The Energetics during the World's Most Challenging Mountain Ultra-Marathon—A Case Study at the Tor des Geants®
Bibliographic record
Abstract
Purpose: To provide insights into the energy requirements as well as the physiological adaptations of an experienced 50-yr-old ultra-marathon male athlete during the world’s most challenging mountain ultra-marathon (MUM). Methods: The international race supporting the study was the Tor des Geants®, characterized by 330 km with +24,000 m D+ to be covered within 150 h. Before the MUM, we assessed the peak oxygen uptake ( O2peak) by means of an incremental graded running test. During the MUM we monitored six ascents (once per race day) with a portable gas analyzer, a GPS and a finger pulse oximeter. We then calculated the net metabolic cost per unit of distance (C), the vertical metabolic cost (Cvert) and the mechanical efficiency of locomotion (Effmech) throughout the six uphills monitored. We further monitored the distance covered, speed, altimetry and D+ from the GPS data as well as the pulse oxygen saturation with the finger pulse oximeter. Results: Subject’s O2peak was 48.1 mL•kg-1•min-1. Throughout the six uphills investigated the mean exercise intensity was 59.7±6.3% O2peak and 68.0±8.7% HRpeak. C, Cvert and Effmech were 11.4±1.9 J•kg-1•m-1, 57.9±15.2 J•kg-1•mvert -1 and 17.7±4.8%, respectively. The exercise intensity, as well as C, Cvert, and Effmech did not consistently increase during the MUM. Conclusions: For the first time, we described the feasibility of assessing the energy requirements as well as the physiological adaptations of a MUM in ecologically valid environment settings. The present case study shows that, despite the distance performed during the MUM, our participant did not experience a metabolic fatigue state. This is likely due to improvements in locomotor efficiency as the race progressed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".