{"id":"W2744773669","doi":"10.1007/s00603-017-1293-0","title":"Modelling Geomechanical Heterogeneity of Rock Masses Using Direct and Indirect Geostatistical Conditional Simulation Methods","year":2017,"lang":"en","type":"article","venue":"Rock Mechanics and Rock Engineering","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":33,"is_retracted":false,"has_abstract":false,"ca_institutions":"ArcelorMittal (Canada); University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; ArcelorMittal","keywords":"Rock mass classification; Kriging; Geology; Geostatistics; Spatial variability; Rock mass rating; Field (mathematics); Geotechnical engineering; Soil science; Statistics; Mathematics","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":[0.000291865,0.0001843756,0.0002839409,0.00005005025,0.000437314,0.00006917575,0.000118219,0.0001044464,0.00003645703],"category_scores_gemma":[0.0002165041,0.0001887751,0.00003919631,0.0000491228,0.000002553559,0.0001727324,0.0003836666,0.000123321,0.000002125352],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004422902,"about_ca_system_score_gemma":0.000008745122,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001081682,"about_ca_topic_score_gemma":0.000005553534,"domain_scores_codex":[0.9989095,0.00002879235,0.0002664697,0.0003308542,0.0002060746,0.0002582587],"domain_scores_gemma":[0.9992526,0.0002413161,0.0001447315,0.0002000865,0.00002104636,0.0001401915],"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":[0.000006984279,0.00001324459,0.0002759654,0.00004333111,0.00002598927,0.000003540972,0.00004108523,0.9888774,0.006814274,0.003327579,0.000001992318,0.0005686416],"study_design_scores_gemma":[0.0002059113,0.00003886803,0.0005351607,0.00004285227,0.00004744361,0.00001282809,0.00001152221,0.9930533,0.002479672,0.003147095,0.0002112773,0.0002140694],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07005353,0.0001057812,0.9294931,0.000007008221,0.0001221355,0.0001100506,0.00005134188,0.00002509231,0.0000320128],"genre_scores_gemma":[0.9285319,0.00006806866,0.07131013,0.000009171445,0.00003419981,0.000004548982,0.00001105529,0.00002186492,0.000009052498],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8584784,"threshold_uncertainty_score":0.7698025,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03773031069983884,"score_gpt":0.3024762281214777,"score_spread":0.2647459174216388,"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."}}