Methods to quantify intermittent exercises
Bibliographic record
Abstract
The purpose of this study was to quantify intermittent training sessions using different types of exercise. Strength, sprint, and endurance sessions were performed until exhaustion. These sessions were quantified by the product of duration and heart rate (HR) (i.e., training impulse (TRIMP) and HR-zone methods), by the product of duration and rate of perceived exertion (RPE-based method), and a new method (work endurance recovery (WER)). The WER method aims to determine the level of exercise-induced physiological stress using the ratio of cumulated work - endurance limit, which is associated with the naparian logarithm of the ratio of work-recovery. Each session's effects were assessed using blood lactate, delayed onset muscle soreness (DOMS), RPE, and HR. Because sessions were performed until exhaustion, it was assumed that each session would have a similar training load (TL) and there would be low interindividual variability. Each method was used to compare each of the TL quantifications. The endurance session induced the higher HR response (p < 0.001), the sprint session the higher blood lactate increase (p < 0.001), and the strength session the higher DOMS when compared with sprint (p = 0.007). TLs were similar after WER calculations, whereas the HR- and RPE-based methods showed differences between endurance and sprint (p < 0.001), and between endurance and strength TL (p < 0.001 and p < 0.01, respectively). The TLs from WER were correlated to those of the HR-based methods of endurance exercise, for which HR was known to accurately reflect the exercise-induced physiological stress (r = 0.63 and r = 0.64, p < 0.05). In addition, the TL from WER presented low interindividual variability, yet a marked variability was observed in the TLs of HR- and RPE-based methods. As opposed to the latter two methods, WER can quantify varied intermittent exercises and makes it possible to compare the athletes' TL. Furthermore, WER can also assist in comparing athlete responses to training programs.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.009 |
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".