{"id":"W2043276204","doi":"10.1190/1.1444742","title":"Wavelet filtering of magnetotelluric data","year":2000,"lang":"en","type":"article","venue":"Geophysics","topic":"Geophysical and Geoelectrical Methods","field":"Earth and Planetary Sciences","cited_by":96,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Consejo Nacional de Investigaciones Científicas y Técnicas","keywords":"Wavelet; Noise (video); Transfer function; Filter (signal processing); Thresholding; Algorithm; Frequency domain; Computer science; Domain (mathematical analysis); Data point; Wavelet transform; Synthetic data; Point (geometry); Pattern recognition (psychology); Mathematics; Artificial intelligence; Mathematical analysis; Engineering","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001144948,0.00010336,0.0001812928,0.00002061764,0.00005178001,0.00001586386,0.0004761078,0.00003564527,0.007200911],"category_scores_gemma":[0.0000193713,0.00008332545,0.00004364631,0.000391401,0.00005336324,0.0001718963,0.00002527235,0.0001120751,0.001155053],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":6.915573e-7,"about_ca_system_score_gemma":0.00001758176,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001154722,"about_ca_topic_score_gemma":0.00002888819,"domain_scores_codex":[0.99907,0.0000533092,0.0001670709,0.0002531336,0.0001927543,0.0002637331],"domain_scores_gemma":[0.999216,0.0001432905,0.00003820742,0.0005031909,0.00001680573,0.00008246923],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00001637275,0.00002279824,0.0004241037,0.0000138343,0.000006971257,0.000003698919,0.00002416595,0.000250631,0.0001378896,0.00004155201,0.0001812644,0.9988767],"study_design_scores_gemma":[0.0003607877,0.0004978756,0.8465796,0.00002055955,0.0000462725,0.000009486806,0.00001528888,0.06498999,0.001665273,0.03315798,0.05220941,0.0004474911],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9758443,0.0001791435,0.0005360237,0.00009614504,0.0001120077,0.00008144243,0.0001990349,0.00003901792,0.02291293],"genre_scores_gemma":[0.9886459,0.00005618532,0.007989679,0.0001205655,0.0002410877,2.815137e-7,0.0002109981,0.000003271206,0.002732063],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9984292,"threshold_uncertainty_score":0.9996226,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03162513456168712,"score_gpt":0.238236039880897,"score_spread":0.2066109053192099,"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."}}