{"id":"W2989106239","doi":"10.1137/18m121914x","title":"A Mass-Conservative Temporal Second Order and Spatial Fourth Order Characteristic Finite Volume Method for Atmospheric Pollution Advection Diffusion Problems","year":2019,"lang":"en","type":"article","venue":"SIAM Journal on Scientific Computing","topic":"Wind and Air Flow Studies","field":"Environmental Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Shandong Province; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Advection; Finite volume method; Discretization; Interpolation (computer graphics); Mathematics; Diffusion; Temporal discretization; Applied mathematics; Meteorology; Mathematical analysis; Mechanics; Computer science; Physics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005150762,0.0005861776,0.0006379298,0.0005024996,0.0006249579,0.0006469696,0.001508321,0.001084392,0.001469141],"category_scores_gemma":[0.001139963,0.0003423543,0.0009725856,0.0004585102,0.0007263076,0.0008527511,0.0009370944,0.001250514,0.0002711484],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007080279,"about_ca_system_score_gemma":0.001766555,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009643639,"about_ca_topic_score_gemma":0.006009326,"domain_scores_codex":[0.999763,0.00005455948,0.00001455919,0.00003328133,0.0001069047,0.00002776193],"domain_scores_gemma":[0.9996177,0.0001602609,0.00004316696,0.00002827073,0.0001207088,0.00002989467],"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":[0.0000711307,0.00004891638,0.001687563,0.000213112,0.00003807402,0.0001873512,0.0001734516,0.8924241,0.01260947,0.03423821,0.001561582,0.05674705],"study_design_scores_gemma":[0.000006766198,0.00001101118,0.00005041394,0.000005078824,0.000002443915,0.00001843709,0.000006623013,0.9965185,0.0005620345,0.001555928,0.001257705,0.000005009841],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008503343,0.0002994213,0.9884402,0.0001360808,0.00008420875,0.00005049334,0.00003675546,0.0001664953,0.002282955],"genre_scores_gemma":[0.3552109,0.0008222589,0.6336591,0.0002196102,0.0001548789,0.0005121038,0.000255538,0.0002840456,0.008881522],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009643639,"threshold_uncertainty_score":0.01917505,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01135931656728975,"score_gpt":0.2414831566279344,"score_spread":0.2301238400606447,"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."}}