{"id":"W2162668636","doi":"10.1002/cjs.11186","title":"Objective Bayesian analysis of spatial models with separable correlation functions","year":2013,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; National Science Foundation","keywords":"Frequentist inference; Prior probability; Bayesian probability; Mathematics; Statistics; Range (aeronautics); Variance (accounting); Gaussian; Applied mathematics; Econometrics; Bayesian inference","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02079168,0.0009298075,0.001714271,0.002486195,0.0005242464,0.002558929,0.002298826,0.001260722,0.001595357],"category_scores_gemma":[0.06710029,0.001072788,0.001490202,0.001969935,0.002410092,0.002465075,0.002372985,0.001838623,0.0001717616],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001976931,"about_ca_system_score_gemma":0.001624129,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01428469,"about_ca_topic_score_gemma":0.00915313,"domain_scores_codex":[0.9925833,0.00521275,0.0002026937,0.0007829273,0.0008841559,0.0003342137],"domain_scores_gemma":[0.9438286,0.04837751,0.003465561,0.002377271,0.001559186,0.0003918656],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00006690152,0.00003066769,0.003253296,0.00008137774,0.00015666,0.0001231066,0.0001573694,0.8671362,0.0002651228,0.1148113,0.0004787741,0.01343917],"study_design_scores_gemma":[0.00001283569,0.00001150862,0.00125731,0.00002131742,0.00001994801,0.00002383856,0.00002824729,0.9231315,0.0001297379,0.07504635,0.0002993488,0.00001804676],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07300223,0.0003155264,0.924996,0.0002514955,0.000007226578,0.00003530415,0.0001730834,0.0001189385,0.001100255],"genre_scores_gemma":[0.9058916,0.0005545798,0.09074106,0.0001099708,0.00005584506,0.0001411293,0.0006103409,0.0001030112,0.001792439],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02079168,"threshold_uncertainty_score":0.1099582,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0079022138037199,"score_gpt":0.1841018837453957,"score_spread":0.1761996699416758,"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."}}