Physiological and metabolic responses as function of the mechanical load in resistance exercise
Bibliographic record
Abstract
The present study aimed to investigate the relationship between the mechanical load during resistance exercise and the elicited physiological responses. Ten resistance-trained healthy male subjects performed 1 set of resistance exercise each at 55%, 70%, and 85% of 1 repetition maximum for as many repetitions as possible and in 4 training modes: 4-1-4-1 (4 s concentric, 1 s isometric, 4 s eccentric, and 1 s isometric successive actions), 2-1-2-1, 1-1-1-1, and explosive (maximum velocity concentric). Mean concentric power and total concentric work were determined. Oxygen uptake (V̇O2) was measured during exercise and for 30 min post exercise. Total volume of consumed oxygen (O2 consumed) and excess post-exercise oxygen consumption (EPOC) were calculated. Maximum blood lactate concentration (LAmax) was also determined. V̇O2 exhibited a linear dependency on mean concentric power. Mean concentric power did not have a detectable effect on EPOC and LAmax. An augmentation of total concentric work resulted in significant linear increase of O2 consumed and EPOC. Total concentric work caused a significant increase in LAmax. In general, a higher mechanical load induced a larger physiological response. An increase in mean concentric power elicited higher aerobic energy turnover rates. However, a higher extent of total concentric work augments total energy cost covered by oxidative and (or) glycolytic pathways.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".