{"id":"W2106320830","doi":"10.1109/tbme.2005.869787","title":"Modified Local Discriminant Bases Algorithm and Its Application in Analysis of Human Knee Joint Vibration Signals","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":77,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Linear discriminant analysis; Pattern recognition (psychology); Knee Joint; Basis (linear algebra); Artificial intelligence; Wavelet; Discriminant; Classifier (UML); Vibration; Computer science; Statistical classification; Population; Basis function; Algorithm; Mathematics; Medicine; Acoustics","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":[0.00008689267,0.0001459391,0.0002671001,0.0009898993,0.00004556877,0.00000900832,0.00004061903,0.00008265094,0.00001074852],"category_scores_gemma":[0.000001954464,0.0001463334,0.00008841562,0.001185105,0.00003681463,0.00009464475,8.466978e-7,0.0001407902,3.381971e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005413987,"about_ca_system_score_gemma":0.000004742236,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001306471,"about_ca_topic_score_gemma":0.00005043608,"domain_scores_codex":[0.9991024,0.00001010314,0.0003537182,0.0001671544,0.0001856777,0.0001809583],"domain_scores_gemma":[0.9997488,0.00004898325,0.00002785623,0.00009549667,0.00002224141,0.00005661133],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003193277,0.0001020286,0.000003997947,0.0000566518,0.00017574,0.000001018837,0.00006198734,0.7513086,0.1916425,0.0001508116,0.00000681881,0.05648661],"study_design_scores_gemma":[0.0002577605,0.0000543994,0.007081026,0.00002831263,0.0001200792,6.761205e-7,0.00003027072,0.9249651,0.06726858,0.00001894565,0.00003411444,0.0001407122],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1293528,0.00008772628,0.8701645,0.00003544391,0.00005452559,0.0001274687,0.00002358701,0.0001204488,0.00003355028],"genre_scores_gemma":[0.999245,0.00005309996,0.0005416357,0.000007703256,0.00002043552,0.00008510466,0.00002602227,0.00001626828,0.000004741303],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8698922,"threshold_uncertainty_score":0.5967301,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0105645173583603,"score_gpt":0.2148054573148311,"score_spread":0.2042409399564708,"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."}}