Regulation of skeletal muscle fat oxidation during exercise in humans
Bibliographic record
Abstract
Fat and carbohydrate are the major energy substrates during aerobic exercise in well-fed humans. The regulation of fat metabolism during exercise has not been as thoroughly studied as carbohydrate metabolism, especially in human skeletal muscle. Traditionally, it was believed that the regulation of skeletal muscle fat metabolism was mainly at the level of the delivery of free fatty acids to the muscle (adipose tissue lipolysis) and transport of the long chain fatty acids into the mitochondria. It is now known that the transport of fatty acids into the muscle cell and the regulation of muscle triacylglycerol lipase activity are also important sites of regulation. New lines of research are currently underway examining the regulation of fat metabolism in skeletal muscle at the level of fat transport across the sarcolemmal and mitochondrial membranes and regulation of TG lipase activity in both rodent and human models. A major goal of this research is to determine the regulatory signals that control the up-regulation of fat metabolism during the transition from rest to low and moderate aerobic exercise (30-65% (.)VO(2max)) and the down-regulation that occurs when exercising at intense aerobic exercise (approximately 85% (.)VO(2max)). Although it is expected that the signals that activate carbohydrate metabolism during exercise (Ca and free ADP, AMP, and P(i)) would also play a role in fat metabolism, this has not been demonstrated to date.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".