{"id":"W2095632822","doi":"10.1016/j.clinbiomech.2011.10.007","title":"Discriminating between maximal and feigned isokinetic knee musculature performance using waveform similarity measures","year":2011,"lang":"en","type":"article","venue":"Clinical Biomechanics","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Kingston General Hospital; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cutoff; Physical medicine and rehabilitation; Mathematics; Logistic regression; Concentric; Knee flexion; Physical therapy; Waveform; Eccentric; Moment (physics); Medicine; Similarity (geometry); Orthodontics; Statistics; Computer science; Artificial intelligence; Structural engineering; Engineering","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":[0.001480571,0.0004033146,0.0003848382,0.001514508,0.0001738222,0.0007065204,0.0003009631,0.000770917,0.001281274],"category_scores_gemma":[0.006633916,0.0001282025,0.000311912,0.0006992272,0.0002495495,0.0007361047,0.000525163,0.0002358599,0.0004532023],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009338259,"about_ca_system_score_gemma":0.0001781597,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004712231,"about_ca_topic_score_gemma":0.0006337023,"domain_scores_codex":[0.9993883,0.0001831208,0.0001064818,0.0001065655,0.0001582495,0.00005724146],"domain_scores_gemma":[0.9973042,0.001626124,0.000317804,0.0001290036,0.0003740309,0.000248828],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.005728193,0.0006413118,0.4942282,0.0003388241,0.0005777609,0.000375725,0.0006820059,0.005035971,0.1390834,0.0005410956,0.0007294873,0.352038],"study_design_scores_gemma":[0.0001356088,0.002535513,0.9245456,0.00004448265,0.0002123865,0.001921789,0.000839292,0.0554133,0.01252676,0.001155907,0.000606095,0.00006318986],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.982958,0.0001347343,0.01584755,0.0000385659,0.00002316675,0.00003882388,0.0001224916,0.00006543416,0.0007711212],"genre_scores_gemma":[0.9957805,0.00004908828,0.00374397,0.000012688,0.00001677713,0.00001980771,0.0001696057,0.00001059869,0.0001968977],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001514508,"threshold_uncertainty_score":0.007830083,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1109090741423039,"score_gpt":0.2835144589236772,"score_spread":0.1726053847813733,"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."}}