Effects of different meal compositions after exercise on fat and carbohydrate oxidation in women with different levels of body fat
Bibliographic record
Abstract
We investigated the effects of consuming a high-carbohydrate meal (HC), high-fat meal (HF), or no meal (CON) following exercise on fat and carbohydrate oxidation (FAT-OX, CHO-OX) in women with differing levels of body fat. Healthy, physically active females were divided into a Lower Fat (<25% fat, n = 10) or Higher Fat (>25% fat, n = 9) group and tested on 4 occasions. During session 1, body composition and maximal oxygen consumption were determined. During 3 treatment sessions, subjects preformed treadmill exercise at 55% of maximal oxygen consumption until 350 kcal were expended. At 10 min postexercise subjects consumed a liquid meal standardized to provide energy equal to 20% of 24-h energy expenditure plus the 350 kcal of exercise. The HC meal comprised 64% carbohydrate, 21.6% fat, and 14.4% protein. The HF meal comprised 24% carbohydrate, 62% fat, and 14% protein. CON consisted of water equal to the meal volume. During exercise and 2 h postexercise, expired gases were collected to determine FAT-OX and CHO-OX. During exercise CHO-OX was a significantly higher for the Lower Fat group and FAT-OX was significantly higher for the Lower Fat group for each of the meal conditions. A significant difference was observed across meals (p < 0.05) for CHO-OX (first hour) and for CHO-OX and FAT-OX (second hour) postexercise. There were no significant differences (p > 0.05) between the Lower Fat and Higher Fat groups for either recovery period. In physically active females, the macronutrient composition of the postexercise meal affects substrate oxidation, but the level of body fat does not.
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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 | 0.001 |
| 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".