{"id":"W2123150539","doi":"10.1109/iembs.2009.5332555","title":"Recombination of common sensory-motor impairment evaluation techniques using a committee of classifiers","year":2009,"lang":"en","type":"article","venue":"","topic":"Stroke Rehabilitation and Recovery","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institutes of Health Research; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Motor impairment; Sensory system; Artificial intelligence; Normalization (sociology); Classifier (UML); Support vector machine; Computer science; Physical medicine and rehabilitation; Machine learning; Pattern recognition (psychology); Psychology; Medicine; Cognitive psychology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008967252,0.001097872,0.001877885,0.001772915,0.0005965619,0.001194633,0.001146087,0.0008768957,0.0007480402],"category_scores_gemma":[0.01799071,0.0004420094,0.001412475,0.0008313915,0.0004432136,0.001077634,0.001247572,0.001254255,0.0006568121],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008394071,"about_ca_system_score_gemma":0.001049458,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002376163,"about_ca_topic_score_gemma":0.001510773,"domain_scores_codex":[0.9961483,0.001202593,0.0004366485,0.0009365825,0.0008511516,0.0004246102],"domain_scores_gemma":[0.991719,0.002543744,0.0005610539,0.001455351,0.003484885,0.0002359537],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001031025,0.0007815636,0.03632711,0.00009959454,0.0004848037,0.0001478252,0.000508702,0.1402989,0.02209876,0.001561057,0.002965744,0.7936949],"study_design_scores_gemma":[0.00004339536,0.000837691,0.01476492,0.00003341086,0.0002269482,0.0002008128,0.0002135098,0.9587772,0.02140539,0.001928241,0.001502168,0.00006641538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3352379,0.0003631794,0.6601959,0.0001636071,0.00009474669,0.0004646499,0.0001453253,0.001345627,0.001989043],"genre_scores_gemma":[0.8175542,0.0001213576,0.1792114,0.000083369,0.00005184803,0.0004936125,0.0008067529,0.00009221368,0.001585282],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008967252,"threshold_uncertainty_score":0.0474239,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04620769114651361,"score_gpt":0.3626334548343924,"score_spread":0.3164257636878788,"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."}}