{"id":"W2177408885","doi":"10.1504/ijamc.2009.026861","title":"Haptic rehabilitation exercises performance evaluation using automated inference systems","year":2009,"lang":"en","type":"article","venue":"International Journal of Advanced Media and Communication","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Haptic technology; Consistency (knowledge bases); Adaptive neuro fuzzy inference system; Inference; Inference system; Fuzzy inference system; Machine learning; Rehabilitation; Artificial intelligence; Fuzzy logic; Human–computer interaction; Data mining; Fuzzy control system; Medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001644546,0.000510408,0.0004360055,0.0007905139,0.000245505,0.0006830501,0.0005280826,0.0005299784,0.001702817],"category_scores_gemma":[0.007165825,0.0001774845,0.0002500489,0.0003140383,0.00032538,0.0004281158,0.0004200596,0.0003740678,0.0002715543],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003850638,"about_ca_system_score_gemma":0.0003404181,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002643495,"about_ca_topic_score_gemma":0.00215558,"domain_scores_codex":[0.9986501,0.000463148,0.000121257,0.0001845141,0.0005204267,0.00006049221],"domain_scores_gemma":[0.9965914,0.002221385,0.0002549926,0.0002257251,0.0006576913,0.00004882559],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001901024,0.0006398013,0.01017121,0.0004782098,0.0001865643,0.0001898494,0.0007024569,0.1574925,0.09657163,0.001943223,0.001271304,0.7284522],"study_design_scores_gemma":[0.0001541686,0.001156969,0.02331245,0.00004818513,0.00007038416,0.0002154569,0.0001156409,0.9286849,0.04362407,0.001287,0.001240497,0.00009014557],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3635692,0.0002137782,0.6297203,0.0001482233,0.00007158579,0.0002996621,0.0002145725,0.002597831,0.003164838],"genre_scores_gemma":[0.9330399,0.0000553748,0.06589961,0.00002753687,0.00001899419,0.000115691,0.00008777142,0.00002588508,0.0007291887],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002643495,"threshold_uncertainty_score":0.008697331,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04275193238393751,"score_gpt":0.3579251598587203,"score_spread":0.3151732274747828,"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."}}