{"id":"W3011875294","doi":"10.1093/gji/ggaa104","title":"A least-squares method for estimating the correlated error of GRACE models","year":2020,"lang":"en","type":"article","venue":"Geophysical Journal International","topic":"Geophysics and Gravity Measurements","field":"Earth and Planetary Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Geological Survey of Canada; Natural Resources Canada","funders":"Natural Resources Canada; National Aeronautics and Space Administration","keywords":"Data assimilation; Spherical harmonics; Decorrelation; Altimeter; Geodesy; Least-squares function approximation; Mathematics; Applied mathematics; Computer science; Meteorology; Environmental science; Statistics; Geology; Geography; Mathematical analysis","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":[0.0002819082,0.00009916722,0.000153491,0.0000247709,0.0001580842,0.0000798141,0.0004000445,0.00002832193,0.0001691313],"category_scores_gemma":[0.0001204852,0.00006491653,0.000171377,0.0001096721,0.00003983054,0.0002204508,0.00001736262,0.0002401867,0.00003343172],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003822519,"about_ca_system_score_gemma":0.00004523138,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002560335,"about_ca_topic_score_gemma":0.00001814716,"domain_scores_codex":[0.9988788,0.00005558064,0.0002712059,0.000138244,0.0004921251,0.0001640436],"domain_scores_gemma":[0.9991366,0.0001984922,0.0002225725,0.00006330441,0.0002655231,0.0001135196],"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":[0.0005821068,0.0001555677,0.01030171,0.00004098901,0.000511143,0.00001284562,0.002123523,0.7831919,0.001528413,0.005189665,0.003815852,0.1925463],"study_design_scores_gemma":[0.0003572473,0.0001489491,0.01614954,0.00002023689,0.0000278774,0.00001288462,0.00009724295,0.9411476,0.0001050131,0.04152485,0.0003335049,0.00007507049],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.259436,0.0001198167,0.7237281,0.01298592,0.001744653,0.0003197959,0.0002748025,0.00002503004,0.001365852],"genre_scores_gemma":[0.9617766,0.000001503922,0.03697772,0.0005575235,0.0005858836,0.000001297107,0.00003592083,0.000003696175,0.00005988105],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7023405,"threshold_uncertainty_score":0.2647219,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0677379620309966,"score_gpt":0.298768422867394,"score_spread":0.2310304608363974,"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."}}