{"id":"W2747274704","doi":"10.1088/1741-2552/aa8735","title":"Evaluation of high-density, multi-contact nerve cuffs for activation of grasp muscles in monkeys","year":2017,"lang":"en","type":"article","venue":"Journal of Neural Engineering","topic":"Neuroscience and Neural Engineering","field":"Neuroscience","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Institut Universitaire en Santé Mentale de Québec","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute of Arthritis and Musculoskeletal and Skin Diseases; National Institute of Neurological Disorders and Stroke; National Institutes of Health; National Science Foundation","keywords":"Functional electrical stimulation; GRASP; Stimulation; Biomedical engineering; Medicine; Implant; Anatomy; Physical medicine and rehabilitation; Computer science; Surgery","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":[0.0007147456,0.0004442935,0.0003130056,0.0004233487,0.0002070071,0.0002407489,0.0004068881,0.0006264509,0.0007862101],"category_scores_gemma":[0.001285471,0.0001976449,0.0002631113,0.0001101416,0.0004485071,0.0005184994,0.0003046026,0.0002838725,0.000209962],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002455085,"about_ca_system_score_gemma":0.0002392836,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003451048,"about_ca_topic_score_gemma":0.0007549005,"domain_scores_codex":[0.9996613,0.00005748465,0.00003711446,0.00008869312,0.00009875719,0.00005661606],"domain_scores_gemma":[0.9994011,0.0001652144,0.0001276767,0.00008416039,0.0001125488,0.0001091875],"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":[0.0001919039,0.0001777906,0.0002759616,0.0000971694,0.000009480768,0.0000466214,0.00004958839,0.0002508134,0.9936222,0.00004084149,0.00004625992,0.00519141],"study_design_scores_gemma":[0.0001129064,0.02164835,0.01328422,0.00005458119,0.00009031897,0.000647089,0.0001385795,0.004092707,0.9570113,0.0001651053,0.002735553,0.00001920352],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9806023,0.000997601,0.01730808,0.000105871,0.00005775403,0.0001822375,0.00009090145,0.0001001951,0.0005551178],"genre_scores_gemma":[0.9718402,0.0008360613,0.02509979,0.000109397,0.0000351295,0.000419933,0.0001324477,0.00003697208,0.001490082],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007862101,"threshold_uncertainty_score":0.003780007,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1189229729879594,"score_gpt":0.3342564993412101,"score_spread":0.2153335263532507,"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."}}