Abdominal fat reducing outcome of exercise training: fat burning or hydrocarbon source redistribution?
Bibliographic record
Abstract
Fat burning, defined by fatty acid oxidation into carbon dioxide, is the most described hypothesis to explain the actual abdominal fat reducing outcome of exercise training. This hypothesis is strengthened by evidence of increased whole-body lipolysis during exercise. As a result, aerobic training is widely recommended for obesity management. This intuition raises several paradoxes: first, both aerobic and resistance exercise training do not actually elevate 24 h fat oxidation, according to data from chamber-based indirect calorimetry. Second, anaerobic high-intensity intermittent training produces greater abdominal fat reduction than continuous aerobic training at similar amounts of energy expenditure. Third, significant body fat reduction in athletes occurs when oxygen supply decreases to inhibit fat burning during altitude-induced hypoxia exposure at the same training volume. Lack of oxygen increases post-meal blood distribution to human skeletal muscle, suggesting that shifting the postprandial hydrocarbons towards skeletal muscle away from adipose tissue might be more important than fat burning in decreasing abdominal fat. Creating a negative energy balance in fat cells due to competition of skeletal muscle for circulating hydrocarbon sources may be a better model to explain the abdominal fat reducing outcome of exercise than the fat-burning model.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".