{"id":"W2061318403","doi":"10.1002/fld.1118","title":"MPDATA error estimator for mesh adaptivity","year":2005,"lang":"en","type":"article","venue":"International Journal for Numerical Methods in Fluids","topic":"Computational Fluid Dynamics and Aerodynamics","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Polygon mesh; Solver; Robustness (evolution); Computer science; Adaptive mesh refinement; Benchmark (surveying); Estimator; Euler equations; Algorithm; Mathematical optimization; Mesh generation; Compressible flow; Applied mathematics; Computational science; Mathematics; Compressibility; Finite element method; Geology","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":[0.008241237,0.001132424,0.0007321855,0.002038292,0.0004344124,0.00184554,0.001735243,0.001566045,0.002258141],"category_scores_gemma":[0.04713091,0.0004833561,0.000768599,0.0007813728,0.001793215,0.002408989,0.003763754,0.002298309,0.0007344272],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008338387,"about_ca_system_score_gemma":0.0007865236,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009418292,"about_ca_topic_score_gemma":0.0004540073,"domain_scores_codex":[0.9958326,0.001194962,0.0003723571,0.0005772532,0.001883963,0.0001388102],"domain_scores_gemma":[0.9688603,0.01549974,0.002788851,0.006057707,0.006356461,0.0004368953],"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":[0.0009633557,0.0002370908,0.01941626,0.0006251207,0.0002090941,0.0001790934,0.0002274524,0.5746459,0.02598618,0.09358024,0.004249141,0.2796811],"study_design_scores_gemma":[0.00002619887,0.00009860127,0.001656329,0.00003787963,0.00001387905,0.00008990111,0.00001887842,0.9671556,0.01843812,0.01014816,0.00228779,0.00002868804],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02136097,0.0002324324,0.9756238,0.0001505898,0.0001432095,0.00008091784,0.0001558659,0.0008943191,0.001357777],"genre_scores_gemma":[0.5607941,0.0001979785,0.4330963,0.0002197544,0.0001740895,0.0004391691,0.0009273345,0.00056854,0.003582693],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008241237,"threshold_uncertainty_score":0.04358435,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03926859276660935,"score_gpt":0.4128745059081287,"score_spread":0.3736059131415194,"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."}}