{"id":"W1994635819","doi":"10.1002/cjs.5550350206","title":"Objective Bayesian analysis of spatial data with measurement error","year":2007,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Science Foundation","keywords":"Prior probability; Frequentist inference; Bayesian probability; Computer science; Data set; Statistics; Variance (accounting); Field (mathematics); Gaussian; Mathematics; Econometrics; Algorithm; Bayesian inference","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009788745,0.00009434541,0.0002404391,0.0002515311,0.0000797775,0.00002053449,0.000333571,0.00002876787,0.0006644984],"category_scores_gemma":[0.0003305442,0.00008138455,0.00002682764,0.0004584204,0.0001952436,0.00008311143,0.00002921999,0.0001199938,0.000003895997],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003246775,"about_ca_system_score_gemma":0.0004117739,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.06857048,"about_ca_topic_score_gemma":0.7655036,"domain_scores_codex":[0.9986592,0.00002597263,0.0003928637,0.0001377383,0.0005233816,0.0002608612],"domain_scores_gemma":[0.9986544,0.0000888599,0.0003617883,0.0002650002,0.0001574734,0.0004725128],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001537241,0.00008957879,0.8275248,0.00003346303,0.002493154,0.001606328,0.003153947,0.01472964,0.0003626246,0.002110917,0.01560982,0.1321319],"study_design_scores_gemma":[0.0003588646,0.0002488822,0.9818689,0.00003401707,0.001151742,0.00002835109,0.0006516677,0.01049656,0.00007529101,0.0005121044,0.004398647,0.0001749212],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01351894,0.00003779797,0.9830808,0.00004661164,0.0001296638,0.00006398311,0.001118589,0.000001289227,0.002002303],"genre_scores_gemma":[0.9413144,0.000003611353,0.05853022,0.00005997352,0.00002926007,1.667565e-7,0.00003391707,0.000008476351,0.00001996674],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9277955,"threshold_uncertainty_score":0.937632,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03299019793800572,"score_gpt":0.2421908577791648,"score_spread":0.2092006598411591,"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."}}