{"id":"W1989843096","doi":"10.3389/fnins.2014.00302","title":"Does EMG control lead to distinct motor adaptation?","year":2014,"lang":"en","type":"article","venue":"Frontiers in Neuroscience","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"National Defense Science and Engineering Graduate; U.S. Department of Defense; National Science Foundation","keywords":"Adaptation (eye); Computer science; Interface (matter); Motor control; Control (management); Bayesian probability; Brain–computer interface; Task (project management); Human–computer interaction; Artificial intelligence; Psychology; Engineering; Electroencephalography; Neuroscience","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":[],"consensus_categories":[],"category_scores_codex":[0.0001051756,0.00009665032,0.0001237446,0.0001774141,0.00007805372,0.00003500729,0.000182646,0.0000190311,0.000002062273],"category_scores_gemma":[0.000191138,0.00007167791,0.00002760892,0.0004615039,0.0000566594,0.0001338572,0.00001284922,0.00008249589,0.000001260396],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002692377,"about_ca_system_score_gemma":0.000005085984,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000653803,"about_ca_topic_score_gemma":0.00001705044,"domain_scores_codex":[0.9992483,0.00002431447,0.0001263438,0.0002043058,0.0001463253,0.0002504629],"domain_scores_gemma":[0.9997371,0.0000326613,0.00001549935,0.0001382298,0.00001640057,0.00006009768],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001042653,0.0001407323,0.3165447,0.0001200831,0.00002384202,0.00001005428,0.00242061,0.08590034,0.1051942,0.001350619,0.06820335,0.4199871],"study_design_scores_gemma":[0.0003884419,0.0000985309,0.5893381,0.00001382949,0.000003879973,5.198641e-7,0.0001170263,0.3570442,0.0008329066,0.0003996928,0.05152392,0.0002390318],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.101513,0.00003483138,0.8921636,0.0006029998,0.003424905,0.0002383538,0.00000705425,0.0002134587,0.001801806],"genre_scores_gemma":[0.9968725,0.00001365626,0.002308282,0.0005586858,0.00004649094,0.00004177453,3.364041e-7,0.00001001064,0.0001482278],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8953595,"threshold_uncertainty_score":0.2922941,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007640073390003272,"score_gpt":0.2019716102697147,"score_spread":0.1943315368797114,"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."}}