Higher intakes of low‐fat milk combined with 12 weeks of endurance training does not result in lower fat mass and higher lean mass.
Bibliographic record
Abstract
Previous studies showed that a low‐fat milk intervention combined with resistance training resulted in lower fat mass and higher lean mass in males and females. The purpose of this study was to determine the effects of combining increased low‐fat milk intake with endurance exercise on fat and lean mass. 32 healthy young males were randomized and blindly assigned into one two groups 1) MILK (3 additional servings of low‐fat milk, 750 mL) or 2) CHO (maltodextrin beverage, isocaloric to MILK). All participants completed a 12‐week endurance‐training program. The exercise program consisted of cycling 1 hour per day at ~60% VO2 peak, 5 days per week for 12 weeks. Whole body fat and lean mass were determined using dual‐energy X‐ray absorptiometry (DXA) that was performed before and after training. 23 participants completed the study. Changes in body mass (MILK: 0.39±0.61 kg; CHO: 0.30±0.77 kg), fat mass (MILK: −420.7±522.1 g; CHO: 99.6±794.8 g) and lean mass (MILK: 831.2±509.8 g; CHO: 624.9±434.6 g) were similar between the 2 groups. Findings to date suggest that combining a higher intake of low‐fat milk with 12 weeks of endurance training does not improve changes in body composition. Further analyses are ongoing. Funding provided by The Dairy Farmers of Canada.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".