Considerations for ultra-endurance activities: part 1- nutrition
Bibliographic record
Abstract
Ultra-endurance activities (≥ 4h) present unique challenges that, beyond fatigue, may be exacerbated by sub-optimal nutrition during periods of increased requirements and compromised gastrointestinal function. The causes of fatigue during ultra-endurance exercise are multi-factorial. However, mechanisms can potentially include central or peripheral fatigue, thermal stress, dehydration, and/or endogenous glycogen store depletion; of which optimising nutrition and hydration can partially attenuate. If exercise duration is long enough (e.g. ≥ 10h) and exercise intensity low enough (e.g. 45-60% of maximal oxygen uptake), it is bio-energetically plausible that ketogenic adaptation may enhance ultra-endurance performance, but this requires scientific substantiation. Conversely, the scientific literature has consistently demonstrated that daily dietary carbohydrates (3-12g/kg/day) and carbohydrate intake (30-110g/h) during ultra-endurance events can enhance performance at individually tolerable intake rates. Considering gastrointestinal symptoms are common in ultra-endurance activities, effective dietary prevention and management strategies may provide functional, histological, systemic, and symptomatic benefits. Taken together, a well-practiced and individualized fuelling approach is required to optimize performance in ultra-endurance events.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".