{"id":"W3049442785","doi":"10.1016/j.mfglet.2020.08.001","title":"Efficient planning of peen-forming patterns via artificial neural networks","year":2020,"lang":"en","type":"article","venue":"Manufacturing Letters","topic":"Metal Forming Simulation Techniques","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Ministère de l'Économie, de la Science et de l'Innovation - Québec","keywords":"Artificial neural network; Automation; Process (computing); Binary number; Nonlinear system; Ground truth; Function (biology)","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":[0.00009378504,0.0001930074,0.0002251783,0.00009380723,0.0000536503,0.00002883206,0.0001779781,0.00006064309,0.00003965371],"category_scores_gemma":[0.00001032131,0.0002006093,0.0000952119,0.00007470283,0.00002243502,0.00006421242,0.00005309761,0.0002508536,0.000006501768],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003655822,"about_ca_system_score_gemma":0.000001409668,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001062769,"about_ca_topic_score_gemma":4.03449e-7,"domain_scores_codex":[0.9989244,0.00001420143,0.0003685881,0.0001826834,0.0002087792,0.0003013016],"domain_scores_gemma":[0.9996173,0.00004990224,0.00007194711,0.0001644756,0.000008228889,0.00008815063],"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":[0.000008872961,0.00000369058,0.0005848177,0.00009403182,0.00001886044,0.00001330527,0.0004204391,0.9681771,0.02246219,0.000007069806,0.00009573965,0.008113908],"study_design_scores_gemma":[0.00007228911,0.00001585463,0.001941441,0.00003028022,0.00001071994,0.000002443306,0.00001692433,0.7109098,0.2867287,0.000009626341,0.00009618486,0.0001657621],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7327995,0.00001878702,0.2661312,0.0001821807,0.0002068765,0.0001145302,0.000002923162,0.000493813,0.00005016314],"genre_scores_gemma":[0.9984405,4.953756e-7,0.0007263032,0.0005369828,0.0002304415,0.000006437532,0.00001060946,0.00004674626,0.000001468237],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.265641,"threshold_uncertainty_score":0.8180611,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01908586756829643,"score_gpt":0.2227708298128506,"score_spread":0.2036849622445542,"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."}}