Fluid, energy, and nutrient recovery via ad libitum intake of different commercial beverages and food in female athletes
Bibliographic record
Abstract
) lost 2.0% ± 0.3% BM cycling at ∼75% maximal oxygen uptake before completing a 4-h recovery period with ad libitum access to 1 of 4 beverages: Water, Powerade (Sports Drink), Up & Go Reduced Sugar (Lower Sugar (LS)-MILK) or Up & Go Energize (Higher Protein (HP)-MILK). Participants also had two 15-min opportunities to access food within the first 2 h of the recovery period. Beverage intake, total water/nutrient intake, and indicators of fluid recovery (BM, urine output, plasma osmolality), gastrointestinal tolerance and palatability were assessed periodically. While total water intake (from food and beverage) (Water: 1918 ± 580 g; Sports Drink: 1809 ± 338 g; LS-MILK: 1458 ± 431 g; HP-MILK: 1523 ± 472 g; p = 0.010) and total urine output (Water: 566 ± 314 g; Sports Drink: 459 ± 290 g; LS-MILK: 220 ± 53 g; HP-MILK: 230 ± 117 g; p = 0.009) differed significantly by beverage, the quantity of ingested water retained was similar across treatments (Water: 1352 ± 462 g; Sports Drink: 1349 ± 407 g; LS-MILK: 1238 ± 400 g; HP-MILK: 1293 ± 453 g; p = 0.691). Total energy intake (from food and beverage) increased in proportion to the energy density of the beverage (Water: 4129 ± 1080 kJ; Sports Drink: 5167 ± 643 kJ; LS-MILK: 6019 ± 1925 kJ; HP-MILK: 7096 ± 2058 kJ; p = 0.014). When consumed voluntarily and with food, different beverages promote similar levels of fluid recovery, but alter energy/nutrient intakes. Providing access to food and understanding the longer-term dietary goals of female athletes are important considerations when recommending a recovery beverage.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".