{"id":"W2965950041","doi":"10.1029/2018jb017020","title":"Three‐Dimensional Sensitivity Kernels for Multicomponent Empirical Green's Functions From Ambient Noise: Methodology and Application to Adjoint Tomography","year":2019,"lang":"en","type":"article","venue":"Journal of Geophysical Research Solid Earth","topic":"Seismic Waves and Analysis","field":"Earth and Planetary Sciences","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Australian Research Council; Medical Research Council Canada; Macquarie University","keywords":"Tomography; Ambient noise level; Sensitivity (control systems); Noise (video); Kernel (algebra); Isotropy; Rotation (mathematics); Transverse plane; Acoustics; Physics; Mathematical analysis; Mathematics; Computer science; Geometry; Optics; Artificial intelligence","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":[0.001823359,0.0001379726,0.0004693661,0.000305518,0.0001933509,0.0000606152,0.0001392037,0.00007781012,0.0001938815],"category_scores_gemma":[0.0002324555,0.0001009232,0.0002668615,0.000428792,0.0001386099,0.0001455399,0.00005435058,0.000448036,0.0002157266],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009106847,"about_ca_system_score_gemma":0.00008619375,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007719881,"about_ca_topic_score_gemma":0.001147218,"domain_scores_codex":[0.9976408,0.0004015437,0.000371449,0.0003546442,0.0007944523,0.0004370528],"domain_scores_gemma":[0.9960412,0.002673397,0.0001361094,0.0002178103,0.0004495398,0.0004818956],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.005341187,0.001161432,0.4858691,0.0001243992,0.001344398,0.000119119,0.0008312907,0.0668818,0.1679174,0.0003215942,0.005019489,0.2650688],"study_design_scores_gemma":[0.000464999,0.0008259228,0.7712282,0.0000233614,0.00003936223,0.0000121085,0.0001092736,0.219413,0.0004607752,0.004664157,0.00264435,0.0001145116],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9674671,0.00007839948,0.0287542,0.003008617,0.0001522925,0.0003373518,0.0001715224,0.000006774907,0.00002379854],"genre_scores_gemma":[0.9897923,0.00001202142,0.009118127,0.0003803843,0.0005411892,0.000002334433,0.00005122635,0.000005506045,0.00009685714],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2853591,"threshold_uncertainty_score":0.9988878,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07410705980697274,"score_gpt":0.3568740885377709,"score_spread":0.2827670287307981,"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."}}