Bibliographic record
Abstract
The field of research examining the link between dehydration and endurance performance is at the dawn of a new era. This article reviews the latest findings describing the relationship between exercise-induced dehydration and endurance performance and provides the knowledge necessary for competitive, endurance-trained athletes to develop a winning hydration strategy. Acute, pre-exercise body weight loss at or above 3% may decrease subsequent endurance performance. Therefore, endurance athletes should strive to start exercise well hydrated, which can be achieved by keeping thirst sensation low and urine color pale and drinking approximately 5-10 mL/kg body weight of water 2 h before exercise. During exercise lasting 1 h or less, dehydration does not decrease endurance performance, but athletes are encouraged to mouth-rinse with sports drinks. During exercise lasting longer than 1 h, in which fluid is readily available, drinking according to the dictates of thirst maximizes endurance performance. In athletes whose thirst sensation is untrustworthy or when external factors such as psychological stress or repeated food intake may blunt thirst sensation, it is recommended to program fluid intake to maintain exercise-induced body weight loss around 2% to 3%.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".