{"id":"W2518468762","doi":"10.1111/1365-2478.12429","title":"Robust<i>f</i>‐<i>x</i>projection filtering for simultaneous random and erratic seismic noise attenuation","year":2016,"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 Alberta","funders":"University of Alberta","keywords":"Gaussian noise; Noise (video); Algorithm; Value noise; Gradient noise; Computer science; Filter (signal processing); Mathematics; Noise measurement; Noise reduction; Noise floor; 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":[0.001291658,0.000966537,0.0008062783,0.0004120207,0.0004117009,0.0007192314,0.0009078649,0.0009656708,0.0009282257],"category_scores_gemma":[0.002688158,0.0004314192,0.0006884011,0.0004457996,0.0007357951,0.0009333494,0.001181703,0.001101029,0.0003120174],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000404923,"about_ca_system_score_gemma":0.001137412,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003003221,"about_ca_topic_score_gemma":0.002076456,"domain_scores_codex":[0.9992563,0.000193998,0.00004408216,0.0001892445,0.0002552405,0.00006112234],"domain_scores_gemma":[0.9992051,0.0003021008,0.0001384955,0.0001069596,0.0002166465,0.00003066194],"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":[0.0003289974,0.0001375473,0.001438909,0.0001495051,0.0001215638,0.0001782125,0.0001490471,0.592841,0.0837689,0.02641304,0.001888664,0.2925846],"study_design_scores_gemma":[0.000006604734,0.00003770652,0.0001995502,0.000004009046,0.000007459311,0.00003284577,0.000005033921,0.9888296,0.008888434,0.001458845,0.0005192222,0.00001069248],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009540816,0.00003946061,0.9898897,0.00004164724,0.00001241977,0.000008801451,0.000009991153,0.0001745621,0.0002824721],"genre_scores_gemma":[0.3021027,0.0001279626,0.6958196,0.00008258419,0.00004392372,0.00007427713,0.0001068879,0.00008437903,0.001557608],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003003221,"threshold_uncertainty_score":0.00683105,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0173382358541179,"score_gpt":0.213969437590745,"score_spread":0.1966312017366271,"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."}}