{"id":"W235494837","doi":"10.1007/s11053-015-9267-y","title":"Practical Incorporation of Multivariate Parameter Uncertainty in Geostatistical Resource Modeling","year":2015,"lang":"en","type":"article","venue":"Natural Resources Research","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canadian Natural Resources; University of Alberta","funders":"","keywords":"Uncertainty quantification; Multivariate statistics; Computer science; Uncertainty analysis; Workflow; Sensitivity analysis; Data mining; Resampling; Econometrics; Mathematics; Algorithm; Machine learning; Simulation","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":[0.002897563,0.0001188992,0.000194615,0.0001597345,0.00008945323,0.0000563203,0.0002428097,0.0001200167,0.00008564426],"category_scores_gemma":[0.008276198,0.00009994127,0.00002975772,0.0006629378,0.0003562489,0.0001595709,0.000447515,0.0008327686,0.00008474955],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003151005,"about_ca_system_score_gemma":0.00006082731,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006872111,"about_ca_topic_score_gemma":0.0007552483,"domain_scores_codex":[0.9967449,0.0005747711,0.0003793482,0.0003813512,0.001414298,0.0005052817],"domain_scores_gemma":[0.9979821,0.001360333,0.00007318296,0.0002521755,0.0001144128,0.0002178019],"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":[0.004291544,0.001085835,0.05477501,0.0001475067,0.0000699176,0.0005944942,0.01915278,0.7168974,0.009631516,0.02214238,0.02644202,0.1447696],"study_design_scores_gemma":[0.0005946313,0.0001295321,0.005696346,0.00003142665,0.000003479419,0.000007068851,0.0008757314,0.9757777,0.00006618156,0.008781889,0.00790489,0.0001311659],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9863292,0.00006240865,0.005483365,0.0008693465,0.00005851907,0.0003427505,0.00001081741,0.00001965717,0.006823921],"genre_scores_gemma":[0.9828432,0.000004624706,0.01669181,0.00004739799,0.00003788044,0.00001709348,0.0000221824,0.00001350175,0.0003223602],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2588803,"threshold_uncertainty_score":0.9997412,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1310898345134073,"score_gpt":0.4036998866026779,"score_spread":0.2726100520892706,"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."}}