{"id":"W3215289130","doi":"","title":"CLEANEMG: Quantifying Power Line Interference in Surface EMG Signals","year":2011,"lang":"en","type":"article","venue":"CMBES Proceedings","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; University of New Brunswick","funders":"","keywords":"Interference (communication); Noise (video); SIGNAL (programming language); Power (physics); Interpolation (computer graphics); Line (geometry); Acoustics; Noise floor; Noise power; Signal-to-noise ratio (imaging); Point (geometry); Electronic engineering; Computer science; Noise measurement; Engineering; Noise reduction; Telecommunications; Mathematics; Artificial intelligence; Physics","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":[0.001460634,0.0009410137,0.0007956136,0.002083038,0.0002812299,0.0009372681,0.0009758143,0.001123178,0.003018312],"category_scores_gemma":[0.002971397,0.0003204467,0.0004236628,0.001083338,0.0004897367,0.0007878034,0.0006696404,0.0004904587,0.00119854],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002760384,"about_ca_system_score_gemma":0.0002668707,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007694971,"about_ca_topic_score_gemma":0.001626995,"domain_scores_codex":[0.998483,0.000243491,0.00004023903,0.0001978097,0.000975547,0.00005990231],"domain_scores_gemma":[0.9990798,0.0004126113,0.0001278236,0.0001291764,0.0002109842,0.00003961395],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005496967,0.0002402606,0.005686047,0.0006856645,0.0001417838,0.0002328826,0.0001953095,0.008169457,0.3702729,0.002036931,0.003021124,0.608768],"study_design_scores_gemma":[0.0002281483,0.002516218,0.08462558,0.0001333253,0.000220082,0.00392264,0.0001643239,0.366699,0.5135843,0.003836345,0.02371487,0.0003552109],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03241526,0.0004935418,0.9612643,0.00008476187,0.00007629387,0.0002380516,0.0003949993,0.003176146,0.001856689],"genre_scores_gemma":[0.1804495,0.0003989857,0.8146745,0.0001123374,0.00005699299,0.0003096551,0.0005941146,0.0002968046,0.003107104],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003018312,"threshold_uncertainty_score":0.01009727,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05572958912138658,"score_gpt":0.2514776348994459,"score_spread":0.1957480457780593,"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."}}