{"id":"W2593529668","doi":"10.1016/j.msea.2017.03.038","title":"Residual stress analysis in linear friction welded in-service Inconel 718 superalloy via neutron diffraction and contour method approaches","year":2017,"lang":"en","type":"article","venue":"Materials Science and Engineering A","topic":"Welding Techniques and Residual Stresses","field":"Engineering","cited_by":52,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Research Council Canada; Canadian Nuclear Laboratories; Hydro-Québec; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"National Research Council Canada; Canadian Nuclear Laboratories","keywords":"Residual stress; Inconel; Materials science; Superalloy; Welding; Neutron diffraction; Diffraction; Residual; Stress (linguistics); Composite material; Structural engineering; Computer science; Optics; Microstructure; Engineering; Physics","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":[0.0003031872,0.0002269585,0.0002863849,0.0006091783,0.0001842346,0.0003630485,0.0007146455,0.0003508173,0.0007123724],"category_scores_gemma":[0.0003800765,0.0001926815,0.0001779166,0.0004690507,0.0004707674,0.000476046,0.0001830013,0.0001885934,0.0001010975],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005725796,"about_ca_system_score_gemma":0.0003678395,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006123747,"about_ca_topic_score_gemma":0.009902817,"domain_scores_codex":[0.9998133,0.00001607851,0.000009702518,0.00003505663,0.0001089159,0.00001708239],"domain_scores_gemma":[0.9997131,0.00007120444,0.00005790542,0.00002986401,0.0001175205,0.00001032352],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0009409538,0.0001375611,0.01416031,0.0003118226,0.00002988518,0.0002298836,0.0007202084,0.07545494,0.8612849,0.006114939,0.0003229148,0.04029166],"study_design_scores_gemma":[0.00002741997,0.0003241978,0.04753977,0.00002860928,0.00003160194,0.0002409509,0.0004640003,0.5917221,0.3570489,0.0008342533,0.001679234,0.00005897142],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9554775,0.0003626796,0.04115767,0.00003041156,0.000007741511,0.00001468575,0.0001500438,0.0001411429,0.002658091],"genre_scores_gemma":[0.9934387,0.00007059235,0.005666767,0.000003209101,0.000001051092,0.000005062945,0.00006144152,0.00001574251,0.0007374709],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006123747,"threshold_uncertainty_score":0.01217622,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02373979673091856,"score_gpt":0.2542482241117261,"score_spread":0.2305084273808075,"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."}}