Implications of probabilistic activation on estimating the number of motor units in a muscle
Bibliographic record
Abstract
Methods aimed at estimating the numbers of motor units [MUs] in muscles have gained increased prominence over the course of the last several years. Currently, all of the these methods are based on a sampling approach in which the properties of only a handful of MUs are examined. It is thus critical that 1) the MU sample be representative of the entire MU population and 2) the signals assumed to represent individual motor unit action potentials [MUAPs] are indeed valid MUAPs. The authors are particularly interested in the latter. Specifically, in the majority of methods, the MU sample is obtained by stimulating the nerve with carefully graded stimulus pulses. In doing so, it is assumed that each successive increase in the recorded compound muscle potential corresponds to the successive activation of an individual MU. This assumption is valid only in those cases in which each MU can be activated independently from the remaining non-active MUs. In practice, only the first few MUs can be activated in this manner. Successive MUs then tend to be activated in groups. Hence, the resultant muscle potentials can represent various combinations of active and/or inactive MUs, and thus successive incremental increases in the observed compound muscle potential will not necessarily correspond to the additional activation of new MUs. As a result, the average MUAP size (i.e. amplitude or area) calculated using these methods tends to be under-estimated, which in turn leads to the over-estimation of the number of MUs in the muscle. Here the authors examine the magnitude of this error, which increases with the number of MUs undergoing probabilistic activation, and investigate possible means of alleviating it.>
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.148 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".