{"id":"W4309731112","doi":"10.1016/j.cad.2022.103439","title":"BeNTO: Beam Network Topology Optimization","year":2022,"lang":"en","type":"article","venue":"Computer-Aided Design","topic":"Topology Optimization in Engineering","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Autodesk (Canada)","funders":"","keywords":"Topology optimization; Design for manufacturability; Leverage (statistics); Topology (electrical circuits); Constraint (computer-aided design); Computer science; Beam (structure); Mathematical optimization; Graph; Representation (politics); Set (abstract data type); Theoretical computer science; Mathematics; Engineering; Finite element method; Geometry; Artificial intelligence; Structural engineering; 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":[0.0002520346,0.000204286,0.000230944,0.0001491945,0.0002628267,0.00003264094,0.0003321685,0.00008331808,0.0006979269],"category_scores_gemma":[0.00001061042,0.0002664319,0.0000556079,0.0004526157,0.00003898012,0.0001146394,0.000165169,0.0002919202,0.00002328552],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000166642,"about_ca_system_score_gemma":0.00002183495,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002091315,"about_ca_topic_score_gemma":2.517365e-7,"domain_scores_codex":[0.9987479,0.0001122261,0.000309664,0.0002557463,0.0001527417,0.0004217226],"domain_scores_gemma":[0.9993619,0.0001613167,0.00004407985,0.0003194309,0.00003106408,0.00008221807],"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":[0.000005885422,0.00001535079,0.00006104566,0.000008702305,0.00004267857,0.00001381925,0.00009788114,0.9754385,0.00003771082,0.001027331,0.02219983,0.001051206],"study_design_scores_gemma":[0.0003144258,0.00009951022,0.00007468194,0.000004420767,0.00001371995,0.00006261871,0.00001349957,0.9941049,0.0001151366,0.0003043348,0.004640307,0.0002524748],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0006663641,0.0004608795,0.9929385,0.00007999628,0.003757021,0.0002671611,0.000004479441,0.001224676,0.0006009469],"genre_scores_gemma":[0.2274205,0.0000573613,0.7708685,0.0003339518,0.0007534672,0.0001886768,0.00007499331,0.0001201443,0.0001823574],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2267542,"threshold_uncertainty_score":0.9999788,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01243085757383147,"score_gpt":0.1988663150488811,"score_spread":0.1864354574750497,"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."}}