{"id":"W4229798980","doi":"10.1190/1.2370101","title":"Curvelet‐based ground roll removal","year":2006,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Curvelet; Reflector (photography); SIGNAL (programming language); Process (computing); Set (abstract data type); Computer science; Interference (communication); Algorithm; Block (permutation group theory); Artificial intelligence; Relaxation (psychology); Computer vision; Acoustics; Mathematics; Optics; Physics; Telecommunications; Geometry; Wavelet transform; Wavelet","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004804837,0.00009140398,0.0001004796,0.00007210055,0.0000909605,0.0002269053,0.0004865298,0.00002926087,0.0000492068],"category_scores_gemma":[0.00002017814,0.00007600759,0.00006376172,0.0002957504,0.00002399583,0.0003178678,0.00006484058,0.0000715936,0.0001357824],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002046205,"about_ca_system_score_gemma":0.00004727513,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005401449,"about_ca_topic_score_gemma":0.00002304598,"domain_scores_codex":[0.9990433,0.0001157924,0.0001481167,0.0002501435,0.000218092,0.0002245015],"domain_scores_gemma":[0.9993709,0.0001135834,0.00003239039,0.000389575,0.00005485752,0.00003874604],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004557531,0.0003970009,0.001014751,0.00003051676,0.00001874375,0.001200346,0.000118248,0.001262266,0.05632191,0.6975723,0.06100776,0.1810106],"study_design_scores_gemma":[0.002927361,0.0001607352,0.01623958,0.00002337707,0.00001296716,0.0002386234,0.000006122745,0.563641,0.08759233,0.146897,0.1813994,0.0008614911],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006065326,0.00006905135,0.8827249,0.0006370812,0.0002116731,0.0000434222,2.223669e-7,0.0002086847,0.1100397],"genre_scores_gemma":[0.2652704,3.995956e-7,0.7222287,0.001387982,0.0001463383,0.00000211701,0.000001636863,0.000006898082,0.01095546],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5623787,"threshold_uncertainty_score":0.30995,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02045802091349777,"score_gpt":0.2678922200865714,"score_spread":0.2474341991730737,"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."}}