Insights into anaerobic power reserve on relationships with exercise tolerance, work above critical power, and accumulated oxygen deficit in endurance-trained male cyclists: a pilot study
Bibliographic record
Abstract
The anaerobic power reserve (APR) model seeks to account for the heterogeneity in athletes’ anaerobic characteristics. However, its relationship with exercise tolerance across various durations and with anaerobic markers remains unclear. Therefore, we investigate the relationship between APR, exercise tolerance, work above critical power (W′), and maximal accumulated oxygen deficit (MAOD) in male cyclists. We further analyzed these relationships replacing maximal aerobic power (MAP) with critical power (CP) as the lower boundary of the power reserve, defining a so-called maximal power reserve (MPR). After preliminary tests, 19 endurance-trained male cyclists performed five trials to exhaustion (Tlim) at 130%, 115%, 100%, 85%, and 80% of MAP and Wingate test. APR and MPR correlated with all Tlims ( r > 0.511, p < 0.03), except at 80% MAP. After fixing CP or MAP, only correlations with supramaximal Tlims remained significant ( r > 0.703, p < 0.002). When peak power output (PPO) was fixed, only MPR correlated with Tlims at 130% and 115% MAP ( r > 0.508, p = 0.037). Both APR and MPR were associated with MAOD and W′ ( r = 0.480–0.542, p ≤ 0.045), but only MPR remained significantly related to MAOD after adjusting for lower boundary ( r = 0.488, p = 0.040). Our findings showed that in endurance-trained male cyclists, both power reserves relate to exercise tolerance, however their influence decreases for longer efforts. MPR exhibited a stronger link to anaerobic capacity compared to APR. The association between exercise tolerance and APR/MPR appears largely driven by PPO, rather than the choice of lower boundary.
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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 | 0.001 |
| 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 | 0.000 | 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".