MétaCan
Menu
Back to cohort
Record W2400202014 · doi:10.1123/mcj.5.1.61

A New Method for Experimental Simulation of EMG Using Multi-Channel Independent Stimulation of Small Groups of Motor Units

2001· article· en· W2400202014 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMotor Control · 2001
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsSIGNAL (programming language)ElectromyographyComputer scienceStimulationSpeech recognitionMathematicsSimulationPsychologyNeuroscience

Abstract

fetched live from OpenAlex

The experimental simulation method was based upon the separate activation of up to 10 small groups of motor units (MU) in an acute nerve-muscle preparation. The investigator was able to precisely control and systematically alter the features of MU pool activation strategies. No implicit assumptions were made regarding MU properties. The purpose of this study was to evaluate the validity of this method. Three criteria were formulated and found to be satisfied: First, in the time domain, visual and audio displays of simulated EMG were indistinguishable from physiological EMG. Secondly, in the frequency domain, power spectra of simulated EMG revealed the typical features of EMG recorded during voluntary activation in the cat. Thirdly, the well-known monotonic relationship between EMG magnitude and force was readily reproduced, although strictly linear relations were not found. In addition, the relationship between the pool's ensemble activation rate and EMG magnitude showed distinct gain compression, mostly attributable to signal cancellation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.607
Threshold uncertainty score0.548

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.047
GPT teacher head0.297
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it