Comparing the lactate and EMG thresholds of recreational cyclists during incremental pedaling exercise
Bibliographic record
Abstract
The purpose of this study was to determine the validity of using the electromyography (EMG) signal as a noninvasive method of estimating the lactate threshold (LT) power output in recreational cyclists. Using an electromagnetic bicycle ergometer and constant pedaling cadence of 80 rpm, 24 recreational cyclists performed an incremental exercise protocol that consisted of stepwise increases in power output of 25 W every 3 min until exhaustion. The EMG signal was recorded from the right vastus lateralis (VL) and right rectus femoris (RF) throughout the test. Blood samples were taken from the fingertip every 3 min. The LT was determined by examining the relation between the lactate concentration and the power output using a log-log transformation model. The root mean square (RMS) value from the EMG signal was calculated for every 1-second non-superimposing window. Sets of pairs of straight regression lines were plotted and the corresponding determination coefficients (R(2)) were calculated. The intersection point of the pair of lines with the highest R(2) product was chosen to represent the EMG threshold (EMGT). The results showed that the correlation coefficients (r) between EMGT and LT were significant (p < 0.01) and high for the VL (r = 0.826) and RF (r = 0.872). The RF and VL muscles showed similar behavior during the maximal incremental test and the EMGT and LT power output were equivalent for both muscles. The validity of using EMG to estimate the LT power output in recreational cyclists was confirmed.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".