{"id":"W4200294959","doi":"10.1177/09544097211049640","title":"Design optimization of hopper cars employing functionally graded honeycomb sandwich panels","year":2021,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part F Journal of Rail and Rapid Transit","topic":"Topology Optimization in Engineering","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Finite element method; Sizing; Minimum mass; Structural engineering; Topology optimization; Optimal design; Reduction (mathematics); Computer science; Mathematical optimization; Mathematics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000376383,0.0005441471,0.0004712741,0.000333377,0.0001754588,0.0004549129,0.0004778008,0.0005907862,0.001347239],"category_scores_gemma":[0.0003990012,0.0003019048,0.0005522897,0.0001650434,0.0003019434,0.0002736982,0.0004844987,0.0003473496,0.0002216722],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002235604,"about_ca_system_score_gemma":0.0005510324,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007263636,"about_ca_topic_score_gemma":0.001165422,"domain_scores_codex":[0.9998398,0.00003853718,0.000005717867,0.00003228804,0.00006036602,0.00002333554],"domain_scores_gemma":[0.9998529,0.0000683582,0.00002328145,0.00001582815,0.00002664864,0.00001297835],"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":[0.00003807745,0.00003475432,0.0005362286,0.00008653102,0.00003194133,0.0000941242,0.00002741099,0.9509813,0.02953169,0.002601771,0.0001689966,0.01586725],"study_design_scores_gemma":[0.000006289456,0.00008306046,0.0003142586,0.000004460071,0.00001026828,0.00002188534,0.00001546463,0.9948606,0.003522974,0.0005716725,0.0005845525,0.000004511001],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2272833,0.0002816383,0.7655954,0.00008065641,0.00003284867,0.00009444035,0.00008219969,0.0002691277,0.006280492],"genre_scores_gemma":[0.8487928,0.0001750072,0.1484054,0.00003439924,0.00000904671,0.0001573776,0.00009285766,0.00005643102,0.002276601],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001347239,"threshold_uncertainty_score":0.004507005,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01495512594180997,"score_gpt":0.1919104481733168,"score_spread":0.1769553222315068,"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."}}