Predicting mountain ultra‐marathon performance using the critical velocity model
Bibliographic record
Abstract
PURPOSE Critical Velocity (CV) and D′ were evaluated as predictors of mountain ultra marathon finishing times in cool conditions. HYPOTHESIS It was hypothesized that CV and D′ would both predict mountain ultra‐marathon performance. METHODS Nine healthy males volunteered to participate in the study. Their average age was 37.9 ± 10.0 y, height 1.79 ± 0.05 m, body mass 76.6 ± 11.2 kg, BMI 23.8 ± 3.0 kg•m −2 . The SFU Office of Research Ethics approved the study and each volunteer gave a signed consent prior to participation. Each volunteer completed a 50‐km mountain ultra‐marathon and then each volunteer's CV and D′ was evaluated using a series of maximal‐effort timed 1200, 2400 and 3600 m running trials on the same day with 30 min rest before trials 2 and 3. A Latin square design was employed to determine the orders of the tests. Maximal aerobic capacity was also assessed using indirect calorimetry and a graded treadmill test. RESULTS The mean CV was 3.5 ± 0.5 m•s −1 , D′ was 197.2 ± 83.2 m and mean absolute and relative aerobic powers were 4.8 ± 0.8 L min −1 and 63.5 ± 6.7 mL•kg −1 •min −1 , respectively. Stepwise multiple linear regression identified CV as the sole significant predictor of the mountain ultra‐marathon performance time (r=−0.88, p<0.05). Correlation analysis of the independent variables with finishing time indicated that aerobic power was significantly and positively correlated with finishing times (r=−0.72, p=0.002). CONCLUSION These preliminary results suggest that CV explains both a significant fraction of the variance in mountain marathon finishing time and that CV obtained via running track tests is an appropriate method for predicting these finishing times. Support or Funding Information Supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) and the Canadian Foundation for Innovation (CFI)
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".