{"id":"W2116574501","doi":"10.1016/j.engfracmech.2006.07.014","title":"Prediction of energy release rates for crack growth using FEM-based energy derivative technique","year":2006,"lang":"en","type":"article","venue":"Engineering Fracture Mechanics","topic":"Mechanical Behavior of Composites","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Killam Trusts","keywords":"Finite element method; Strain energy release rate; Materials science; Crack growth resistance curve; Crack closure; Fracture mechanics; Tension (geology); Structural engineering; Nonlinear system; Deformation (meteorology); Fracture (geology); Crack tip opening displacement; Paris' law; Composite material; Mechanics; Engineering; Ultimate tensile strength","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":[0.0004017278,0.0003681303,0.0004546532,0.0004625008,0.0002579235,0.0002881506,0.0005352935,0.0007455563,0.0008742548],"category_scores_gemma":[0.001227198,0.0003610565,0.0003651541,0.0002470128,0.0002617093,0.0004576892,0.0002318033,0.0004953244,0.000216971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005612104,"about_ca_system_score_gemma":0.0004301786,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004669276,"about_ca_topic_score_gemma":0.00347532,"domain_scores_codex":[0.999915,0.00001548547,0.000005721559,0.00001646833,0.00003716482,0.00001013743],"domain_scores_gemma":[0.9994023,0.0003819307,0.00004597185,0.0000481106,0.0001035223,0.00001820775],"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":[0.0001109571,0.00006706042,0.002270716,0.00008199335,0.00001324761,0.00009675949,0.00005180407,0.9322272,0.03872724,0.003420364,0.0002776541,0.022655],"study_design_scores_gemma":[0.000001183568,0.000003254874,0.0001847822,8.421299e-7,8.17568e-7,0.000006209681,9.763681e-7,0.9976814,0.002031972,0.00006347286,0.00002367229,0.000001452027],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4772508,0.0003556522,0.5168743,0.0001286781,0.00003789673,0.00005474717,0.000200771,0.001086506,0.004010784],"genre_scores_gemma":[0.9644246,0.00008813249,0.03431083,0.000008578163,0.00000507018,0.0000247083,0.00005503893,0.00006671287,0.001016452],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004669276,"threshold_uncertainty_score":0.009284198,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00945234603176411,"score_gpt":0.2093031861308382,"score_spread":0.1998508400990741,"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."}}