{"id":"W2784098778","doi":"","title":"Quantifying The Signal-To-Noise Ratio of Silicon- Embedded Sensors for Mechanomyography","year":2017,"lang":"en","type":"article","venue":"CMBES Proceedings","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Silicon; SIGNAL (programming language); Accelerometer; Noise (video); Signal-to-noise ratio (imaging); Acoustics; Computer science; Materials science; Electronic engineering; Engineering; Optoelectronics; Artificial intelligence; Physics; Telecommunications","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":[0.002058425,0.0005805962,0.000386221,0.0004178261,0.0001497657,0.0005214511,0.0004313473,0.0007307697,0.001177668],"category_scores_gemma":[0.005040954,0.0003025067,0.0002095735,0.0004106698,0.0005986794,0.0005044951,0.0004270702,0.0003040154,0.0002670832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000157814,"about_ca_system_score_gemma":0.0001584707,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001405115,"about_ca_topic_score_gemma":0.0003418291,"domain_scores_codex":[0.9987701,0.0004186212,0.0001018309,0.000216101,0.0004168647,0.00007655365],"domain_scores_gemma":[0.9970181,0.001937021,0.0002701253,0.000246793,0.0004483058,0.00007965782],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002485114,0.00004654727,0.0009443142,0.0000939637,0.0000175276,0.000038714,0.00003928085,0.0005879198,0.9910833,0.00007199564,0.00002635316,0.006801787],"study_design_scores_gemma":[0.00002821289,0.001891017,0.0214383,0.00002810065,0.00008413388,0.0003850499,0.0000887479,0.01064518,0.9643919,0.0002665774,0.0007244177,0.00002836764],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8552073,0.001019884,0.1416281,0.00009935024,0.00008493396,0.0001497972,0.0001597125,0.0002369704,0.001413975],"genre_scores_gemma":[0.910526,0.0005712096,0.08764659,0.0001205394,0.00002943285,0.0002420393,0.0001699228,0.00006574002,0.0006284752],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002058425,"threshold_uncertainty_score":0.01088613,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03175826460349992,"score_gpt":0.2677091956241068,"score_spread":0.2359509310206069,"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."}}