{"id":"W2254458703","doi":"10.1016/j.ijsolstr.2016.01.012","title":"Corrigendum to “Shot peening and peen forming finite element modelling - Towards a quantitative method”","year":2016,"lang":"en","type":"erratum","venue":"International Journal of Solids and Structures","topic":"Surface Treatment and Residual Stress","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal; National Research Council Canada; Polytechnique Montréal","funders":"","keywords":"Finite element method; Shot peening; Shot (pellet); Peening; Structural engineering; Materials science; Mechanical engineering; Engineering; Composite material; Metallurgy; Residual stress","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001782943,0.0003019481,0.000428325,0.0003375874,0.00007237645,0.0001595049,0.0002444177,0.0001743325,0.00005931989],"category_scores_gemma":[0.00004024981,0.0002036444,0.0001125171,0.00004283626,0.00003222475,0.0001714418,0.00008392496,0.0004048213,8.325857e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001035913,"about_ca_system_score_gemma":0.00005237095,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005346206,"about_ca_topic_score_gemma":0.00002072398,"domain_scores_codex":[0.9985693,0.00002903856,0.0004690227,0.0001737876,0.0005355622,0.0002232679],"domain_scores_gemma":[0.9991988,0.00009413425,0.0002165265,0.0000672967,0.0002732763,0.000149974],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000999589,0.00005403408,0.001154681,0.0005205306,0.01001476,0.001007265,0.01771751,0.3064945,0.001701696,0.005057348,0.3031513,0.3521268],"study_design_scores_gemma":[0.004947758,0.001568911,0.003131468,0.005225611,0.0009759372,0.001061716,0.003622743,0.2174497,0.006598506,0.02974264,0.7229936,0.002681466],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07882014,0.04184731,0.7604595,0.001876387,0.09730466,0.0007119544,0.001235987,0.0001407172,0.01760339],"genre_scores_gemma":[0.6675425,0.03537305,0.2508048,0.0004388633,0.01133076,0.00002812791,0.0002174654,0.0003697639,0.03389468],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5887223,"threshold_uncertainty_score":0.8304378,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04843287067002085,"score_gpt":0.3268443055717845,"score_spread":0.2784114349017637,"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."}}