{"id":"W1991308457","doi":"10.1016/j.engfracmech.2015.02.021","title":"Investigation of the rate dependence of fracture propagation in rocks using digital image correlation (DIC) method","year":2015,"lang":"en","type":"article","venue":"Engineering Fracture Mechanics","topic":"Rock Mechanics and Modeling","field":"Engineering","cited_by":164,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"National Key Research and Development Program of China; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Digital image correlation; Fracture toughness; Fracture mechanics; Displacement (psychology); Materials science; Stress intensity factor; Fracture (geology); 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":[0.0004439798,0.0002633704,0.0002344164,0.00101239,0.0001928791,0.0002922941,0.0003825442,0.0003262488,0.0009183349],"category_scores_gemma":[0.00183206,0.0002418878,0.0001857011,0.0009171483,0.0002833569,0.0004519842,0.0002135951,0.0004457669,0.000152552],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003108475,"about_ca_system_score_gemma":0.0003138054,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002408054,"about_ca_topic_score_gemma":0.002537125,"domain_scores_codex":[0.9996432,0.00002255898,0.0000124656,0.00005585692,0.0002403553,0.00002550808],"domain_scores_gemma":[0.9985278,0.00052296,0.000211592,0.0001492661,0.0005517799,0.00003665241],"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":[0.0001940177,0.00009029307,0.01517985,0.0002906178,0.00003813286,0.0004466175,0.0003002046,0.02225196,0.9056699,0.002197353,0.0005988301,0.05274222],"study_design_scores_gemma":[0.00001344842,0.000128702,0.04176829,0.00001652535,0.00004363814,0.000828121,0.000112401,0.40565,0.5491819,0.0003222396,0.001873876,0.00006093567],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8529141,0.0005867604,0.1407887,0.0001165953,0.00002864453,0.00005770593,0.0003717081,0.000421762,0.004713984],"genre_scores_gemma":[0.9666209,0.0003495065,0.03164226,0.00001653756,0.000009866763,0.00002198406,0.000167856,0.00003811568,0.001132972],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002408054,"threshold_uncertainty_score":0.004788041,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01581613784553769,"score_gpt":0.221022216222523,"score_spread":0.2052060783769853,"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."}}