{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001901182,0.0001684723,0.0002218629,0.0001585791,0.0005174712,0.0001301829,0.0003232878,0.00005419287,0.000009623985],"category_scores_gemma":[0.0001237841,0.0001323594,0.0001545559,0.0001897525,0.00007891576,0.0002324775,0.00004057429,0.00009277072,0.000001344669],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001148458,"about_ca_system_score_gemma":0.000006639984,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007838774,"about_ca_topic_score_gemma":0.000005030541,"domain_scores_codex":[0.9991828,0.000002454264,0.0002215755,0.0001808662,0.0001374617,0.0002748526],"domain_scores_gemma":[0.9993658,0.00006665911,0.0001074486,0.0001854545,0.0002185552,0.00005609676],"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":[0.0000891913,0.00004812127,0.01093828,0.0006875477,0.0005109371,3.398003e-7,0.006393149,0.0003286518,0.9139093,0.0184662,0.01831522,0.03031302],"study_design_scores_gemma":[0.0009890747,0.0002585268,0.08285862,0.0001896431,0.0001260335,0.000003530357,0.003895423,0.02120585,0.8757941,0.001465573,0.01253838,0.0006753034],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9926401,0.0001110095,0.00153119,0.000543861,0.0001827555,0.0006612964,0.000008372152,0.0001853385,0.004136119],"genre_scores_gemma":[0.9988906,0.00004435461,0.0006364618,0.00006773602,0.000105332,0.0001793878,0.000001324426,0.00003074382,0.00004409361],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07192034,"threshold_uncertainty_score":0.5397459,"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."}}