990151,0.00003236788,0.0002634684,0.0001074369,0.00007650749,0.001875213],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004522923,"threshold_uncertainty_score":0.008993208,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03529311615687632,"score_gpt":0.2516329996654544,"score_spread":0.2163398835085781,"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."}}