Considerations for ultra-endurance activities: part 2 – hydration
Bibliographic record
Abstract
It is not unusual for those participating in ultra-endurance (> 4 hr) events to develop varying degrees of either hypohydration or hyperhydration. Yet, it is important for ultra-endurance athletes to avoid the performance limiting and potentially fatal consequences of these conditions. During short periods of exercise (< 1 hr), trivial effects on the relationship between body mass change and hydration status result from body mass loss due to oxidation of endogenous fuel stores, and water supporting the intravascular volume being generated from endogenous fuel oxidation and released with glycogen oxidation. However, these effects have meaningful implications during prolonged exercise. In fact, body mass loses well over 2% may be required during some ultra-endurance activities to avoid hyperhydration. Therefore, the typical hydration guidelines to avoid more than 2% body mass loss do not apply in ultra-endurance activities and can potentially result in hyperhydration. Fortunately, achieving the balance of proper hydration during ultra-endurance activities need not be complicated and has been well demonstrated to generally be achieved by simply drinking to thirst and avoiding excessive sodium supplementation with intention of replacing all sodium losses during the exercise.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".