10 km performance prediction by metabolic and mechanical variables: influence of performance level and post-submaximal running jump potentiation
Bibliographic record
Abstract
We aimed to develop models to explain performance and pacing during a 10-km running trial.Well-trained runners (n = 27, VO2max = 62.3 ± 4.5 mL·kg−1·min−1) divided into High (HPG, T10km = 33.9 ± 1.2 min, n = 9) and Low (LPG, T10km = 37.9 ± 1.2 min, n = 18) performers completed, in different days, the half squat and loaded squat jump (LSJ) exercises (1st day), an incremental test and a submaximal running bout to induce jump potentiation (2nd day), and a 10-km time trial (3rd day). Pacing was significantly different between performance groups (p < 0.05). The inclusion of mechanical and metabolic variables increased the explained variance in performance (LPG, r2adj = 0.87, p < 0.001; HPG, r2adj = 0.99 p < 0.01). Analysis between potentiation and non-potentiation groups revealed significant differences for the speed in the last 400 m (p = 0.02), and in the final RPE (p = 0.03). Performance and pacing can be explained by combining metabolic and mechanical variables and should be controlled by performance level. The relationship between jump potentiation and speed during the last 400 m may suggest that post-activation performance enhancement could be involved in pacing regulation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".