{"id":"W2065284489","doi":"10.1007/s00477-009-0322-2","title":"An approximate method for joint sequential simulation of multiple spatial variables","year":2009,"lang":"en","type":"article","venue":"Stochastic Environmental Research and Risk Assessment","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; University of Alberta","keywords":"Matrix (chemical analysis); Computer science; Correlation; Covariance matrix; Geostatistics; Variable (mathematics); Mathematical optimization; Applied mathematics; Decomposition; Multivariate statistics; Algorithm; Statistics; Mathematics; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001475538,0.0001540901,0.0002055758,0.0000714342,0.0003756483,0.00004793496,0.0001373689,0.00006869131,0.0001950322],"category_scores_gemma":[0.0001233699,0.0001375313,0.00004223408,0.00007945674,0.0002621485,0.0001553084,0.0001378188,0.0002014702,0.000005469949],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000199499,"about_ca_system_score_gemma":0.00002015688,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009575248,"about_ca_topic_score_gemma":0.00006928907,"domain_scores_codex":[0.9979625,0.0001852931,0.0003038715,0.0004558779,0.0006498796,0.0004425941],"domain_scores_gemma":[0.9989358,0.0004660183,0.0001278355,0.0002483426,0.00001136037,0.0002106244],"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":[0.0002935272,0.001184438,0.01195296,0.00003224557,0.00005311909,0.000003899648,0.0005616033,0.5791889,0.1195974,0.001070745,0.00005473053,0.2860065],"study_design_scores_gemma":[0.0008692283,0.001389101,0.09214064,0.00001353714,0.00002512198,0.000001643258,0.0002579583,0.8851096,0.001029472,0.01893472,0.00008378784,0.0001452052],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1609031,0.000019255,0.8378712,0.00003078513,0.00003972055,0.0007370926,0.000191973,0.00001137784,0.0001954911],"genre_scores_gemma":[0.8738428,0.00005034822,0.1258556,0.00001237233,0.00004680688,0.00005261396,0.00008601939,0.00001286199,0.00004058779],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7129397,"threshold_uncertainty_score":0.5608365,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04157600687253318,"score_gpt":0.3779349481032476,"score_spread":0.3363589412307144,"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."}}