{"id":"W4414027182","doi":"10.1108/ec-04-2024-0266","title":"Strength simulation of metro train bogie frame using edge-based and face-based smoothed finite element method","year":2025,"lang":"en","type":"article","venue":"Engineering Computations","topic":"Railway Engineering and Dynamics","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Education and Child Care","funders":"Natural Science Foundation of Hunan Province; National Natural Science Foundation of China","keywords":"Bogie; Finite element method; Structural engineering; Frame (networking); Enhanced Data Rates for GSM Evolution; Face (sociological concept); Engineering; Computer science; Mechanical 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001894816,0.0002367753,0.0002826784,0.0005466641,0.00005423507,0.00003979304,0.0001019464,0.00009894318,0.000005544804],"category_scores_gemma":[0.0001454746,0.0002817458,0.00008011275,0.0005980023,0.000017693,0.00006665385,0.00001521742,0.0001917412,7.014651e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001217703,"about_ca_system_score_gemma":0.0000548057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000086991,"about_ca_topic_score_gemma":0.000002468799,"domain_scores_codex":[0.9990207,0.00002378976,0.0003765107,0.0001915292,0.0001430802,0.0002443822],"domain_scores_gemma":[0.9986678,0.0009592306,0.00003995751,0.0002064064,0.00005866247,0.00006793807],"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":[0.000003141381,0.00002362252,0.0001045689,0.0002711582,0.00006926363,8.191007e-7,0.00008346604,0.9946406,0.00178651,0.0004406766,0.000006657019,0.002569501],"study_design_scores_gemma":[0.0005806913,0.00002356873,0.001169592,0.0001466772,0.00006069658,1.803097e-7,0.00002778725,0.9957899,0.001642554,0.00007810407,0.0002551927,0.0002250473],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08149007,0.0002426638,0.9171682,0.00003103055,0.0002847706,0.0001874953,0.00006245836,0.0004861767,0.00004712402],"genre_scores_gemma":[0.7364551,0.000001891231,0.2634144,0.00001332219,0.00001961627,0.00001140957,0.0000427613,0.00003508693,0.00000632854],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6549651,"threshold_uncertainty_score":0.9999635,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01501291207869556,"score_gpt":0.276025857707362,"score_spread":0.2610129456286665,"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."}}