Nutritional Preparation for an Ultra-Endurance Swimmer: A Case Report
Bibliographic record
Abstract
Introduction: Nutritional considerations and metabolic determinates for ultra-endurance events continue to evolve with the increasing popularity of events. This case study assessed the nutritional requirements required of 30-year-old male ultra-endurance athlete to successfully complete an endurance open-water swim. Methods: Dietary intake in preparation for the event was assessed, estimated caloric needs, and a nutritional analysis was performed on the athlete to determine nutritional intake that was required to successfully complete the endurance swim event. The athlete was monitored for 24.3 miles (Ontario, Canada to Northeast, Pennsylvania, United States). Day of nutritional changes were observed, and body composition was assessed pre- and post-event. Results: The swimmer was monitored for 11 hours, 28 minutes and 5 seconds. Determination of athletes’ potential metabolic energy would shape the energy cost to complete the endurance event. Modifications prior to the ultra-endurance swim event met the athlete’s energy requirement for training and the energy reserve needed for the endurance swim event. Conclusions: The addition of calories and nutritional modifications prior to the ultra-endurance swim event met the athlete’s energy requirement for training and the energy reserve needed for the endurance swim event. This case study highlights the importance of providing nutritional support for ultra-endurance athletes and the difficulty in predicting energy expenditure for such an event.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| 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".