{"id":"W2512144736","doi":"10.1002/2016jb013165","title":"Numerical upscaling in 2‐D heterogeneous poroelastic rocks: Anisotropic attenuation and dispersion of seismic waves","year":2016,"lang":"en","type":"article","venue":"Journal of Geophysical Research Solid Earth","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":84,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Université de Lausanne; Kommission für Technologie und Innovation; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Poromechanics; Attenuation; Anelastic attenuation factor; Anisotropy; Seismic anisotropy; Seismic wave; Dissipation; Geology; Dispersive body waves; Dispersion (optics); Mechanics; Wave propagation; Stiffness matrix; Porous medium; Geophysics; Stiffness; Geotechnical engineering; Porosity; Physics; Optics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006257836,0.00008941458,0.0002665593,0.0002747223,0.00007401474,0.00002640181,0.0001711213,0.00004250534,0.0001239628],"category_scores_gemma":[0.0004120381,0.00005417793,0.00007444748,0.0002438375,0.0002030764,0.0002826271,0.0000242584,0.0002824315,0.00003125447],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001242014,"about_ca_system_score_gemma":0.00008375503,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001161435,"about_ca_topic_score_gemma":0.00001242188,"domain_scores_codex":[0.998282,0.0002465332,0.0003531475,0.0001523387,0.0006503399,0.0003156062],"domain_scores_gemma":[0.9987804,0.0006264374,0.0001388302,0.000103634,0.0001843443,0.000166402],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0007356,0.0002491723,0.3418832,0.0001098629,0.00005773107,0.000165957,0.000627191,0.003706193,0.0180827,0.00004464659,0.001003767,0.633334],"study_design_scores_gemma":[0.001984298,0.003931571,0.6480645,0.001135826,0.00002390365,0.0002342678,0.000381006,0.2799137,0.05045867,0.01098674,0.002539734,0.0003457879],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9959432,0.0001879136,0.0026358,0.0009804559,0.00006945049,0.0000666192,0.000006339744,0.000007024873,0.0001031199],"genre_scores_gemma":[0.9989426,0.0004130617,0.0003553303,0.00007366089,0.0001335697,2.046898e-7,0.000001457267,0.000003604521,0.00007650089],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6329882,"threshold_uncertainty_score":0.2209312,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02456199599417013,"score_gpt":0.2914658620021334,"score_spread":0.2669038660079633,"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."}}