{"id":"W2811208379","doi":"10.1108/ec-07-2017-0240","title":"Numerical simulation of the anisotropic properties of a columnar jointed rock mass under triaxial compression","year":2018,"lang":"en","type":"article","venue":"Engineering Computations","topic":"Rock Mechanics and Modeling","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Education and Child Care; University of Toronto","funders":"","keywords":"Geological Strength Index; Overburden pressure; Rock mass classification; Anisotropy; Geotechnical engineering; Materials science; Compressive strength; Joint (building); Deformation (meteorology); Elastic modulus; Triaxial shear test; Poisson's ratio; Modulus; Compression (physics); Stress (linguistics); Structural engineering; Computer simulation; Geology; Poisson distribution; Composite material; Mechanics; Mathematics; Engineering; Physics; Optics; Statistics","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":[0.00003664993,0.00008871269,0.0001424629,0.00006353296,0.00005939962,0.000008887602,0.0001009269,0.00004303567,0.000007080596],"category_scores_gemma":[0.00003636826,0.00007345818,0.00005685363,0.0002050356,0.000008680497,0.00004844792,0.0000238537,0.00008245783,0.000002347595],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002907487,"about_ca_system_score_gemma":0.00001623318,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007425282,"about_ca_topic_score_gemma":7.941757e-7,"domain_scores_codex":[0.9994122,0.00001321893,0.0002564216,0.00007876114,0.000132087,0.0001072466],"domain_scores_gemma":[0.999653,0.00004249947,0.00004299794,0.0001314303,0.0001082771,0.0000217348],"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":[0.000003729967,0.000009656153,0.000004943546,0.00004585933,0.00001947285,4.225843e-8,0.0002034693,0.887154,0.1121751,0.0003019447,0.00001634373,0.00006544255],"study_design_scores_gemma":[0.0001933068,0.00003049048,0.0001724898,0.000151975,0.00001278467,6.217692e-7,0.00002276284,0.9516112,0.04756045,0.0001160646,0.00005501121,0.00007290138],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3140141,0.00005514577,0.6853944,0.00001126468,0.0003197595,0.0001056069,0.000002098068,0.0000786239,0.00001898874],"genre_scores_gemma":[0.9962285,0.000002125489,0.003650132,0.000004296814,0.00007633045,0.000005136959,0.000001881178,0.00002250668,0.000009147613],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6822143,"threshold_uncertainty_score":0.2995538,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02163842775807879,"score_gpt":0.2245956144371105,"score_spread":0.2029571866790318,"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."}}