{"id":"W2016755848","doi":"10.1111/j.1365-246x.2009.04429.x","title":"Seismic tomography of the southern California crust based on spectral-element and adjoint methods","year":2009,"lang":"en","type":"article","venue":"Geophysical Journal International","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":373,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Geology; Seismogram; Multitaper; Seismology; Seismic tomography; Tomography; Inversion (geology); Sedimentary basin; Algorithm; Geophysics; Mantle (geology); Mathematics; Physics","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":[0.0002884275,0.00008836987,0.0001033135,0.00009435301,0.0001013706,0.00004972832,0.0002398744,0.00002450608,0.0006287158],"category_scores_gemma":[0.00002945466,0.00005330171,0.000132154,0.00009735446,0.00007858725,0.0000547324,0.00000829042,0.000239872,0.00002778022],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007395505,"about_ca_system_score_gemma":0.00002614218,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001979763,"about_ca_topic_score_gemma":0.000001970749,"domain_scores_codex":[0.9991311,0.0001000936,0.0001907941,0.0001135639,0.0003363114,0.0001281688],"domain_scores_gemma":[0.9995853,0.0000742213,0.0001248119,0.00009058505,0.00005597829,0.0000690555],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002951458,0.0003023681,0.08109239,0.000009615352,0.0001066143,0.0000156418,0.000302985,0.008530525,0.001916594,0.0005982573,0.01911358,0.8877163],"study_design_scores_gemma":[0.0005734974,0.0004788319,0.2765773,0.0001133706,0.00002914773,0.00005886111,0.0001627625,0.656675,0.00972392,0.02873531,0.02666961,0.0002023667],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9296655,0.0001053163,0.02788898,0.0319672,0.0009408345,0.0001714329,0.0003172896,0.00005820451,0.008885215],"genre_scores_gemma":[0.9890038,0.00001073163,0.005936712,0.004763563,0.0001898169,1.742149e-7,0.000008180638,0.000001726288,0.00008534455],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8875139,"threshold_uncertainty_score":0.6883997,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01068642268481783,"score_gpt":0.2527177138205391,"score_spread":0.2420312911357212,"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."}}