{"id":"W2230449664","doi":"10.1016/j.ijmecsci.2016.01.005","title":"Robust methodology to simulate real shot peening process using discrete-continuum coupling method","year":2016,"lang":"en","type":"article","venue":"International Journal of Mechanical Sciences","topic":"Surface Treatment and Residual Stress","field":"Engineering","cited_by":69,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Héroux-Devtek","keywords":"Shot peening; Peening; Residual stress; Computation; Residual; Shot (pellet); Coupling (piping); Process (computing); Materials science; Structural engineering; Computer science; Engineering; Algorithm; Metallurgy","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":[],"consensus_categories":[],"category_scores_codex":[0.001505217,0.0001304021,0.0002420093,0.0001761211,0.00007505807,0.00007604007,0.0006225655,0.00006605453,0.0001104473],"category_scores_gemma":[0.0003336262,0.00007397454,0.0001011658,0.0001822707,0.00005074243,0.0003268055,0.00006628889,0.0001105784,0.000009865546],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001084028,"about_ca_system_score_gemma":0.00004337627,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002492745,"about_ca_topic_score_gemma":0.00003725712,"domain_scores_codex":[0.9984523,0.00006651284,0.0004187893,0.0001702181,0.0006402797,0.0002519111],"domain_scores_gemma":[0.9986677,0.0007636056,0.0001443346,0.00005365539,0.0002273415,0.0001433997],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00009603834,0.00002257602,0.002173323,0.000004587342,0.0001620134,0.00006213372,0.0002077565,0.8177466,0.1683087,0.0004120665,0.00003245925,0.01077174],"study_design_scores_gemma":[0.001935343,0.0006095494,0.001431731,0.0008620699,0.0001341196,0.0003598618,0.001008664,0.4465145,0.5363621,0.009777736,0.0003848365,0.0006195256],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6416956,0.00004967192,0.3562478,0.000577901,0.001145892,0.00004505213,0.000004858369,0.00002678298,0.0002064919],"genre_scores_gemma":[0.894055,0.00004563994,0.1055257,0.00002478381,0.0003111234,0.00000111271,2.147414e-7,0.00001079716,0.00002560361],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3712322,"threshold_uncertainty_score":0.3016594,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1762197625164019,"score_gpt":0.4147949335448278,"score_spread":0.2385751710284259,"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."}}