{"id":"W2091482787","doi":"10.1002/cnm.611","title":"Analysis of a projection/characteristic scheme for incompressible flow","year":2003,"lang":"en","type":"article","venue":"Communications in 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 Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Centre National de la Recherche Scientifique","keywords":"Projection method; Advection; Compressibility; Mathematics; Convergence (economics); Scheme (mathematics); Vector field; Projection (relational algebra); Applied mathematics; Divergence (linguistics); Incompressible flow; Inversion (geology); Rate of convergence; Grid; Numerical analysis; Mathematical analysis; Flow (mathematics); Geometry; Mathematical optimization; Computer science; Algorithm; Mechanics; Physics; Dykstra's projection algorithm; Geology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001127504,0.0001976611,0.0006424308,0.0009362875,0.00003280545,0.00001281902,0.0005205443,0.0001011629,0.00001198232],"category_scores_gemma":[0.003696573,0.0002269902,0.0001407584,0.003635074,0.00005444063,0.000116162,0.00008856583,0.0003737161,7.41691e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001927887,"about_ca_system_score_gemma":0.00002130089,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002955853,"about_ca_topic_score_gemma":0.000001506453,"domain_scores_codex":[0.9982878,0.0002828889,0.0008375957,0.0002045407,0.0001170789,0.0002700995],"domain_scores_gemma":[0.9936038,0.005271068,0.00009796262,0.0008938062,0.00007860646,0.00005473325],"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":[0.000004156699,0.0001130023,0.001253294,0.0001963641,0.0001431755,2.904061e-7,0.0002165691,0.9664726,0.002668755,0.007364499,0.000002111702,0.02156516],"study_design_scores_gemma":[0.0002145143,0.00001568438,0.001833274,0.00007948354,0.00006713695,0.000001544608,0.00003965261,0.9897754,0.00101839,0.005521815,0.001220904,0.0002121817],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001188227,0.0006403259,0.9969644,0.00001917918,0.0001838689,0.0003594795,0.00001283739,0.0001367976,0.0004949149],"genre_scores_gemma":[0.08953783,0.00008666333,0.9098426,0.000009495457,0.000008343977,0.000455467,0.00001192775,0.00004390107,0.000003738723],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0883496,"threshold_uncertainty_score":0.9256394,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07273513641189046,"score_gpt":0.4240712783667279,"score_spread":0.3513361419548375,"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."}}