{"id":"W2040686030","doi":"10.1190/1.3063871","title":"Attenuation of coherent noise using localized‐adaptive eigenimage filter","year":2008,"lang":"en","type":"article","venue":"","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"ConocoPhillips (Canada)","funders":"","keywords":"Attenuation; Noise (video); Filter (signal processing); Nonlinear filter; Computer science; Acoustics; Energy (signal processing); Noise measurement; Noise floor; Nonlinear system; Algorithm; Noise reduction; Physics; Optics; Filter design; Artificial intelligence; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003104826,0.0004288804,0.000416877,0.0004590181,0.0002290002,0.0003468144,0.0004946733,0.0003667617,0.0009887801],"category_scores_gemma":[0.0007556609,0.0002421086,0.0004619844,0.0003096578,0.0003839285,0.000770603,0.0005396946,0.0004648289,0.000337166],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003256745,"about_ca_system_score_gemma":0.0004293482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002932946,"about_ca_topic_score_gemma":0.0048366,"domain_scores_codex":[0.9998025,0.00004056692,0.000008812473,0.00004229745,0.00008102872,0.00002472283],"domain_scores_gemma":[0.999734,0.00008145128,0.00004005907,0.00005545545,0.00007673563,0.00001224668],"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":[0.0003301328,0.00015789,0.002797079,0.0001146007,0.0001493207,0.0001921597,0.0002918306,0.1593729,0.3457766,0.01291596,0.002036475,0.4758651],"study_design_scores_gemma":[0.00001398311,0.00005536719,0.001560779,0.000004598845,0.00002573451,0.00008286715,0.00001711692,0.934707,0.06110281,0.001179149,0.001226298,0.00002429675],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07304041,0.0001261573,0.9251019,0.00007463391,0.00002223113,0.00001298541,0.00002508269,0.0006530868,0.0009435218],"genre_scores_gemma":[0.4594421,0.0002436833,0.5362689,0.00005816482,0.00004243397,0.00005206571,0.0001600543,0.0001501471,0.003582373],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002932946,"threshold_uncertainty_score":0.005831718,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08829171166154069,"score_gpt":0.25223643332558,"score_spread":0.1639447216640393,"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."}}