{"id":"W2157312881","doi":"10.1002/nme.4968","title":"Bridging cell multiscale modeling of fatigue crack growth in fcc crystals","year":2015,"lang":"en","type":"article","venue":"International Journal for Numerical Methods in Engineering","topic":"Microstructure and mechanical properties","field":"Materials Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Bridging (networking); Materials science; Paris' law; Structural engineering; Composite material; Crystal structure; Crystal plasticity; Crack closure; Finite element method; Crystallography; Multiscale modeling; Mechanics; Fracture mechanics; Microstructure; Engineering; Chemistry; Physics; Computational chemistry; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001460995,0.0001264722,0.0002797599,0.0002169453,0.00001665191,0.00005456461,0.0003802661,0.00006710477,0.00002481844],"category_scores_gemma":[0.001458225,0.0001094455,0.0000841584,0.0001163664,0.00001392664,0.000256292,0.00009014715,0.0002471711,0.000001610176],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001554792,"about_ca_system_score_gemma":0.00004153623,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008624746,"about_ca_topic_score_gemma":0.000001424737,"domain_scores_codex":[0.9985937,0.00009210837,0.0006213305,0.0001725545,0.0002763853,0.0002438459],"domain_scores_gemma":[0.9992636,0.0001921533,0.0001237896,0.00006929773,0.0002403735,0.000110771],"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":[0.00006526894,0.00003081791,0.00007162961,0.00002595119,0.000005058921,0.000008981653,0.0003315383,0.4230714,0.5741386,0.0001685568,0.00001644107,0.00206578],"study_design_scores_gemma":[0.0005953595,0.0000411881,0.00001598221,0.0001125759,0.000003468372,0.00003545543,0.00007882884,0.6631135,0.3336133,0.002180478,0.0001000524,0.0001098843],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1412828,0.000237222,0.8565004,0.00009883553,0.001703939,0.00008825959,0.000006838324,0.00001496275,0.00006669556],"genre_scores_gemma":[0.5919221,0.00001028933,0.4078822,0.00002473354,0.0001263406,0.000005910549,8.562187e-7,0.00001363118,0.00001397336],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4506392,"threshold_uncertainty_score":0.4463058,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07128347726969818,"score_gpt":0.3755385881788772,"score_spread":0.304255110909179,"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."}}