{"id":"W2198521890","doi":"10.1016/j.jelekin.2015.12.001","title":"An approach for improving repeatability and reliability of non-negative matrix factorization for muscle synergy analysis","year":2015,"lang":"en","type":"article","venue":"Journal of Electromyography and Kinesiology","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":60,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"University of Waterloo; Canadian Institutes of Health Research; Sharif University of Technology; Natural Sciences and Engineering Research Council of Canada; Amirkabir University of Technology; Canada Foundation for Innovation","keywords":"Repeatability; Non-negative matrix factorization; Matrix decomposition; Reliability (semiconductor); Pattern recognition (psychology); Computer science; Artificial intelligence; Matching (statistics); Rank (graph theory); Rank correlation; Mathematics; Statistics; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006840446,0.0001558393,0.0005418676,0.0005305064,0.00007184542,0.00001415516,0.00008838248,0.000115106,6.535506e-7],"category_scores_gemma":[0.000260835,0.0001272886,0.0002213739,0.000684154,0.0001060257,0.0002390544,0.000009753709,0.0001147253,1.510452e-9],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002209509,"about_ca_system_score_gemma":0.00002748059,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000232297,"about_ca_topic_score_gemma":0.000006306023,"domain_scores_codex":[0.9989162,0.00006582405,0.0004996125,0.0002059569,0.00009250133,0.0002199076],"domain_scores_gemma":[0.9986289,0.0002680713,0.0002783723,0.0001536318,0.0005598852,0.0001111944],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.003482389,0.001034471,0.3588709,0.002384001,0.004644436,0.000001279771,0.004078139,0.02943047,0.5120092,0.001749748,0.0004190153,0.08189593],"study_design_scores_gemma":[0.00281644,0.005257006,0.8980591,0.00001196992,0.001309698,0.000008768323,0.0009044862,0.06772679,0.01238761,0.01093635,0.0001576697,0.000424085],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.652441,0.0003275455,0.3469241,0.00002051389,0.000048018,0.0001915655,0.00001465808,0.00001468055,0.00001786992],"genre_scores_gemma":[0.9736339,0.0001477011,0.02607093,0.0000108996,0.00007457432,0.00002991728,0.00001922364,0.00001186505,9.609776e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5391882,"threshold_uncertainty_score":0.5190676,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01065653407541159,"score_gpt":0.2523226931544144,"score_spread":0.2416661590790028,"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."}}