{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005589871,0.00009627817,0.0001489135,0.0002051656,0.00008845198,0.0001089098,0.0004717616,0.00004030235,0.000005367169],"category_scores_gemma":[0.001371126,0.00007613637,0.00003549945,0.0001195022,0.00007383836,0.0009820469,0.00004485801,0.0001620923,0.000001832645],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001227811,"about_ca_system_score_gemma":0.00007177222,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004487825,"about_ca_topic_score_gemma":0.000001463367,"domain_scores_codex":[0.9985073,0.0002139889,0.0004718213,0.0001222315,0.0005904579,0.00009418346],"domain_scores_gemma":[0.9977326,0.0007861272,0.0005106389,0.0001835294,0.0007345255,0.00005255958],"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":[0.0003093223,0.0002152925,0.0007787023,0.00001904767,0.00002017984,0.000004401074,0.004688553,0.08081368,0.5625736,0.001535391,0.00006420849,0.3489776],"study_design_scores_gemma":[0.001352075,0.0004237669,0.02079522,0.000871537,0.00003939704,0.0002089547,0.0007598571,0.9422032,0.02929223,0.003544513,0.0003303593,0.00017885],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9955878,0.000551168,0.001822997,0.001021414,0.000665072,0.0001531263,0.000002349101,0.00004296862,0.0001531399],"genre_scores_gemma":[0.9930632,0.0009987717,0.005726346,0.0001214158,0.0000706994,0.000004076981,0.000004237697,0.00000513467,0.00000613285],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8613896,"threshold_uncertainty_score":0.3104751,"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."}}