{"id":"W2997080587","doi":"10.1016/j.jocs.2019.101063","title":"An efficient and high accuracy finite-difference scheme for the acoustic wave equation in 3D heterogeneous media","year":2019,"lang":"en","type":"article","venue":"Journal of Computational Science","topic":"Electromagnetic Simulation and Numerical Methods","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Richardson extrapolation; Extrapolation; Acoustic wave equation; Wave equation; Scheme (mathematics); Stability (learning theory); Grid; Mathematics; Boundary (topology); Central differencing scheme; Finite difference method; Finite difference; Applied mathematics; Runge–Kutta methods; Mathematical analysis; Algorithm; Acoustic wave; Computer science; Numerical analysis; Finite difference coefficient; Geometry; Acoustics; Finite element method; Physics","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":[0.000635638,0.00005747675,0.0000944503,0.0001109437,0.00005407767,0.00005316086,0.0001388736,0.00001853303,0.00002020304],"category_scores_gemma":[0.0005367025,0.00004006772,0.00001778231,0.0002854712,0.00006710643,0.0001027761,0.00001067102,0.00009142549,0.000001313156],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004737377,"about_ca_system_score_gemma":0.00006356848,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001434897,"about_ca_topic_score_gemma":6.804826e-7,"domain_scores_codex":[0.9992442,0.00002708898,0.0002266645,0.00008126329,0.0002965425,0.0001242268],"domain_scores_gemma":[0.9966632,0.002980731,0.00008037652,0.00005372569,0.000159796,0.00006214007],"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":[0.00001199196,0.00001298971,0.0002133308,0.000004834199,0.000002039077,4.744506e-7,0.0001633405,0.9691008,0.009987028,0.0002446941,8.400756e-7,0.02025761],"study_design_scores_gemma":[0.0002938118,0.0001401545,0.0784788,0.00001152252,0.000003683736,0.00001423544,0.00001523442,0.9179735,0.0003600719,0.002655757,0.000005782721,0.00004751052],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5826105,0.00006584858,0.4170312,0.00005842337,0.0001551705,0.00006824971,7.308656e-7,0.000004661276,0.000005257816],"genre_scores_gemma":[0.9279956,0.000007195443,0.07188994,0.00006121648,0.00003864169,0.000001687191,6.434645e-7,0.000003860358,0.00000125354],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3453851,"threshold_uncertainty_score":0.1633914,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02574013335362535,"score_gpt":0.2953663739808894,"score_spread":0.2696262406272641,"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."}}