{"id":"W2127683826","doi":"10.1115/imece2013-63628","title":"Computational Optimization of Arc Welding Parameters Using Coupled Genetic Algorithms and Finite Element Method","year":2013,"lang":"en","type":"article","venue":"Volume 2A: Advanced Manufacturing","topic":"Welding Techniques and Residual Stresses","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"U.S. Department of Energy","keywords":"Welding; Finite element method; Genetic algorithm; Arc welding; Process (computing); Distortion (music); Computer science; Algorithm; Mechanical engineering; Engineering; Structural engineering","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":[0.00007120669,0.0001810261,0.0002230564,0.0001694045,0.00008115693,0.00004909658,0.00009127569,0.00005383715,0.0000837176],"category_scores_gemma":[0.00001509911,0.0001842738,0.00004046267,0.00009259294,0.00003246238,0.0001958392,0.00006057708,0.0001110022,0.000001512465],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000613689,"about_ca_system_score_gemma":0.000006263994,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000128133,"about_ca_topic_score_gemma":0.000003121752,"domain_scores_codex":[0.9990498,0.00001982105,0.0003182667,0.000206099,0.0001607236,0.0002452248],"domain_scores_gemma":[0.9995561,0.0001211076,0.00008881053,0.0001257947,0.00004017394,0.0000680688],"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":[0.000002481655,0.000006956157,0.0001526027,0.0001077633,0.000030499,0.000001550705,0.00005163419,0.9879388,0.001405463,0.000002188291,0.00001659732,0.01028341],"study_design_scores_gemma":[0.0002291587,0.00002482802,0.001200807,0.00008327321,0.00001816906,0.000004913386,0.00003102547,0.8795566,0.1177956,0.0008081733,0.00006180918,0.0001856535],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3613225,0.0001637856,0.6380479,0.0000133838,0.00006856713,0.0002103663,0.000004656949,0.0001417263,0.00002709515],"genre_scores_gemma":[0.4314405,0.00009059824,0.5683815,0.000008517261,0.00001643954,0.00001785335,0.000007430449,0.00002398927,0.00001308473],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1163901,"threshold_uncertainty_score":0.7514468,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01212424005407791,"score_gpt":0.2455178095973839,"score_spread":0.233393569543306,"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."}}