{"id":"W3156073031","doi":"10.3390/applmech2020013","title":"Prediction of Tensile Strain Capacity for X52 Steel Pipeline Materials Using the Extended Finite Element Method","year":2021,"lang":"en","type":"article","venue":"Applied Mechanics","topic":"Structural Integrity and Reliability Analysis","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Mitacs","keywords":"Finite element method; Structural engineering; Ultimate tensile strength; Materials science; Extended finite element method; Pipeline (software); Fracture (geology); Displacement (psychology); Fracture mechanics; Composite material; Engineering; Mechanical engineering","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":[0.0003129012,0.0005108874,0.0002624009,0.0005100989,0.0001508049,0.0002323539,0.0005606816,0.0006557729,0.00160935],"category_scores_gemma":[0.0007676965,0.0002450422,0.0004153443,0.0002582594,0.0001775405,0.0002919941,0.0001964625,0.0001925338,0.0003490351],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002780074,"about_ca_system_score_gemma":0.0003445363,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00366109,"about_ca_topic_score_gemma":0.003149473,"domain_scores_codex":[0.9998826,0.00002381706,0.00001218776,0.00002169156,0.00004548209,0.00001426251],"domain_scores_gemma":[0.999749,0.000115042,0.00003476345,0.00002822186,0.00006594427,0.000007021427],"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":[0.0000650477,0.00006724522,0.004505839,0.00008901062,0.00001115032,0.0001319256,0.00006098563,0.9283489,0.04383239,0.0008585695,0.0001993535,0.02182947],"study_design_scores_gemma":[0.000004045929,0.00006208119,0.001851312,0.000006175426,0.00000269006,0.00002227809,0.000008078814,0.992439,0.005277996,0.0000759094,0.0002452836,0.000005058429],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8124922,0.0002400248,0.1797458,0.00006532817,0.0000189105,0.00009990907,0.0005069888,0.0005776101,0.006253232],"genre_scores_gemma":[0.9667132,0.0001161194,0.03113975,0.000006839222,0.00000238295,0.0000792409,0.0002786039,0.00003420587,0.001629653],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00366109,"threshold_uncertainty_score":0.007279575,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04522142155446559,"score_gpt":0.2627027637757569,"score_spread":0.2174813422212913,"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."}}