{"id":"W2972237095","doi":"10.1016/j.spasta.2019.100385","title":"Simulation of decorrelated factors in presence of secondary data","year":2019,"lang":"en","type":"article","venue":"Spatial Statistics","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Multivariate statistics; Geostatistics; Multivariate analysis; Gaussian; Variable (mathematics); Computer science; Multivariate normal distribution; Variables; Data mining; Statistics; Algorithm; Mathematics; Machine learning; Spatial variability; Chemistry","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001292191,0.00006944963,0.0001476525,0.00002854881,0.00001212422,0.00000415288,0.0002230063,0.00003939174,0.001887663],"category_scores_gemma":[0.0004086031,0.00006858556,0.000007860405,0.0001241473,0.00007306299,0.00009265253,0.0002347536,0.00007851214,0.00003637914],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002549185,"about_ca_system_score_gemma":0.00001862847,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01241346,"about_ca_topic_score_gemma":0.003229393,"domain_scores_codex":[0.9991716,0.0000298638,0.00029012,0.0001779372,0.0002129484,0.0001176038],"domain_scores_gemma":[0.9989467,0.0005352251,0.0001631886,0.0003104661,0.00001443102,0.00003004562],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000213871,0.00005392977,0.702401,0.00004810661,0.000007082374,0.000002362968,0.0004452156,0.2578096,0.0009670255,0.0004006958,0.0002626858,0.03758088],"study_design_scores_gemma":[0.0001801642,0.00004178919,0.4816449,0.00001371941,0.000005077557,6.550835e-8,0.00003666951,0.5167381,0.0001485051,0.0009468848,0.0001870231,0.00005714369],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.760941,0.000007660054,0.2358877,0.000002797822,0.0001967914,0.0001851164,0.001373804,0.000005213612,0.001399869],"genre_scores_gemma":[0.9916814,0.000005343276,0.007922042,0.00000546807,0.000004114899,5.295868e-7,0.0003134539,0.000006768722,0.00006085866],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2589285,"threshold_uncertainty_score":0.9990247,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02016158448990196,"score_gpt":0.2668493752540915,"score_spread":0.2466877907641896,"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."}}