{"id":"W2079099543","doi":"10.1007/s11004-014-9535-0","title":"Revisiting Multi-Gaussian Kriging with the Nataf Transformation or the Bayes’ Rule for the Estimation of Spatial Distributions","year":2014,"lang":"en","type":"article","venue":"Mathematical Geosciences","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"BP (Canada)","funders":"","keywords":"Mathematics; Kriging; Conditional probability distribution; Gaussian; Multivariate normal distribution; Marginal distribution; Gaussian random field; Probability density function; Statistics; Transformation (genetics); Normal-gamma distribution; Chain rule (probability); Conjugate prior; Gaussian process; Prior probability; Random variable; Regular conditional probability; Multivariate statistics; Bayesian probability; Probability mass function; Asymptotic distribution","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":[],"consensus_categories":[],"category_scores_codex":[0.001419676,0.00008842623,0.0001025114,0.00001025986,0.0008941259,0.0001045289,0.0003621589,0.00001915348,0.0001427339],"category_scores_gemma":[0.0005898785,0.00002853534,0.00003688587,0.0002235756,0.0006139968,0.0001452916,0.00004747539,0.0000709727,0.00001730833],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001614215,"about_ca_system_score_gemma":0.00001249587,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002184023,"about_ca_topic_score_gemma":0.0001394082,"domain_scores_codex":[0.9990247,0.00005027084,0.0002272267,0.000138299,0.0003541677,0.0002053592],"domain_scores_gemma":[0.9983653,0.001259052,0.0001349137,0.0001930528,0.00001568568,0.0000319574],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005168553,0.0001877321,0.0006454627,0.0002679476,0.00003003887,5.008207e-7,0.00994043,0.01992426,0.0005044877,0.2177074,0.0003675509,0.7503725],"study_design_scores_gemma":[0.0001607487,0.00005950254,0.007064349,0.00005347465,0.00003779059,0.000006143603,0.001444206,0.9802002,0.0002273771,0.008903102,0.00176724,0.00007581151],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01567714,0.000005336154,0.977757,0.004898499,0.00004115079,0.0004634724,0.00002170063,0.00001482292,0.001120873],"genre_scores_gemma":[0.9773174,0.000002641428,0.02235256,0.00009882263,0.00002731209,0.00007824481,0.000004824291,0.000004177446,0.0001140019],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9616403,"threshold_uncertainty_score":0.6876984,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01697402550901563,"score_gpt":0.2670517533460693,"score_spread":0.2500777278370537,"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."}}