A snapshot of nitrogen balance in endurance-trained women
Bibliographic record
Abstract
Indirect estimates of the mean daily protein requirement for female endurance athletes are 1.2-1.4 g·kg(-1)·day(-1); however, an empirical estimate using nitrogen balance is absent. A 72-h nitrogen balance was determined during the mid-follicular phase of 10 female cyclists and triathletes training for 10.8 h·week(-1) (SD 2.8) following 2 habituated protein intakes: (i) normal habitual (NH) (protein 85 g·day(-1)), and (ii) isocaloric high-protein (HP) (∼2-fold increase in protein). Total 72-h nitrogen intake was determined from Leco total combustion of ingested food samples. Nitrogen loss was determined from micro-Kjeldahl analysis of 72-h total urinary nitrogen and representative resting and exercise sweat output, plus estimates for fecal and miscellaneous losses. Habituated (steady state) protein requirement was estimated from the mean regression of adapted nitrogen balance vs nitrogen intake. Mean (SD) 24-h dietary protein and energy intake was NH: 1.4 g·kg(-1)·day(-1) (0.2), energy: 9078 kJ·day(-1) (1492), HP: 2.7 g·kg(-1)·day(-1) (0.3) 8909 kJ·day(-1) (1411). Average 24-h urinary nitrogen and sweat urea nitrogen outputs were 13.2 g·day(-1) (2.4) and 0.33 g·day(-1) (0.08) in NH; 21.5 g·day(-1) (3.9) and 0.54 g·day(-1) (0.12) in HP, respectively. Nitrogen balance was negative in NH (-0.59 gN·day(-1) SD 1.64) but positive in HP (2.69 gN·day(-1) SD 3.09). Estimated mean protein requirement was 1.63 g·kg(-1)·day(-1) (95% confidence interval: 1.1-3.8). In conclusion the snapshot of follicular phase dietary protein requirement conformed with previous estimates for men, but was higher than previous nonempirical estimates for endurance-training women; low self-selected energy and carbohydrate intakes may explain the higher than expected nitrogen turnover, and consequently protein requirement.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".