Aerobic fitness determines whole-body fat oxidation rate during exercise in the heat
Bibliographic record
Abstract
The purpose of this study was to determine whole-body fat oxidation in endurance-trained (TR) and untrained (UNTR) subjects exercising at different intensities in the heat. On 3 occasions, 10 TR cyclists and 10 UNTR healthy subjects (peak oxygen uptake = 60 ± 6 vs. 44 ± 3 mL·kg-1·min-1; p < 0.05) exercised at 40%, 60%, and 80% peak oxygen uptake in a hot, dry environment (36 °C; 25% relative humidity). To complete the same amount of work in all 3 trials, exercise duration varied (107 ± 4, 63 ± 1, and 45 ± 0 min for 40%, 60%, and 80% peak oxygen uptake, respectively). Substrate oxidation was calculated using indirect calorimetry. Blood samples were collected at the end of exercise to determine plasma epinephrine ([EPI]plasma) and norepinephrine ([NEPI]plasma) concentrations. The maximal rate of fat oxidation was achieved at 60% peak oxygen uptake for the TR group (0.41 ± 0.01 g·min-1) and at 40% peak oxygen uptake for the UNTR group (0.28 ± 0.01 g·min-1). TR subjects oxidized absolutely (g·min-1) and relatively (% of total energy expenditure) more fat than UNTR subjects at 60% and 80% peak oxygen uptake (p < 0.05). At these exercise intensities, TR subjects also had higher [NEPI]plasma concentrations than UNTR subjects (p < 0.05). In the heat, whole-body peak fat oxidation occurs at higher relative exercise intensities in TR than in UNTR subjects (60% vs. 40% peak oxygen uptake). Moreover, TR subjects oxidize more fat than UNTR subjects when exercising at moderate to high intensities (>60% peak oxygen uptake).
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".