High‐level performances following low altitude training and tapering in warm environments in elite racewalkers
Bibliographic record
Abstract
Abstract Current guidelines for prolonged altitude exposure suggest altitude levels ranging from 2000 to 2500 m to optimize an increase in total hemoglobin mass (Hbmass). However, natural low altitude locations (<2000 m) remain popular, highlighting the interest to investigate any possible benefit of low altitude camps for endurance athletes. Ten elite racewalkers (4 women and 6 men) underwent a 4‐week “live high‐train high” (LHTH) camp at an altitude of 1720 m (P I O 2 = 121 mmHg; 20.1°C; 67% relative humidity [RH]), followed by a 3‐week tapering phase (20 m; P I O 2 = 150 mmHg; 28.3°C; 53% RH) in preparation for the World Athletics Championships (WC). Venous blood samples were withdrawn weekly during the entire observation period. In addition, blood volumes were determined weekly by carbon monoxide rebreathing during altitude exposure and 2 weeks after return to sea level. High‐level performances were achieved at the WC (five placings among the Top 10 WC races and three all‐time career personal bests). A slight but significant increase in absolute (+1.7%, p = 0.03) and relative Hbmass (+2.3%, p = 0.02) was observed after 4‐week LHTH. In addition, as usually observed during LHTH protocols, weekly training distance (+28%, p = 0.02) and duration (+30%, p = 0.04) significantly increased during altitude compared to the pre‐LHTH period. Therefore, although direct causation cannot be inferred, these results suggest that the combination of increased training load at low altitudes with a subsequent tapering period in a warm environment is a suitable competition‐preparation strategy for elite endurance athletes.
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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 | 0.000 |
| 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) | 0.001 | 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".