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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".