{"id":"W2897535261","doi":"10.1186/s12938-018-0593-2","title":"Regressing grasping using force myography: an exploratory study","year":2018,"lang":"en","type":"article","venue":"BioMedical Engineering OnLine","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Wrist; Thumb; Computer science; Position (finance); Artificial intelligence; Prosthesis; Physical medicine and rehabilitation; Amputation; Orthodontics; Medicine; Anatomy; Surgery","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":[0.0002007669,0.0002850149,0.0002822489,0.000529488,0.0001611251,0.00004672728,0.0002104852,0.0001025931,0.00002383021],"category_scores_gemma":[0.00005675039,0.0002790317,0.0000787575,0.001066158,0.0001154448,0.0002871481,0.00004937178,0.0002552324,0.000001742975],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000617474,"about_ca_system_score_gemma":0.0000202176,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009737682,"about_ca_topic_score_gemma":0.00001967014,"domain_scores_codex":[0.9984995,0.00002359725,0.0003524348,0.0002944353,0.0003380008,0.0004920267],"domain_scores_gemma":[0.9992915,0.00003512386,0.00003524716,0.0003122688,0.00007664678,0.0002492187],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001029848,0.003459596,0.00636066,0.0007018739,0.002134162,0.0001699258,0.01216998,0.02866466,0.6924152,0.0003546022,0.002170172,0.2512962],"study_design_scores_gemma":[0.001529343,0.0010876,0.02427432,0.0002955435,0.0001062368,0.00002156587,0.00242225,0.9566767,0.003108562,0.00005005251,0.009286443,0.001141394],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.924597,0.0003262205,0.07282891,0.0000332553,0.0008025955,0.0001836561,0.00000688173,0.001187143,0.00003428029],"genre_scores_gemma":[0.9921874,0.00002399689,0.006561462,0.00004838826,0.001053189,0.00001642863,0.0000289518,0.0000756261,0.0000045139],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.928012,"threshold_uncertainty_score":0.9999662,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02764778088341072,"score_gpt":0.2748332555787839,"score_spread":0.2471854746953732,"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."}}