{"id":"W4307052046","doi":"10.1007/s40964-022-00352-0","title":"Evolutionary computation to design additively manufactured optimal heterogeneous lattice structures","year":2022,"lang":"en","type":"article","venue":"Progress in Additive Manufacturing","topic":"Additive Manufacturing and 3D Printing Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of New Brunswick","funders":"Harrison McCain Foundation; Mitacs","keywords":"Computation; Lattice (music); Genetic algorithm; Mathematical optimization; Computer science; Observable; Optimization algorithm; Global optimization; Algorithm; Evolutionary algorithm; Mathematics; Physics","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":[0.0003514017,0.0004739277,0.0006254027,0.0006467322,0.0003772234,0.0007303227,0.0006908463,0.001093135,0.003074139],"category_scores_gemma":[0.001034469,0.00052042,0.0006928926,0.000507608,0.000628915,0.0004562344,0.0006354092,0.0007580651,0.000243961],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008189618,"about_ca_system_score_gemma":0.000815202,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003536394,"about_ca_topic_score_gemma":0.005043566,"domain_scores_codex":[0.9998803,0.0000348057,0.000004488401,0.00001633979,0.00004498782,0.0000190662],"domain_scores_gemma":[0.9997258,0.000175608,0.00001945274,0.00001821011,0.00004613803,0.00001481902],"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":[0.00000826308,0.00001356089,0.00009222551,0.00001086169,0.000009212271,0.00001390883,0.000009883239,0.9864724,0.0006620414,0.005783731,0.0001371594,0.006786775],"study_design_scores_gemma":[0.000003842615,0.000007192938,0.00001509243,0.000001766763,0.000002053762,0.000002627485,0.000003536494,0.9985927,0.0001648772,0.001063413,0.0001417303,0.000001089757],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1017393,0.0003159062,0.8776074,0.0002845922,0.0001074994,0.00008549718,0.00006105862,0.0002226293,0.01957608],"genre_scores_gemma":[0.7081356,0.0001618183,0.284559,0.0001220562,0.00002845433,0.0002041217,0.00009425158,0.0001075946,0.006587145],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003536394,"threshold_uncertainty_score":0.01028401,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01969532098739531,"score_gpt":0.2477308288638362,"score_spread":0.2280355078764409,"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."}}