{"id":"W2801009924","doi":"10.1139/cgj-2017-0578","title":"Mechanical analysis and interpretation of excavation damage zone formation around deep tunnels within massive rock masses using hybrid finite–discrete element approach: case of Atomic Energy of Canada Limited (AECL) Underground Research Laboratory (URL) test tunnel","year":2018,"lang":"en","type":"article","venue":"Canadian Geotechnical Journal","topic":"Rock Mechanics and Modeling","field":"Engineering","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Military College of Canada; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Nuclear Waste Management Organization","keywords":"Spall; Brittleness; Excavation; Rock mass classification; Geotechnical engineering; Discrete element method; Geology; Finite element method; Fracture mechanics; Rock mechanics; Stress field; Engineering; Structural engineering; Mechanics; Materials science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"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":[0.0001995028,0.0002828079,0.0002516329,0.0006913221,0.0003940383,0.0005059974,0.0006940031,0.0008639432,0.0007633781],"category_scores_gemma":[0.0003337945,0.0002164074,0.000332979,0.0003620742,0.0006064085,0.0002148908,0.0002950038,0.0001708899,0.00009004216],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004974887,"about_ca_system_score_gemma":0.0007093269,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02453343,"about_ca_topic_score_gemma":0.03152873,"domain_scores_codex":[0.9999267,0.000009561189,0.000004900037,0.000013673,0.00002619663,0.00001898134],"domain_scores_gemma":[0.9998478,0.00005308368,0.00002902826,0.00001844213,0.00003578583,0.00001588486],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00009562964,0.0001062662,0.02304832,0.0001063534,0.00001888256,0.001304289,0.0003964277,0.9282314,0.0324403,0.00118542,0.0002328364,0.01283392],"study_design_scores_gemma":[0.000006334973,0.00003317013,0.008837448,0.000006709916,0.000007301634,0.0001279558,0.0002260324,0.9885564,0.001841028,0.0001561577,0.0001907343,0.00001075265],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9724124,0.0000668131,0.02509691,0.00006113913,0.000007144994,0.00003362092,0.0001211269,0.00009927116,0.002101558],"genre_scores_gemma":[0.9955428,0.00003280563,0.003873888,0.000004253702,0.000001165394,0.00000929101,0.00004016782,0.000004231808,0.0004914394],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9754665,"threshold_uncertainty_score":0.04878122,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02327702076841566,"score_gpt":0.2460081663317065,"score_spread":0.2227311455632909,"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."}}