{"id":"W2327544630","doi":"10.1190/1.3255555","title":"Adaptive linear prediction filtering for random noise attenuation","year":2009,"lang":"en","type":"article","venue":"","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"University of Alberta","keywords":"Underdetermined system; Algorithm; Smoothing; Adaptive filter; Computer science; Noise (video); Filter (signal processing); Linear prediction; Inversion (geology); Quadratic equation; Mathematics; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006795673,0.0005789346,0.0006107538,0.0004452592,0.0004150089,0.0006285159,0.001079014,0.0008528982,0.003277466],"category_scores_gemma":[0.001949124,0.0003200376,0.0005743866,0.0005283129,0.0005484758,0.0009384254,0.0006750707,0.0009036565,0.001072599],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006289645,"about_ca_system_score_gemma":0.0009806901,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003866848,"about_ca_topic_score_gemma":0.004258926,"domain_scores_codex":[0.999605,0.00006256602,0.00001869262,0.00009122204,0.0001820521,0.00004045568],"domain_scores_gemma":[0.9994721,0.0002290468,0.00005017677,0.00006741181,0.0001663094,0.00001493819],"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":[0.0001484518,0.00006811463,0.0005561006,0.00007930394,0.00005764045,0.0001191193,0.00009627521,0.4935886,0.01926093,0.06476945,0.003522802,0.4177331],"study_design_scores_gemma":[0.000009730268,0.00002354466,0.00006156902,0.000003529424,0.000007046832,0.00002242563,0.000002822632,0.9897786,0.002671937,0.005668416,0.001744014,0.00000636743],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001213464,0.00004556583,0.9981685,0.00002920825,0.00001664582,0.00000783565,0.00000566541,0.0001864834,0.0003266043],"genre_scores_gemma":[0.08119657,0.0001755757,0.9134692,0.00008841208,0.00008022314,0.000111117,0.00009152314,0.0001054427,0.004681944],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003866848,"threshold_uncertainty_score":0.01096416,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02215461973954285,"score_gpt":0.2283712445312671,"score_spread":0.2062166247917242,"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."}}