{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002618718,0.001025765,0.001270573,0.001082625,0.0001793883,0.00004358284,0.0005731738,0.0007580418,0.0001520383],"category_scores_gemma":[0.0001078131,0.00100475,0.0007364443,0.001523732,0.000016067,0.0002963685,0.00006419994,0.0009129279,0.000004260005],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004548536,"about_ca_system_score_gemma":0.0001601183,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008772392,"about_ca_topic_score_gemma":0.00002777918,"domain_scores_codex":[0.9964768,0.00003194436,0.0009966734,0.0006828244,0.0008714166,0.0009403623],"domain_scores_gemma":[0.997825,0.0001502579,0.0002913285,0.001104303,0.0002732099,0.0003558648],"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":[0.00002306242,0.0001317056,0.000008086179,0.0001638525,0.001884578,0.0000413426,0.0006801441,0.921088,0.07482546,0.0005155003,0.00009365148,0.000544647],"study_design_scores_gemma":[0.0007583721,0.0001925002,0.00002990582,0.00008785705,0.001589762,0.000008839871,0.0000659451,0.9178712,0.07791135,0.0002341602,0.0004096646,0.0008404877],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04962529,0.0006033636,0.9473577,0.00007984765,0.000581916,0.0006107925,0.0004834045,0.0006313724,0.00002634847],"genre_scores_gemma":[0.9632601,0.000111755,0.03511622,0.0005474705,0.000113071,0.00005185615,0.0005355667,0.0002437416,0.00002027021],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9136348,"threshold_uncertainty_score":0.9992403,"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."}}