RELATIONSHIPS BETWEEN HEART RATE AND PHYSIOLOGICAL PARAMETERS OF PERFORMANCE IN TOP-LEVEL WATER POLO PLAYERS
Bibliographic record
Abstract
The aim of this study was to measure the heart rate (HR) response of eight elite water polo players during the four 7-min quarters of the game and to check for relationships with the physiological parameters of performance ([Formula: see text]O2max, Th1vent, Th2vent). Each athlete performed a [Formula: see text]O2max treadmill test and played a water polo game wearing a heart rate monitor. The game fatigue index was calculated as the ratio of the fourth-quarter HR to the first-quarter HR: HR4/HR1. The results showed a slight decrease in fourth-quarter HR compared with the first quarter, with the mean four-quarter HR equal to 79.9±4.2% of HRmax. Stepwise multiple regression analysis showed [Formula: see text]O2max to be the main explanatory factor of game intensity, i.e. game HR expressed in %HRreserve (R=0.88, P<0.01). We observed that higher aerobic capacity resulted in higher game intensity. We also observed a decrease in the playing intensity in the fourth quarter compared with the first, likely due to very high game involvement. We concluded that high aerobic capacity seems necessary to ensure high game intensity in water polo. This suggests that coaches should encourage their athletes to reach a minimum level of [Formula: see text]O2max and that HR monitoring could be of great interest in the control of water polo training sessions.
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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 | 0.000 | 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".