{"id":"W2115211351","doi":"10.1016/j.engfracmech.2008.07.002","title":"Fatigue analysis of post-weld fatigue improvement treatments using a strain-based fracture mechanics model","year":2008,"lang":"en","type":"article","venue":"Engineering Fracture Mechanics","topic":"Fatigue and fracture mechanics","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"University of Waterloo; École Polytechnique Fédérale de Lausanne","keywords":"Welding; Materials science; Fracture mechanics; Structural engineering; Parametric statistics; Fracture (geology); Strain (injury); Damage mechanics; Composite material; Finite element method; Engineering; Mathematics","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":[0.0002657396,0.0002899828,0.0005199242,0.0003744089,0.0002619567,0.0002091773,0.0006991852,0.0008985855,0.0017935],"category_scores_gemma":[0.0004217819,0.0002527763,0.0005708614,0.0002981668,0.0001925485,0.0003234553,0.0001333965,0.0002810527,0.0002963123],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000493613,"about_ca_system_score_gemma":0.0004559305,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01201832,"about_ca_topic_score_gemma":0.01413628,"domain_scores_codex":[0.999908,0.000007503143,0.000005463303,0.00001471161,0.00004775366,0.00001658234],"domain_scores_gemma":[0.9998523,0.00004910354,0.00002089477,0.00001820243,0.0000544147,0.000005050617],"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":[0.000289755,0.0001627258,0.00151151,0.0001452909,0.00003678738,0.0001373357,0.0001013667,0.7829549,0.1799102,0.001426984,0.0006321793,0.03269092],"study_design_scores_gemma":[0.000005763852,0.0000906588,0.002278259,0.000002947447,0.00001354671,0.00002189896,0.000009475475,0.9823069,0.01491789,0.000101986,0.0002427187,0.000007904637],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.822468,0.0006734913,0.1685642,0.0001471114,0.00003573604,0.0001039703,0.0004094227,0.0007553845,0.006842686],"genre_scores_gemma":[0.9899379,0.0001179859,0.007534713,0.00001139537,0.000002995689,0.00003239613,0.0001086463,0.00003089204,0.00222308],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01201832,"threshold_uncertainty_score":0.02389675,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02369266581709234,"score_gpt":0.2331113453519518,"score_spread":0.2094186795348595,"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."}}