{"id":"W1640851730","doi":"10.1111/1365-2478.12050","title":"Application of randomized sampling schemes to curvelet‐based sparsity‐promoting seismic data recovery","year":2013,"lang":"en","type":"article","venue":"Geophysical Prospecting","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Sampling (signal processing); Computer science; Compressed sensing; Algorithm; Separable space; Mathematics; 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":[0.0009354517,0.0001487586,0.0004349443,0.0000980805,0.0001781952,0.00008129136,0.0004743186,0.00004789226,0.000133043],"category_scores_gemma":[0.0006551972,0.0001279205,0.0000962401,0.0003381545,0.00008821153,0.000433288,0.00008320138,0.0001944053,0.0003059936],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008527708,"about_ca_system_score_gemma":0.00003273409,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01352817,"about_ca_topic_score_gemma":0.000008609292,"domain_scores_codex":[0.9984771,0.00009164909,0.0003913858,0.0004690932,0.0002695021,0.0003012823],"domain_scores_gemma":[0.9985121,0.0005315028,0.0002347204,0.0005149913,0.0001074073,0.00009926704],"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":[0.001081486,0.000117682,0.04308577,0.0001909227,0.00007340797,0.000001149098,0.0003759529,0.003332253,0.006822472,0.0001854292,0.001684098,0.9430494],"study_design_scores_gemma":[0.002828182,0.00005467067,0.004747684,0.00007960619,0.00002372543,0.000001291608,0.00006835484,0.9758726,0.009526286,0.005905745,0.0006871923,0.0002046253],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7958315,0.00006289266,0.2001399,0.001127983,0.0001349623,0.001124443,0.00003447417,0.0002663521,0.001277484],"genre_scores_gemma":[0.9623971,0.000002699569,0.03643513,0.0008008666,0.000160759,0.0000104547,0.0001471587,0.000006654934,0.00003918774],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9725404,"threshold_uncertainty_score":0.9930409,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02465237685497981,"score_gpt":0.2506228751516473,"score_spread":0.2259704982966675,"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."}}