{"id":"W3013204269","doi":"10.1108/ec-03-2019-0123","title":"Numerical Brazilian split test of pre-cracked granite with randomly distributed micro-components","year":2020,"lang":"en","type":"article","venue":"Engineering Computations","topic":"Rock Mechanics and Modeling","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Anisotropy; Materials science; Deformation (meteorology); Computer simulation; Stress (linguistics); Ultimate tensile strength; Structural engineering; Composite material; Geology; Mechanics; Engineering; Optics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005291172,0.0003584078,0.0002245979,0.0005511495,0.0002604307,0.0002688989,0.0005076603,0.0003671038,0.002222936],"category_scores_gemma":[0.001246081,0.0001855266,0.0003132029,0.0003264069,0.0005812847,0.0002223224,0.0003669346,0.0002023105,0.0002600954],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003368613,"about_ca_system_score_gemma":0.0002773069,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003233528,"about_ca_topic_score_gemma":0.006675027,"domain_scores_codex":[0.9996498,0.00003530971,0.0000203023,0.00007827505,0.0001789845,0.00003742392],"domain_scores_gemma":[0.9992197,0.0002235026,0.0001278764,0.0001520837,0.0002160447,0.00006067847],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001648692,0.0002727798,0.0607162,0.00033374,0.00007727583,0.001067892,0.0006729459,0.06261743,0.8315755,0.003200666,0.0005074704,0.0373094],"study_design_scores_gemma":[0.00009532253,0.002775331,0.1191996,0.00006347113,0.0001194088,0.001224795,0.0009820889,0.3486516,0.5204335,0.001474601,0.004916262,0.00006395357],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9850456,0.00006226181,0.01269464,0.00002118563,0.000009507658,0.00003260041,0.0001590442,0.0001119859,0.001863181],"genre_scores_gemma":[0.9948003,0.00001810006,0.004423841,0.000004990702,6.037572e-7,0.00001668892,0.00009934879,0.00001442995,0.0006216262],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003233528,"threshold_uncertainty_score":0.007436454,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008024619380600393,"score_gpt":0.1934187666958956,"score_spread":0.1853941473152952,"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."}}