{"id":"W2792855610","doi":"10.1016/j.jmbbm.2018.01.029","title":"Modelling a soft composite accumulator for human mobility assist devices","year":2018,"lang":"en","type":"article","venue":"Journal of the mechanical behavior of biomedical materials/Journal of mechanical behavior of biomedical materials","topic":"Prosthetics and Rehabilitation Robotics","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Artificial muscle; Hydraulic accumulator; Dissipation; Accumulator (cryptography); Composite number; Elasticity (physics); Mechanical engineering; Computer science; Replicate; Structural engineering; Mechanical energy; Materials science; Engineering; Artificial intelligence; Actuator; Composite material; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.006039163,0.0006359945,0.002657669,0.0005129036,0.0002089546,0.0001248192,0.002150584,0.001005237,0.0007549138],"category_scores_gemma":[0.0007197349,0.0004168337,0.001102613,0.0005119928,0.001280167,0.0002970393,0.0004239744,0.0006351139,0.000005285409],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002366987,"about_ca_system_score_gemma":0.0002972793,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002223842,"about_ca_topic_score_gemma":0.000001601373,"domain_scores_codex":[0.9892805,0.0005105072,0.006593488,0.0003927699,0.002463856,0.000758881],"domain_scores_gemma":[0.9925281,0.0005846788,0.003514245,0.0007105601,0.001770987,0.0008913755],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006998348,0.002063375,0.00004176707,0.0005911021,0.0002077177,0.00003285406,0.00008014609,0.00006543308,0.9939611,0.0007298064,0.0001743133,0.001352541],"study_design_scores_gemma":[0.003452346,0.004899868,0.0009131273,0.001318399,0.001779191,0.0004342315,0.0001004341,0.0005991269,0.9824297,0.003104732,0.00048987,0.0004789419],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.939512,0.0001375355,0.0501181,0.0004162441,0.008267527,0.0008619414,0.0006497925,0.00003567792,0.000001197834],"genre_scores_gemma":[0.9747239,0.00004466854,0.02372756,0.00005334567,0.001281334,0.00003426834,0.00002105032,0.0001081442,0.000005737397],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03521192,"threshold_uncertainty_score":0.9998283,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02901474603085233,"score_gpt":0.2964711379558254,"score_spread":0.267456391924973,"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."}}