{"id":"W4249168025","doi":"10.1190/1.3064092","title":"Bayesian ground‐roll separation by curvelet‐domain sparsity promotion","year":2008,"lang":"en","type":"article","venue":"","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Reflector (photography); Curvelet; Noise (video); Bayesian probability; Set (abstract data type); Separation (statistics); Computer science; Geology; Domain (mathematical analysis); Field (mathematics); Surface wave; SIGNAL (programming language); Surface (topology); Acoustics; Remote sensing; Artificial intelligence; Computer vision; Optics; Wavelet transform; Image (mathematics); Wavelet; Telecommunications; Physics; Mathematics; Geometry; Machine learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001067995,0.0004463707,0.0005015862,0.0005362587,0.0002389642,0.0006243611,0.0005153335,0.0005937945,0.001644417],"category_scores_gemma":[0.004089043,0.0003047051,0.0003087049,0.0006958075,0.0007413611,0.001304446,0.001044225,0.001115872,0.0005778138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002708466,"about_ca_system_score_gemma":0.0006784646,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001205445,"about_ca_topic_score_gemma":0.001291074,"domain_scores_codex":[0.9996164,0.0001071196,0.00001666079,0.00006200049,0.0001555069,0.00004222224],"domain_scores_gemma":[0.9981142,0.0009355173,0.0002082839,0.0002921186,0.0003617525,0.00008804101],"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":[0.0004793427,0.000212381,0.001993756,0.0001214478,0.00006394491,0.0001301468,0.0001885741,0.4372442,0.06734672,0.09880278,0.005627153,0.3877896],"study_design_scores_gemma":[0.00001640246,0.0000372172,0.0003353832,0.000005419108,0.000005575177,0.00003841172,0.0000101405,0.9795223,0.007094481,0.01149356,0.001430957,0.00001014613],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01942459,0.00007167147,0.9788831,0.0001488825,0.00001498202,0.00001461902,0.0000507861,0.0001116918,0.001279817],"genre_scores_gemma":[0.4319811,0.0003764363,0.5616468,0.0001300233,0.000146641,0.00008467931,0.000629666,0.0001238946,0.004880637],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001644417,"threshold_uncertainty_score":0.005648196,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01934105489867947,"score_gpt":0.2241190371650875,"score_spread":0.204777982266408,"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."}}