Influencing Factors of Pacing Variations and Performance in a 44-Kilometer Mountain Trail Race
Bibliographic record
Abstract
Objective: This study aimed to analyse the changes in the biomechanical and psychophysiological responses, and the body mass of trailers in a small-distance (44 km and 1520 m elevation gain) trail competition performed in tropical conditions. Methods: Ten trained trailers (8 men, 2 females; age: 42.0 ± 5 years, body mass: 65.57 ± 5.4 kg, height: 174.9 ± 5.9 cm BMI: 21.71 ± 2.1, Maximal Aerobic Speed (MAS): 16.6 ± 2.1 km·h-1) volunteered to take part in the competition, comprising eight laps of 5.5 km. At the end of each lap, the trailers had to stop for 10 min to perform tests measuring 1) the maximal horizontal force (F0), theoretical maximal running velocity (V0) and maximal power output (PO) during a 30-m sprint; 2) the vertical oscillations and maximal relative force during a 30-s treadmill submaximal run; 3) the perceived exertion and pleasure; and 4) body mass. The pacing, stride variations and heart rate were continuously recorded during the race. Results: The variations of PO (W·kg-1) during the 30-m sprint and perceived pleasure were significant (p = 0.003 and p = 0.02, respectively) influencing factors of pacing. A significant decrease (p the first and last laps. Fraction of MAS and MAS were significantly (p = 0.004 and p = 0.04, respectively) related to the trail performance. Conclusions: Training programmes could be proposed that include the increase of MAS, fraction of MAS and lower limb PO. During the competition, it could be interesting to plan a drinking programme to avoid potential thermoregulatory impairment, as well as psychological strategies to increase pleasure.
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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) | 0.001 | 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".