{"id":"W2740900773","doi":"10.1093/gji/ggx323","title":"A gradient-based model parametrization using Bernstein polynomials in Bayesian inversion of surface wave dispersion","year":2017,"lang":"en","type":"article","venue":"Geophysical Journal International","topic":"Seismic Waves and Analysis","field":"Earth and Planetary Sciences","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; Natural Resources Canada; University of Calgary; University of Victoria","funders":"","keywords":"Parametrization (atmospheric modeling); Inversion (geology); Polynomial; Synthetic data; Nonlinear system; Mathematics; Autoregressive model; Mathematical analysis; Applied mathematics; Statistical physics; Geology; Algorithm; Physics; Statistics; Optics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002151232,0.00009746868,0.000184894,0.0001637403,0.0001997207,0.0001394971,0.0003153328,0.00004955378,0.0002163349],"category_scores_gemma":[0.00008990893,0.00008135747,0.000150991,0.00009839438,0.00007901884,0.0004256739,0.00002203339,0.0001557613,0.000009021842],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002713767,"about_ca_system_score_gemma":0.00006358577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002803028,"about_ca_topic_score_gemma":0.0002290252,"domain_scores_codex":[0.9989248,0.00004763193,0.0002952232,0.0001570151,0.0004103901,0.0001649464],"domain_scores_gemma":[0.9992462,0.00006259311,0.0003687596,0.0001377783,0.00008337574,0.0001013089],"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":[0.0001216111,0.00008380059,0.2065468,0.000005596597,0.00004421655,0.00002479239,0.00006876454,0.7834268,0.002847254,0.0001571594,0.00004941307,0.006623721],"study_design_scores_gemma":[0.0004476885,0.00003481884,0.08032622,0.00005941492,0.0000176482,0.000004538104,0.00004443502,0.9163131,0.0004701477,0.00218199,0.00001599846,0.00008406683],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9875611,0.00001984058,0.0109292,0.0007459396,0.0002790545,0.00003970248,0.00006453612,0.000003125518,0.0003574884],"genre_scores_gemma":[0.9969888,0.00002610709,0.00262032,0.00009143227,0.0001443514,4.461283e-8,0.00005235522,0.000002938756,0.00007368829],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1328862,"threshold_uncertainty_score":0.4237359,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03264697828192666,"score_gpt":0.2601995442460795,"score_spread":0.2275525659641529,"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."}}