{"id":"W2785546602","doi":"10.1190/geo2017-0562.1","title":"Ground-roll attenuation using curvelet downscaling","year":2018,"lang":"en","type":"article","venue":"Geophysics","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; Laurentian University; University of Sudbury","funders":"","keywords":"Curvelet; Attenuation; Reflection (computer programming); Geology; Acoustics; Function (biology); Computer science; Optics; Artificial intelligence; Physics; Wavelet transform","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":[0.0004535516,0.0006997488,0.000476606,0.0008084094,0.0002366637,0.0005601392,0.0005594215,0.0004618825,0.001904356],"category_scores_gemma":[0.001469092,0.0002491632,0.0003978608,0.0008435216,0.0003720862,0.0008300229,0.0006995822,0.0008879365,0.000877345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003046856,"about_ca_system_score_gemma":0.0005146666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001808414,"about_ca_topic_score_gemma":0.001788186,"domain_scores_codex":[0.9997466,0.00003684096,0.00001236239,0.00004862692,0.000124185,0.00003135637],"domain_scores_gemma":[0.9994479,0.0001135333,0.00008493721,0.0001192726,0.0002075368,0.00002682138],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002991582,0.000172634,0.002519767,0.000097002,0.00007253885,0.0001797667,0.0002006747,0.1881965,0.2159572,0.009303946,0.002198922,0.580802],"study_design_scores_gemma":[0.00001112527,0.00006190894,0.00159329,0.000007371312,0.0000177366,0.00007526988,0.00002035954,0.951891,0.04151291,0.001712406,0.00308008,0.00001640547],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05043854,0.00008274964,0.9471782,0.0001171741,0.00002830226,0.00002991214,0.00007107348,0.0006005327,0.001453607],"genre_scores_gemma":[0.3633564,0.00024979,0.6319299,0.0001037996,0.00007387938,0.00004781151,0.0005916731,0.0003673666,0.003279374],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001904356,"threshold_uncertainty_score":0.006370723,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02717449433139242,"score_gpt":0.2429563291734925,"score_spread":0.2157818348421001,"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."}}