Effects of carbohydrate ingestion during exercise on substrate oxidation in physically active women with different body compositions
Bibliographic record
Abstract
We examined the effects of consuming a 6% carbohydrate (CHO) beverage during exercise on substrate oxidation in active, young Caucasian women with higher body fat (HF) (>25%) and lower body fat (LF) (<25%). The HF group (n = 9) had 32.4% ± 5.6% fat and the LF group (n = 8) had 20.0% ± 3.0% fat. Subjects completed 2 sessions of 45 min of treadmill exercise at 55% of maximal oxygen consumption. Immediately prior to and at 15-min intervals during exercise, subjects consumed 25% of a total volume of a CHO beverage (1 g CHO·kg(-1)) or a placebo (PLC). During exercise and for 2 h after exercise, expired gases were analyzed to determine oxidation rates for CHO (CHO-OX) and fat (FAT-OX). During exercise, significant differences (p < 0.05) in CHO-OX (mg·kg fat-free mass(-1)·min(-1)) were found between groups for the CHO trial (LF, 35.4 ± 4.7 vs. HF, 29.8 ± 3.6) and the PLC trial (LF, 33.7 ± 6.4 vs. HF, 26.3 ± 4.3). CHO-OX was significantly higher during the first hour of recovery in both the LF group (CHO, 9.3 ± 2.1 vs. PLC, 5.3 ± 2.4) and the HF group (CHO, 8.7 ± 2.0 vs. PLC, 4.2 ± 1.0), but during the second hour of recovery, only the HF group had a higher CHO-OX (CHO, 5.3 ± 1.8 vs. PLC, 3.9 ± 1.1). FAT-OX was significantly lower during the first hour of recovery in both the LF group (CHO, 0.6 ± 0.4 vs. PLC, 1.0 ± 0.4) and the HF group (CHO, 0.4 ± 0.4 vs. PLC, 1.4 ± 0.4), as well as during the second hour of recovery in both the LF group (CHO, 0.8 ± 0.4 vs. PLC, 1.3 ± 0.5) and the HF group (CHO, 0.9 ± 0.6 vs. PLC, 1.6 ± 0). CHO ingestion promotes CHO-OX and suppresses FAT-OX during and following exercise in physically active women with low and high levels of body fat.
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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".