METABOLIC AND FUNCTIONAL RESPONSES PLAYING TENNIS ON DIFFERENT SURFACES
Bibliographic record
Abstract
The purpose of this study was to compare various metabolic and functional responses while playing tennis on clay and hard courts. Twelve 90-minute matches were played (6 on clay courts and 6 on hard courts) by 4 nationally ranked players. During the on-court tests, oxygen uptake (VO2) and heart rate (HR) were measured using portable systems. Capillary blood lactate concentration (LA) was measured every 10 minutes. Additionally, distance ran, playing time, resting time, and exercise to rest ratio were monitored by time-motion analysis. The statistical analysis showed that playing time was higher on clay courts than on hard courts (p < 0.05), and resting time on clay courts and hard courts was not statistically different (p > 0.05). The exercise to rest ratio was affected by the interaction between playing time and resting time, showing a longer recovery time per unit of exercise on hard courts than on clay courts (p < 0.05). Distance ran, mean HR, and mean LA were significantly higher on clay courts than on hard courts (p < 0.05). There was less fluctuation of the VO2 response on clay courts than on hard courts. Therefore, it is suggested that conditioning programs should be adjusted according to the playing surface to account for the longer playing time, greater exercise to rest ratio, increased HR and LA, and a more steady pattern of VO2 seen on clay courts.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".