{"id":"W2000536488","doi":"10.2118/07-11-02","title":"Global Resource Uncertainty Using a Spatial/Multivariate Decomposition Approach","year":2007,"lang":"en","type":"article","venue":"Journal of Canadian Petroleum Technology","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Multivariate statistics; Monte Carlo method; Geostatistics; Consistency (knowledge bases); Multivariate normal distribution; Gaussian; Field (mathematics); Spatial variability; Computer science; Uncertainty analysis; Statistics; Mathematics; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002258576,0.001049076,0.0006950612,0.002336022,0.0004560085,0.001877869,0.0007637612,0.0005713263,0.003037309],"category_scores_gemma":[0.003797579,0.000507849,0.001401259,0.002240509,0.0008029546,0.001588024,0.00169215,0.001126242,0.0002579321],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001492341,"about_ca_system_score_gemma":0.001323439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01118878,"about_ca_topic_score_gemma":0.005890038,"domain_scores_codex":[0.9989128,0.0005215706,0.00004000921,0.0001206831,0.0003066064,0.0000983093],"domain_scores_gemma":[0.9981865,0.001101812,0.0001905013,0.000128609,0.0003300006,0.00006259634],"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":[0.00001416232,0.00001272489,0.0007869054,0.00003067274,0.00004301853,0.0000725561,0.00003263421,0.9349526,0.0004492987,0.04443036,0.0005943551,0.01858061],"study_design_scores_gemma":[0.00000132057,0.000005511738,0.0002005455,0.000007953412,0.000007293383,0.00001223322,0.00001843673,0.9778339,0.0001274608,0.02130004,0.000479379,0.000005985771],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009217467,0.0001261604,0.9866683,0.0001863428,0.00001605557,0.00002602202,0.0001133568,0.00008478124,0.003561457],"genre_scores_gemma":[0.6811531,0.000886668,0.3128912,0.0001314041,0.0001270256,0.0002045792,0.0004451063,0.0001626552,0.003998382],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01118878,"threshold_uncertainty_score":0.02224731,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01029087107998495,"score_gpt":0.2474182440256411,"score_spread":0.2371273729456562,"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."}}