Bibliographic record
Abstract
Fat is an important fuel for exercise but plays a secondary role to carbohydrate (CHO). Increasing fat use during exercise can decrease the reliance on CHO and spare CHO for later in training sessions or competitions that depend on CHO for success. The pathways that metabolize and oxidize fat are activated more slowly than CHO at the onset of exercise and reach a maximum at moderate exercise intensities. As exercise intensity increases to ∼75% VO(2max) and beyond, fat metabolism is inhibited: using CHO will increase the amount of energy produced per liter of oxygen consumed. The capacity for fat use during exercise is increased by aerobic training and the dietary combination of little or no CHO intake and high fat intake. Fat oxidation is very dependent on the mitochondrial volume of muscle but other key sites of regulation include release of fat from storage forms and fat transport across plasma and mitochondrial membranes. This chapter examines the control of fat metabolism during moderate and intense exercise with an emphasis on human findings and the adaptations that occur with aerobic training and other acute nutritional manipulations. Recent work using molecular and cellular compartmentalization techniques have advanced the knowledge in this area.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".