Performance prediction of endurance runners through laboratory and track tests
Bibliographic record
Abstract
The objectives of this study were: 1) determine and compare physiological indexes from laboratory and track tests (Université de Montréal Track Test - UMTT) in endurance runners; 2) analyze the predictive capacity of VO2max, vVO2max and AT with the running performance at 1,500 m, 5,000 m and 10,000 m time trials; 3) analyze the effects of running distance on the relationship between the physiological indexes with aerobic performance. The study included 10 moderately trained endurance runners who performed the following series of tests on different days: 10,000 m, 5,000 m, and 1,500 m time trials on a 400 m track; two maximal incremental tests (laboratory and track) to determine the VO2max, vVO2max, and AT. There were no significant differences between VO2max, vVO2max and AT determined in both protocols. The multiple regression analysis revealed that vVO2max was the only index from laboratory associated with running performance at 1,500 and 5,000 m (62 and 35%, respectively). In addition, vVO2max from UMTT explained the running performance for the same previous distance (78 and 66%, respectively). On the other hand, the AT determined in both incremental tests explained 38 and 52% of performance at 10,000 m time trial, respectively. Thus, the prediction of endurance performance of long distance runners using VO2max, vVO2max and AT determined in the laboratory and UMTT tests depends on the running distance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".