{"id":"W4379618394","doi":"10.32920/23330861","title":"Generative Design for 3D Printing of Advanced Aerial Drones","year":2023,"lang":"en","type":"preprint","venue":"","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Drone; Generative Design; Frame (networking); 3D printing; Rapid prototyping; Computer science; Generative grammar; Aerospace; Finite element method; Systems engineering; Engineering drawing; Interdependence; Artificial intelligence; Engineering; Mechanical engineering; Aerospace engineering; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004605796,0.0005093985,0.0003066629,0.0005734272,0.000257636,0.001214424,0.000646936,0.0005688095,0.003985648],"category_scores_gemma":[0.0007838834,0.000407475,0.0009472019,0.0003744726,0.0006463989,0.0004348077,0.0007916439,0.0005313868,0.0007687064],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004134796,"about_ca_system_score_gemma":0.0002716162,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005279935,"about_ca_topic_score_gemma":0.0008488755,"domain_scores_codex":[0.9996356,0.00008667068,0.00001760635,0.00005489583,0.0001806005,0.0000245313],"domain_scores_gemma":[0.9996682,0.0001316076,0.00003657232,0.0001015234,0.00004927082,0.00001287139],"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":[0.00008662788,0.00005836924,0.001070857,0.0005082185,0.00007234276,0.0005183194,0.0004980528,0.6978573,0.0974886,0.0615666,0.001843262,0.1384314],"study_design_scores_gemma":[0.00003359511,0.0002631656,0.0010983,0.00008988444,0.00006098793,0.0006241969,0.000165508,0.8725037,0.05490996,0.01814419,0.05203698,0.00006956563],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02745276,0.000357319,0.9564483,0.00009244919,0.00006028343,0.0000831097,0.00009122818,0.0007719157,0.01464265],"genre_scores_gemma":[0.5048343,0.0007436313,0.4853724,0.00009255546,0.00002227831,0.0001712086,0.0002151965,0.0003506744,0.008197872],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003985648,"threshold_uncertainty_score":0.01333332,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1039600137596035,"score_gpt":0.2784492065522709,"score_spread":0.1744891927926674,"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."}}