{"id":"W2124395632","doi":"10.1109/iembs.2007.4353441","title":"Strict 2-Surface Proximal Classification of Knee-joint Vibroarthrographic Signals","year":2007,"lang":"en","type":"article","venue":"Conference proceedings","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Artificial intelligence; Pattern recognition (psychology); Classifier (UML); Nonlinear system; Gaussian; Mathematics; Robustness (evolution); Computer science; Kernel (algebra); Combinatorics; Biology; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001300548,0.0004207619,0.0006329553,0.0007477906,0.0001767672,0.0008210646,0.000332237,0.0005336445,0.0005902568],"category_scores_gemma":[0.004887265,0.000142191,0.0003998999,0.0003823637,0.0003532476,0.0005270811,0.0005806203,0.0003025817,0.0004592553],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001404346,"about_ca_system_score_gemma":0.0002506093,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006674595,"about_ca_topic_score_gemma":0.0009269901,"domain_scores_codex":[0.9992212,0.0001948572,0.00007134766,0.0001408368,0.0002887082,0.00008294109],"domain_scores_gemma":[0.9982889,0.0007049789,0.000193667,0.000187367,0.0005654157,0.00005968603],"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":[0.001041782,0.0003471107,0.1147159,0.0002326342,0.0001625128,0.0002940983,0.0002630732,0.1017252,0.1800952,0.001228786,0.0008690585,0.5990246],"study_design_scores_gemma":[0.00003145136,0.0007165886,0.1001307,0.00001694601,0.00004634741,0.0003826472,0.0001025238,0.867426,0.0292883,0.001225056,0.0005958121,0.00003754226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7328311,0.00008010248,0.2648036,0.00005286953,0.00001674139,0.00009278582,0.00008436216,0.000431584,0.001606869],"genre_scores_gemma":[0.9622127,0.00002908138,0.03672891,0.00001693058,0.00000905364,0.00004390625,0.0002130381,0.00001810463,0.0007282266],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001300548,"threshold_uncertainty_score":0.006878018,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0313479244533162,"score_gpt":0.2367364208779033,"score_spread":0.2053884964245871,"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."}}