Energy Balance during an Ironman Triathlon in Male and Female Triathletes
Bibliographic record
Abstract
Energy balance of 10 male and 8 female triathletes participating in an Ironman event (3.8-km swim, 180-km cycle, 42.2-km run) was investigated. Energy intake (EI) was monitored at 7 designated points by dietary recall of food and fluid consumption. Energy expenditure (EE) during cycling and running was calculated using heart rate-VO, regression equations and during swimming by the multiple regression equation: Y = 3.65v+ 0.02W- 2.545 where Yis VO,in L x min(-1), v is the velocity in m s(-1), Wis the body weight in kilograms. Total EE (10,036 +/- 931 and 8,570 +/- 1,014 kcal) was significantly greater than total EI (3,940 +/- 868 and 3,115 +/- 914 kcal, p <.001) for males and females, respectively, although energy balance was not different between genders. Finishing time was inversely related to carbohydrate (CHO) intake (g x kg(-1) x h(-1)) during the marathon run for males (r = -.75,p <.05), and not females, suggesting that increasing CHO ingestion during the run may have been a useful strategy for improving Ironman performance in male triathletes.
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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 | 0.000 | 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) | 0.000 | 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".