{"id":"W2551700842","doi":"10.1152/jn.00435.2016","title":"On identifying kinematic and muscle synergies: a comparison of matrix factorization methods using experimental data from the healthy population","year":2016,"lang":"en","type":"article","venue":"Journal of Neurophysiology","topic":"Motor Control and Adaptation","field":"Neuroscience","cited_by":79,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"University of British Columbia Graduate School; Vlaamse regering; University of British Columbia","keywords":"Non-negative matrix factorization; Principal component analysis; Matrix decomposition; Kinematics; Computer science; Independent component analysis; Factorization; Population; Curse of dimensionality; Artificial intelligence; Set (abstract data type); Pattern recognition (psychology); Machine learning; Algorithm","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":[0.0001040637,0.0000782708,0.0002445962,0.00006197193,0.00009387006,0.0000179238,0.0002322905,0.00002883522,0.00001481535],"category_scores_gemma":[0.0009146322,0.00004224978,0.00003381123,0.00007839845,0.00004945288,0.0002823295,0.00007709117,0.00008550799,0.000001006899],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001980329,"about_ca_system_score_gemma":0.00001618355,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004796319,"about_ca_topic_score_gemma":8.19503e-7,"domain_scores_codex":[0.9984487,0.000673127,0.0004605319,0.0001662698,0.0001627941,0.00008855396],"domain_scores_gemma":[0.9975039,0.001491068,0.0007296492,0.0002124052,0.00003142892,0.00003159258],"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":[0.0001513572,0.00005016996,0.00006915311,0.00000764157,0.000004000331,0.000001570167,0.0001937761,0.001040669,0.996362,0.0001843695,0.000003153094,0.001932153],"study_design_scores_gemma":[0.004160568,0.003070441,0.5231356,0.0005000658,0.0001480741,0.00005443097,0.0008015317,0.3375148,0.1231069,0.006995557,0.0001636354,0.0003483942],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9670034,0.00006676451,0.03214054,0.0001900105,0.000492323,0.00008713565,0.00001399209,0.000004560641,0.000001253912],"genre_scores_gemma":[0.9976088,0.00003193872,0.002088467,0.0001385322,0.0001196024,4.992367e-7,0.000001635845,0.000008504893,0.000001952858],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8732551,"threshold_uncertainty_score":0.1722896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2105184765922734,"score_gpt":0.4470262164346241,"score_spread":0.2365077398423507,"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."}}