{"id":"W3014901723","doi":"10.1007/s11837-020-04131-6","title":"Deep Learning and Design for Additive Manufacturing: A Framework for Microlattice Architecture","year":2020,"lang":"en","type":"article","venue":"JOM","topic":"Additive Manufacturing and 3D Printing Technologies","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of New Brunswick","funders":"Harrison McCain Foundation; Natural Sciences and Engineering Research Council of Canada; Atlantic Canada Opportunities Agency; Mitacs; New Brunswick Innovation Foundation","keywords":"Autoencoder; Encoder; Computer science; Component (thermodynamics); Graph; Artificial intelligence; Deep learning; Representation (politics); Convolutional neural network; Pattern recognition (psychology); Algorithm; Theoretical computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007440693,0.0001827776,0.0001915149,0.00004676644,0.0001333395,0.00005097218,0.0001236641,0.0001588958,0.00001563114],"category_scores_gemma":[0.0005983108,0.0001757533,0.0000662421,0.00003854488,0.00004266308,0.00003772712,0.00005519371,0.0003897942,0.000006161106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001742136,"about_ca_system_score_gemma":0.000004189535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":5.372779e-7,"about_ca_topic_score_gemma":3.952246e-7,"domain_scores_codex":[0.9992854,0.00001543601,0.0001216783,0.0002441433,0.00005132979,0.0002820207],"domain_scores_gemma":[0.9987513,0.001043173,0.00003694601,0.00008499722,0.00001826883,0.00006530972],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003044814,0.00001905358,0.00005450803,0.0009288141,0.0003800375,0.000008405328,0.003813072,0.06841484,0.003574029,0.002960489,0.004507485,0.9150348],"study_design_scores_gemma":[0.0007176369,0.0004116753,0.0004480305,0.0001116449,0.00008148721,0.000009128874,0.0007100354,0.06476516,0.6693973,0.05047881,0.2122527,0.0006164066],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02257679,0.0002583649,0.9747501,0.0006428914,0.00007490868,0.0004740193,0.00002848897,0.001101212,0.00009319988],"genre_scores_gemma":[0.6443354,0.00004152122,0.3550854,0.0001257509,0.0001882346,0.0001379165,0.00001224765,0.00004939049,0.00002406625],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9144184,"threshold_uncertainty_score":0.7167013,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02148244653095499,"score_gpt":0.2382670988646866,"score_spread":0.2167846523337316,"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."}}