Fuel selection during short-term submaximal treadmill exercise in the cold is not affected by pre-exercise low-intensity shivering
Bibliographic record
Abstract
Exercise and shivering rely on different metabolic pathways and consequently, fuel selection. The present study examined the effects of a pre-exercise low-intensity shivering protocol on fuel selection during submaximal exercise in a cold environment. Nine male subjects exercised 4 times for 60 min at 50% (LOW) or 70% (MOD) of their peak oxygen consumption on a motorized treadmill in a climatic chamber set at 0 °C with (SHIV) and without (CON) a pre-exercise cooling protocol, inducing low-intensity shivering. Thermal, cardiorespiratory and metabolic responses were measured every 15 min whereas blood samples were collected every 30 min to assess serum nonesterified fatty acids (NEFA), glycerol, glucose, β-hydroxybutyrate (BHB) and plasma catecholamine concentrations. Rectal and skin temperatures were lower in the SHIV condition, within LOW and MOD conditions, during the first 45 min of exercise. Norepinephrine (NE) concentration was greater in SHIV vs. CON within LOW (1.39 ± 0.17 vs. 0.98 ± 0.17 ng·mL(-1)) and MOD (1.50 ± 0.20 vs. 1.01 ± 0.09 ng·mL(-1)), whereas NEFA, glycerol and BHB were greater in SHIV vs. CON (1060 ± 49 vs. 898 ± 78 μmol·L(-1); 0.27 ± 0.02 vs. 0.22 ± 0.03 mmol·L(-1); 0.39 ± 0.06 vs. 0.27 ± 0.04 mmol·L(-1), respectively) within MOD only. No changes were observed in fat or carbohydrate oxidation between SHIV and CON during exercise. Despite increases in NE, NEFA, glycerol and BHB from pre-exercise low-intensity shivering, fuel selection during short-term submaximal exercise in the cold was unaltered.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".