{"id":"W2773177717","doi":"10.1098/rsif.2017.0734","title":"Navigating features: a topologically informed chart of electromyographic features space","year":2017,"lang":"en","type":"article","venue":"Journal of The Royal Society Interface","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":74,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Fondation de la recherche en santé du Nouveau-Brunswick; Compagnia di San Paolo","keywords":"Pattern recognition (psychology); Artificial intelligence; Computer science; Feature vector; Robustness (evolution); Feature (linguistics); Generalizability theory; Feature selection; ENCODE; Redundancy (engineering); Feature extraction; Chart; Machine learning; Mathematics; Biology; Statistics","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":[0.001426388,0.0008453851,0.0006603572,0.003691041,0.0007376837,0.003165948,0.0009712544,0.0009062574,0.00287903],"category_scores_gemma":[0.00983786,0.000279562,0.0007826381,0.002311806,0.00150407,0.002357425,0.001403667,0.001246558,0.0006871721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006881744,"about_ca_system_score_gemma":0.0009694854,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00294811,"about_ca_topic_score_gemma":0.001783783,"domain_scores_codex":[0.9991488,0.0002132418,0.00008419013,0.0002227509,0.0002506466,0.00008030912],"domain_scores_gemma":[0.9963774,0.00161613,0.0004440068,0.0004627342,0.0008603028,0.0002394634],"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":[0.0009963125,0.0001890614,0.01438816,0.0007503157,0.0001033943,0.0009571877,0.001808159,0.2271821,0.04238559,0.1479607,0.01335014,0.5499289],"study_design_scores_gemma":[0.0000638154,0.000506575,0.007894807,0.0001967731,0.00006374181,0.000501013,0.0004875021,0.779267,0.01571384,0.1716779,0.02344699,0.0001800433],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05118718,0.0003674087,0.9414876,0.0004066622,0.00006738235,0.000132291,0.001647782,0.00233665,0.002367048],"genre_scores_gemma":[0.3910257,0.0005501802,0.6033223,0.0000978902,0.00008459089,0.000301109,0.002896242,0.0003963094,0.001325644],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003691041,"threshold_uncertainty_score":0.009631276,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008847167024761482,"score_gpt":0.2641078142755878,"score_spread":0.2552606472508264,"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."}}