{"id":"W2999895175","doi":"10.1109/tmag.2019.2960731","title":"Sequence-Based Environment for Topology Optimization","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Magnetics","topic":"Topology Optimization in Engineering","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Leverage (statistics); Discrete space; Topology optimization; Sequence (biology); Smoothing; Mathematical optimization; Topology (electrical circuits); Discretization; Actuator; Algorithm; Mathematics; Finite element method; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000679564,0.0007183203,0.0006913439,0.0007085452,0.0004716134,0.0007720708,0.001254297,0.0006750051,0.008305914],"category_scores_gemma":[0.001422637,0.0004811617,0.0007397097,0.0006319071,0.0006952712,0.0009753904,0.00129361,0.001052326,0.001973323],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004399339,"about_ca_system_score_gemma":0.0006376717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007710261,"about_ca_topic_score_gemma":0.001261144,"domain_scores_codex":[0.999491,0.0001577113,0.00002025309,0.00006836663,0.0002309269,0.0000318147],"domain_scores_gemma":[0.9995353,0.0002525265,0.00004440481,0.00007407145,0.00006404343,0.00002963289],"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":[0.00009022779,0.00009767232,0.000264975,0.0001425922,0.00003575451,0.0001100001,0.0001118206,0.7696719,0.01276277,0.09905457,0.002439847,0.1152179],"study_design_scores_gemma":[0.00002738305,0.0000525196,0.00004445301,0.00001027823,0.000004695745,0.00004561329,0.00001202144,0.9644132,0.002189584,0.02171062,0.01148163,0.000007982248],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002747931,0.00005650962,0.9939566,0.00002687651,0.00002162009,0.00002546714,0.00002839456,0.0004107046,0.002725866],"genre_scores_gemma":[0.1258482,0.0002649817,0.8664302,0.00005686968,0.00005192621,0.0003288724,0.0002760453,0.0003550019,0.006387945],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008305914,"threshold_uncertainty_score":0.02778602,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02133057857686534,"score_gpt":0.2122443344357237,"score_spread":0.1909137558588584,"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."}}