{"id":"W3034688949","doi":"10.1111/cgf.14185","title":"EMU: Efficient Muscle Simulation in Deformation Space","year":2020,"lang":"en","type":"preprint","venue":"Computer Graphics Forum","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Connaught Fund; National Science Foundation","keywords":"Parallelizable manifold; Polygon mesh; Finite element method; Scalability; Computer science; Deformation (meteorology); Key (lock); Space (punctuation); Structural engineering; Simulation; Engineering; Materials science; Algorithm; Computer graphics (images); Composite material","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009874698,0.0002830116,0.0002925172,0.0005066521,0.00007188221,0.00007743387,0.0001990332,0.0002075048,0.000003603943],"category_scores_gemma":[0.000009170351,0.0003140362,0.0001673648,0.0005833703,0.00002639501,0.00007754748,0.0002484478,0.0006212827,0.00000427351],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007849238,"about_ca_system_score_gemma":0.00001490744,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001347675,"about_ca_topic_score_gemma":0.00003260748,"domain_scores_codex":[0.9987747,0.00002958889,0.0003576613,0.0002906207,0.0002201797,0.0003272462],"domain_scores_gemma":[0.9994555,0.0000607833,0.00008047411,0.0002738255,0.00006242163,0.00006695535],"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":[0.00000406359,0.000024118,0.0008667643,0.0002183775,0.00005374758,0.000001868629,0.0006775105,0.9828779,0.00002492048,0.003562261,0.001327626,0.01036084],"study_design_scores_gemma":[0.0002182222,0.0000220579,0.05857966,0.00009442752,0.000009990898,3.251596e-7,0.00003204931,0.9354838,0.00004526164,0.002748327,0.002477568,0.0002883452],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2922758,0.0004624134,0.7023377,0.001261038,0.001630339,0.0006456657,0.0000172529,0.0008339943,0.0005358226],"genre_scores_gemma":[0.9984536,0.0001229431,0.0009777371,0.0001469765,0.0001320682,0.00003697249,0.00009186178,0.0000364573,0.000001358353],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7061779,"threshold_uncertainty_score":0.9999312,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01695194914473129,"score_gpt":0.2297872870276532,"score_spread":0.2128353378829219,"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."}}