{"id":"W4295253178","doi":"10.48550/arxiv.2108.13526","title":"Computational Design of Active 3D-Printed Multi-State Structures for\\n Shape Morphing","year":2021,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Topology Optimization in Engineering","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Morphing; Computer science; Compliant mechanism; Topology optimization; Dither; Control engineering; Mechanical engineering; Engineering drawing; Finite element method; Engineering; Structural engineering; Artificial intelligence","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":[0.0003371647,0.0004569484,0.0005953137,0.0003991534,0.0003738512,0.0008534937,0.0009045286,0.001225694,0.003673207],"category_scores_gemma":[0.0006768526,0.0005096516,0.0007023719,0.0002475076,0.0007775342,0.0004502452,0.0007067931,0.0006795747,0.0003509674],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006695858,"about_ca_system_score_gemma":0.0008793913,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002056711,"about_ca_topic_score_gemma":0.002993151,"domain_scores_codex":[0.9999075,0.00002197686,0.000003365315,0.0000177141,0.00003053957,0.00001898925],"domain_scores_gemma":[0.999707,0.000193699,0.00002536084,0.0000216772,0.00003044848,0.00002183568],"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":[0.00001583438,0.00001407732,0.0001545937,0.00002287707,0.000006542823,0.0000327517,0.00001093851,0.990599,0.001142397,0.004824613,0.000186817,0.002989722],"study_design_scores_gemma":[0.000003619362,0.000004684146,0.00001595529,0.000001137365,0.000001198803,0.000002629827,0.000002539666,0.9990251,0.0001632237,0.0006393677,0.0001393875,0.000001088407],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1828738,0.0004293276,0.7816849,0.0007207398,0.0001121308,0.0001474572,0.0003118996,0.0008679471,0.03285179],"genre_scores_gemma":[0.7743942,0.0001986421,0.2168937,0.0001673695,0.00002334504,0.0004321137,0.0003213256,0.0001625208,0.007406909],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003673207,"threshold_uncertainty_score":0.01228809,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06530318911349059,"score_gpt":0.2010782066065041,"score_spread":0.1357750174930135,"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."}}