Sweat rate, salt loss and fluid intake during an intense on‐ice practice in elite level junior hockey players
Bibliographic record
Abstract
Previous research suggests that losing ~2% body mass (BM) impairs athletic performance. This study evaluated the hydration status and sweat responses of 44 junior hockey players during a 1 hr on‐ice practice. Players (x ± SE, 18.4 ± 0.1 yr, 184.8 ± 0.9 cm, 89.9 ± 1.1 kg) were studied in groups of 10–12 during 4 practices on one day in an arena (13.9°C, 66% RH). Hydration status prior to practice was estimated by measuring urine specific gravity (USG, mild dehydration > 1.020). Sweat rate (SR) was calculated from BM changes and fluid intake during practice. Players drank a sports drink prior to practice and water during practice. Sweat [Na] was analyzed in forehead sweat patch samples. On average, players arrived reasonably well hydrated (USG = 1.020 ± 0.001), but 24 players were above 1.02. The average SR during practice was 1.76 ± 0.08 l/hr. While the players replaced only 60% of their fluid loss (1.03 ± 0.08 l), average BM loss was only 0.8 ± 0.1 %. Sweat [Na] was 54.2 ± 2.4 mM, resulting in a loss of 2.26 ± 0.17 g of Na. Goalies (n = 4) sweat at a much higher rate (2.87 ± 0.17 l/hr) than other players (1.65 ± 0.07 l/hr). While goalies drank more during practice (1.82 ± 0.55 l), they lost a greater percent of their BM (1.1 ± 0.4%). In summary, elite junior hockey players exhibited high sweat rates and sweat [Na] during intense practices. The majority of the elite players drank enough during practice to prevent dehydration. Study supported by GSSI Canada.
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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 | 0.001 | 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".