{"id":"W4388494155","doi":"10.3390/geotechnics3040067","title":"Veined Rock Performance under Uniaxial and Triaxial Compression Using Calibrated Finite Element Numerical Models","year":2023,"lang":"en","type":"article","venue":"Geotechnics","topic":"Rock Mechanics and Modeling","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Queen's University","keywords":"Finite element method; Geotechnical engineering; Compression (physics); Stiffness; Geology; Calibration; Orientation (vector space); Excavation; Materials science; Structural engineering; Geometry; Engineering; Mathematics; Composite material","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006154857,0.0003902602,0.0002229478,0.0004687642,0.0001495361,0.0004225218,0.0006460844,0.0005274234,0.0009656427],"category_scores_gemma":[0.001750007,0.0002249009,0.0002722768,0.0004688904,0.0005433332,0.0004518539,0.0003397558,0.0002786642,0.0002216944],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003782227,"about_ca_system_score_gemma":0.0004129215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002507646,"about_ca_topic_score_gemma":0.003271845,"domain_scores_codex":[0.9996477,0.00005294799,0.00004357327,0.00006807187,0.0001593318,0.00002826188],"domain_scores_gemma":[0.9992852,0.0002780197,0.0001191816,0.0001433549,0.000158179,0.0000162258],"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":[0.0001278639,0.0001399104,0.008335767,0.0001326089,0.00001902986,0.00008040098,0.000203081,0.8645402,0.1120024,0.0008163329,0.0001246142,0.01347785],"study_design_scores_gemma":[0.00001422537,0.0001964648,0.005668085,0.0000126695,0.00001057801,0.00003536499,0.00007197449,0.9463333,0.04687046,0.0002237815,0.0005413181,0.00002171195],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9543642,0.0000974822,0.04230573,0.00004493057,0.00001089321,0.00006060744,0.0004249015,0.0001690814,0.002522083],"genre_scores_gemma":[0.9874899,0.00007627506,0.01173167,0.000005023823,0.000001117536,0.00006123895,0.0001912235,0.00001327621,0.0004302625],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002507646,"threshold_uncertainty_score":0.004986048,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03847816784572504,"score_gpt":0.2362490424052071,"score_spread":0.1977708745594821,"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."}}