Efecto del entrenamiento de intervalo en jugadores mexicanos juveniles de fútbol soccer
Bibliographic record
Abstract
In order to achieve a good performance in a soccer match, soccer players use aerobic capacity. It has been suggested that such capacity can be trained using Interval Training. The purpose of this research was to compare the performance of juvenile soccer players before and after they practiced interval training. To prove it we assessed the physical performance of 38 juvenile soccer players using Université Montreal track test and Hoff test. Their performance was evaluated before and after the interval training measuring maximal aerobic speed in kilometers per hour, indirect maximum oxygen volume (VO2max) in mm/kg/min, traveled distance in meters and basal heart rate in beats per minute. The juvenile soccer players performance increased significantly at all the variables measured after the interval training (maximal aerobic speed 15.76 ± 0.12 [mean ± 1 standard error] before and 17.08 ± 0.13 after; VO2max 55.17 ± 0.43 before vs 59.78 ± 0.45 after; traveled distance 1461.37 ± 11.20 before vs 1593.79 ± 13.21 after; basal heart rate 71.58 ± 1.14 before vs 67.37 ± 1.07 after; in all the cases p <0.0001). We concluded that the interval training was helpful to improve physical performance in the juvenile soccer players tested.
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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 | 0.002 |
| 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) | 0.002 | 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".