Fluid Balance of Elite Female Basketball Players Before and During Game Play
Bibliographic record
Abstract
This study determined the fluid balance of elite female basketball players before and during competition. Before and during 2 international games, 17 national-level players (age 24.2 ± 3 yr, height 180.5 ± 6 cm, mass 78.8 ± 8 kg) were assessed. Fluid-balance assessment included pregame hydration level as determined by urine specific gravity (USG), change in body mass during the game, ad libitum intake of water or sports drink, and estimated sweat losses. Mean (± SD) USG before Game 1 was 1.005 ± 0.002 and before Game 2 USG equaled 1.010 ± 0.005. Players lost an average of 0.7% ± 0.8% and 0.6% ± 0.6% of their body mass during Games 1 and 2, respectively. In each game, 3 players experienced a fluid deficit >1% of body mass, and 1 other, a fluid deficit >2%. Sweat losses in both games, from the beginning of the warm-up to the conclusion of the game (~125 min with average playing time 16-17 min), were approximately 1.99 ± 0.75 L. Fluid intake in Game 1 and Game 2 equaled 77.8% ± 32% and 78.0% ± 21% of sweat losses, respectively. Most players were hydrated before each game and did not become meaningfully dehydrated during the game. It is possible that the players who experienced the highest levels of dehydration also experienced some degree of playing impairment, and the negative relationship between change in body mass and shooting percentage in Game 2 provides some support for this notion.
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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) | 0.001 | 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".