{"id":"W2166525224","doi":"10.1190/1.1649391","title":"Efficient waveform inversion and imaging: A strategy for selecting temporal frequencies","year":2003,"lang":"en","type":"article","venue":"Geophysics","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":1029,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; Geological Survey of Canada","funders":"","keywords":"Aliasing; Discretization; Algorithm; Frequency domain; Computer science; Inversion (geology); Wavenumber; Offset (computer science); Redundancy (engineering); Geophysical imaging; Geology; Mathematics; Undersampling; Geophysics; Optics; Mathematical analysis; Seismology; Physics; Artificial intelligence; Computer vision","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":[0.0002002822,0.0001060626,0.0001021576,0.00004719747,0.0002424233,0.00005803764,0.00006657834,0.00002717957,0.00004582622],"category_scores_gemma":[0.00003336451,0.0000894986,0.00003868411,0.0001308754,0.00006608092,0.00009609988,0.000005212034,0.00008266158,0.0000181355],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006499596,"about_ca_system_score_gemma":0.0000505603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001673563,"about_ca_topic_score_gemma":0.00001148032,"domain_scores_codex":[0.9993117,0.00002241573,0.000107959,0.0001944202,0.0001140447,0.0002494636],"domain_scores_gemma":[0.9996807,0.00006009794,0.0000535978,0.00009231843,0.00004755988,0.00006574122],"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":[0.00008218452,0.0001043583,0.3409345,0.0002587718,0.00005275037,0.00001575046,0.002280141,0.003943088,0.002523854,0.006636,0.03296104,0.6102076],"study_design_scores_gemma":[0.001078613,0.0004665255,0.01382806,0.00007420996,0.00005081245,0.0000573766,0.003867406,0.8377916,0.01777468,0.0559196,0.06823295,0.0008581199],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.985772,0.0003134985,0.003213283,0.0002294133,0.0002090742,0.000235777,0.00002932683,0.0001560981,0.009841556],"genre_scores_gemma":[0.9943271,0.000008667833,0.004566949,0.0008539507,0.000039957,0.00000127846,0.0000321107,0.000003685466,0.0001663143],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8338485,"threshold_uncertainty_score":0.3649647,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01759775483382638,"score_gpt":0.2174872219081115,"score_spread":0.1998894670742851,"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."}}