{"id":"W4362660145","doi":"10.4271/2023-01-0029","title":"Frequency-Constrained Multi-Material Topology Optimization: Commercial Solver Integrable Sensitivities","year":2023,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Topology Optimization in Engineering","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"General Motors (Canada); Queen's University","funders":"","keywords":"Topology optimization; Solver; Computer science; Aerospace; Finite element method; Automotive industry; Constraint (computer-aided design); Mathematical optimization; Class (philosophy); Optimization problem; Engineering optimization; Topology (electrical circuits); Industrial engineering; Engineering; Mechanical engineering; Mathematics; Algorithm; 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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004699099,0.0008603549,0.001004457,0.0004880589,0.0004758132,0.0001505782,0.0007344108,0.001218853,0.00194482],"category_scores_gemma":[0.0005422611,0.0008755619,0.0003213326,0.001110266,0.001740517,0.00054828,0.0003019933,0.001210121,0.0002190899],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003242887,"about_ca_system_score_gemma":0.00008975637,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004083163,"about_ca_topic_score_gemma":0.01546764,"domain_scores_codex":[0.9962102,0.0001419623,0.00108073,0.0008593518,0.0004683844,0.001239351],"domain_scores_gemma":[0.9980029,0.0004660104,0.0001122269,0.0009945952,0.0001156956,0.0003085096],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00007070279,0.00009825534,0.00008730913,0.0000756013,0.0001006706,0.0001307257,0.0001006982,0.1696614,0.8136216,0.01023784,0.005041984,0.0007732971],"study_design_scores_gemma":[0.003492016,0.001062151,0.9663714,0.0004550636,0.0002484864,0.0006753986,0.0009150717,0.0006061269,0.001633464,0.002240746,0.01872705,0.003573019],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6754532,0.0007665706,0.003478338,0.01601767,0.01583695,0.005631309,0.001823476,0.09894788,0.1820446],"genre_scores_gemma":[0.9559976,0.0002986091,0.04147402,0.000623499,0.00033416,0.0003188241,0.0003129977,0.0002410039,0.0003992378],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9662841,"threshold_uncertainty_score":0.9993695,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01240166068454959,"score_gpt":0.2337274612230203,"score_spread":0.2213258005384707,"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."}}