Bibliographic record
Abstract
Anecdotal claims have suggested that an increasing number of ultramarathoners purposely undertake chronic low-carbohydrate (CHO) ketogenic diets while training, and race with very low CHO intakes, as a way to maximize fat oxidation and improve performance. However, very little empirical evidence exists on specific fueling strategies that elite ultramarathoners undertake to maximize race performance. The study's purpose was to characterize race nutrition habits of elite ultramarathon runners. Three veteran male ultrarunners (M ± SD; age 35 ± 2 years; mass 59.5 ± 1.7 kg; 16.7 ± 2.5 hr 100-mi. best times) agreed to complete a competition- specific nutrition intake questionnaire for 100-mi. races. Verbal and visual instructions were used to instruct the athletes on portion sizes and confirm dietary intake. Throughout 2014, the athletes competed in 16 ultramarathons with a total of 8 wins, including the prestigious Western States Endurance Run 100-miler (14.9 hr). The average prerace breakfast contained 70 ± 16 g CHO, 29 ± 20 g protein, and 21 ± 8 g fat. Athletes consumed an average of 1,162 ± 250 g of CHO (71 ± 20g/hr), with minor fat and protein intakes, resulting in caloric intakes totaling 5,530 ± 1,673 kcals (333 ± 105 kcals/hr) with 93% of calories coming from commercial products. Athletes also reported consuming 912 ± 322 mg of caffeine and 6.9 ± 2.4 g of sodium. Despite having limited professional nutritional input into their fueling approaches, all athletes practiced fueling strategies that maximize CHO intake and are congruent with contemporary evidence-based recommendations.
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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 | 0.000 | 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".