{"id":"W2110136282","doi":"10.1093/gji/ggu329","title":"Frequency- and spatial-correlated noise on layered magnetotelluric inversion","year":2014,"lang":"en","type":"article","venue":"Geophysical Journal International","topic":"Geophysical and Geoelectrical Methods","field":"Earth and Planetary Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Magnetotellurics; Covariance; Covariance matrix; Inversion (geology); Geology; Diagonal; Mathematics; Inverse theory; Statistics; Bayesian probability; Geodesy; Seismology; Physics; Geometry","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":[0.002952986,0.0007242409,0.0004289146,0.0004679824,0.0003630153,0.00105762,0.0005274528,0.0007375041,0.0004094258],"category_scores_gemma":[0.02670296,0.0004311769,0.0004437419,0.0005925038,0.00108879,0.001415137,0.001642222,0.0006153933,0.0001451422],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006753754,"about_ca_system_score_gemma":0.001231611,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01054982,"about_ca_topic_score_gemma":0.01223967,"domain_scores_codex":[0.9988161,0.0004860318,0.00007481242,0.0001148195,0.0004182359,0.00008994775],"domain_scores_gemma":[0.9929497,0.005368247,0.0006227854,0.0004265846,0.0005537811,0.0000790106],"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":[0.000128028,0.00001909871,0.007835448,0.00005608774,0.00005515524,0.0001190264,0.0001015683,0.9567837,0.007950695,0.008293975,0.0001317289,0.01852549],"study_design_scores_gemma":[0.00001696447,0.00003421072,0.005221869,0.00002929516,0.00003307479,0.00006030832,0.00003223863,0.9851794,0.006288345,0.002796393,0.0002728117,0.0000350462],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4519199,0.000474365,0.5435069,0.0003196352,0.00003188677,0.00004560778,0.0002533201,0.0003700851,0.003078254],"genre_scores_gemma":[0.9460016,0.0003062336,0.0529564,0.00006494578,0.00001772362,0.00002843783,0.0001654521,0.00008927201,0.0003698495],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01054982,"threshold_uncertainty_score":0.02097684,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008935491100276619,"score_gpt":0.215397509247627,"score_spread":0.2064620181473504,"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."}}