Commentaries on Viewpoint: Resistance training and exercise tolerance during high-intensity exercise: moving beyond just running economy and muscle strength
Bibliographic record
Abstract
EDITOR: A consistent increase in endurance performance has often been observed after resistance training (RT) (4).Based on the critical power (CP) concept, Denadai and Greco (1) recently proposed an interesting model to explain this RT-induced improvement in endurance performance.According to these authors, the gains (35-60%) in the curvature constant of the power-duration hyperbola (W=) could explain the performance improvements during constant-workload exercises performed above the CP after a RT program.However, it is important to highlight that during most athletic events, the intensity of the exercise is not previously fixed, but self-selected by the athletes.The intensity distribution during middle-and long-distance running races has often been characterized by a U-shaped pacing profile, with start and finish intensities being higher than in the middle part of the race (5).This U-shaped pacing makes the W= use more complex, because athletes might switch from one exercise intensity domain to another throughout the race (3).This could indicate that the increase in W= with RT might be more relevant for some specific parts of the race, in which athletes perform at intensities above the CP, such as during the fast start and the final sprint.This suggestion is in agreement with previous findings showing that RT can counteract fatigue during the last part of a running race (2).Therefore, further research in this exciting area is necessary to elucidate the influence of RT on W= and its possible relationship with changes in specific parts of self-paced, real races.
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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 | 0.008 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.046 | 0.043 |
| Insufficient payload (model declined to judge) | 0.015 | 0.011 |
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".