Performance Determinants in Trail-Running Races of Different Distances
Bibliographic record
Abstract
PURPOSE: While the physiological determinants of road running have been widely studied, there is a lack of research in trail-running racing performance. The aim of our study was to determine the physiological predictors of trail-running performance in races of different distances in similar terrain and weather conditions. METHODS: Seventy-five trail runners participating in one of the races of the Ultra-Trail du Mont-Blanc were recruited. Previous to the race, each runner was evaluated with (1) an incremental treadmill test to determine maximal oxygen uptake, ventilatory thresholds, cost of running, and substrate utilization; (2) a power-force-velocity profile on a cycle ergometer; (3) maximal voluntary contractions of the knee extensors and plantar flexors; and (4) anthropometric characteristics. Neuromuscular fatigue was evaluated after the races. Twenty-four runners finished a SHORT (<55 km), 16 finished a MEDIUM (101 km), and 14 finished a LONG (>145 km) race. Correlations and multiple linear regressions were used to find the determinants of performance in each race distance. RESULTS: Performance in SHORT was explained by maximal oxygen uptake and lipid utilization at 10 km/h (r2 = .825, P < .001). Performance in MEDIUM was determined by maximal oxygen uptake, maximal isometric strength, and body fat percentage (r2 = .917, P < .001). A linear model could not be applied in LONG, but performance was correlated to peak velocity during the incremental test. CONCLUSIONS: Performance in trail running is mainly predicted by aerobic capacity, while lipid utilization also influences performance in races <60 km and performance in approximately 100 km is influenced by muscle strength and body composition.
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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 | 0.000 | 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".