{"id":"W3195967870","doi":"10.1016/j.ijmst.2021.08.001","title":"Dynamic Mode Ⅱ fracture behavior of rocks under hydrostatic pressure using the short core in compression (SCC) method","year":2021,"lang":"en","type":"article","venue":"International Journal of Mining Science and Technology","topic":"Rock Mechanics and Modeling","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Beijing Institute of Technology; Academy of Finland; Natural Sciences and Engineering Research Council of Canada; State Key Laboratory of Explosion Science and Technology; National Natural Science Foundation of China","keywords":"Hydrostatic equilibrium; Hydrostatic pressure; Geotechnical engineering; Fracture toughness; Shear (geology); Fracture (geology); Hydrostatic stress; Hydraulic fracturing; Dynamic loading; Rock mechanics; Geology; Materials science; Finite element method; Composite material; Structural engineering; Mechanics; Engineering","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":[0.0002964985,0.0002761537,0.0001941917,0.0007215411,0.0001867781,0.0001148826,0.0003154053,0.0002266456,0.001142235],"category_scores_gemma":[0.0003483063,0.0002020711,0.0001424275,0.0004213551,0.0003150005,0.0002945834,0.0001653438,0.0003351561,0.0001130289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001422275,"about_ca_system_score_gemma":0.000144537,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002001174,"about_ca_topic_score_gemma":0.004584414,"domain_scores_codex":[0.9997985,0.000009307913,0.000009293854,0.00004198922,0.0001245152,0.00001638881],"domain_scores_gemma":[0.9996538,0.00007337284,0.00007802703,0.00002980621,0.0001407756,0.00002419997],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00009377934,0.00002432603,0.00581012,0.0001344746,0.000009199824,0.0001339096,0.0002781498,0.001212713,0.9735454,0.0002237889,0.0001037872,0.01843041],"study_design_scores_gemma":[0.0000102773,0.0004834626,0.07125746,0.0000186927,0.00003509513,0.0005867604,0.0003288133,0.05064442,0.8748882,0.0002256141,0.001474868,0.0000462703],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9666861,0.0004594307,0.03138652,0.00003398847,0.00001815029,0.00003283088,0.0001734177,0.0001267043,0.001082801],"genre_scores_gemma":[0.984314,0.0002717751,0.01430077,0.00001353494,0.000006878033,0.00003351643,0.0001096384,0.00001383787,0.0009360157],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002001174,"threshold_uncertainty_score":0.003979027,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02232131492331209,"score_gpt":0.3418210646327435,"score_spread":0.3194997497094314,"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."}}