{"id":"W2093070449","doi":"10.1190/geo2014-0084.1","title":"Noise reduction procedures for gravity-gradiometer data","year":2014,"lang":"en","type":"article","venue":"Geophysics","topic":"Geophysics and Gravity Measurements","field":"Earth and Planetary Sciences","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Geological Survey of Canada","funders":"Natural Resources Canada","keywords":"Kriging; Smoothing; Variogram; Gradiometer; Interpolation (computer graphics); Noise (video); Smoothness; Estimator; Geology; Noise reduction; Algorithm; Computer science; Geodesy; Mathematics; Statistics; Mathematical analysis; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003256375,0.0001384465,0.0001587641,0.00004421357,0.0002050943,0.00008335611,0.0004299494,0.0000426898,0.00003249383],"category_scores_gemma":[0.00007109099,0.0001215527,0.00005723931,0.0002044135,0.00004655177,0.0003268556,0.00002312121,0.00008043111,0.000211301],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002364266,"about_ca_system_score_gemma":0.00004145949,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005860064,"about_ca_topic_score_gemma":0.0002478778,"domain_scores_codex":[0.9988628,0.00003434153,0.0001443662,0.0003877543,0.0002697547,0.0003010076],"domain_scores_gemma":[0.9991047,0.00004712447,0.00007945493,0.000593917,0.00007980599,0.00009501258],"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":[0.0002538804,0.0003653278,0.08434541,0.0005448813,0.0002193055,0.000001659108,0.0005441577,0.001217226,0.008400974,0.003765052,0.03495531,0.8653868],"study_design_scores_gemma":[0.001271517,0.0005646539,0.6894168,0.00005019865,0.0001635854,0.00001018831,0.00008006284,0.02046433,0.001231668,0.2182308,0.06768316,0.0008330545],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9912413,0.00008870639,0.003519709,0.0004257313,0.001475048,0.000614804,0.0006418006,0.00008827583,0.001904677],"genre_scores_gemma":[0.9950407,0.000006031198,0.002142286,0.0001337681,0.000798728,0.000003945363,0.001554387,0.000006280057,0.0003139317],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8645537,"threshold_uncertainty_score":0.4956774,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04157917204333603,"score_gpt":0.2459330447246504,"score_spread":0.2043538726813144,"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."}}