LIMITS TO FAT OXIDATION BY SKELETAL MUSCLE DURING EXERCISE
Bibliographic record
Abstract
Compared with the body's stores of carbohydrate, endogenous fat depots are large and represent a potentially unlimited source of fuel for oxidation by skeletal muscle during aerobic exercise. However, fatty acid (FA) oxidation by muscle is limited, especially at the exercise intensities sustained by athletes during training and competition. This symposium will focus on nutritional interventions that promote FA oxidation, attenuate the rate of muscle glycogen utilization and modify subsequent exercise capacity and detail the mechanisms that underlie such perturbations. Dr. Spriet will provide an overview of the regulation of FA oxidation by skeletal muscle during exercise. Dr. Hawley will then discuss the impact of altering FA availability on substrate utilization and exercise performance. Dr. Burke and Dr. Helge will present data on the effects of both short-term (<7d) and long-term (>7d) adaptation to a high-fat diet on metabolism, training capacity and performance during endurance and ultra-endurance exercise. Finally, Dr. Hargreaves will discuss the effects of a high-fat diet on gene expression in skeletal muscle. This symposium will highlight the results of new investigations that have utilized various nutritional strategies to increase FA oxidation by muscle during exercise, as well as providing a state-of-the-art synopsis of our current knowledge of the regulation of FA oxidation.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".