{"id":"W1878432473","doi":"10.1109/iembs.1993.978532","title":"Classification of muscle contraction levels with an artificial neural network","year":2005,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Artificial neural network; Contraction (grammar); Artificial muscle; Artificial intelligence; Muscle contraction; Anatomy; Medicine; Internal medicine","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":[0.0008074324,0.0005279862,0.0004590953,0.001204678,0.0002841899,0.0006047445,0.0003486189,0.0008078233,0.001938674],"category_scores_gemma":[0.00209329,0.0002428498,0.0006041909,0.0008692469,0.0002968139,0.0004096871,0.0003827598,0.0005161416,0.0005000076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003303549,"about_ca_system_score_gemma":0.0003033737,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002255278,"about_ca_topic_score_gemma":0.002238207,"domain_scores_codex":[0.9997377,0.00005071054,0.00003077859,0.00007432239,0.00006707587,0.00003942153],"domain_scores_gemma":[0.99915,0.000435261,0.00006182211,0.00005713696,0.0002464251,0.00004932302],"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":[0.002463257,0.0003635972,0.03489823,0.0003072983,0.000214024,0.0002277509,0.0001756281,0.06425579,0.0942625,0.001072184,0.002525652,0.7992342],"study_design_scores_gemma":[0.00003803105,0.0003875317,0.03730383,0.00004373533,0.0001078649,0.0001779841,0.00008373563,0.9458272,0.01410899,0.001073149,0.0008112404,0.00003676721],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4570137,0.000980361,0.5338039,0.0003673459,0.0003251387,0.0003323765,0.0009004114,0.001444291,0.004832375],"genre_scores_gemma":[0.8664656,0.0003687678,0.1293372,0.00008319115,0.00007391648,0.0001885196,0.0004799647,0.00005285763,0.002949997],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002255278,"threshold_uncertainty_score":0.006485522,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05151552931815227,"score_gpt":0.2799814075033577,"score_spread":0.2284658781852054,"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."}}