Fitness‐related differences in the rate of whole‐body evaporative heat loss in exercising men are heat‐load dependent
Bibliographic record
Abstract
New Findings What is the central question of this study? Aerobic fitness modulates heat loss, but the heat‐load threshold at which fitness‐related differences in heat loss occur in young healthy men remains unclear. What is the main finding and its importance? We demonstrate using direct calorimetry that aerobic fitness modulates heat loss in a heat‐load‐dependent manner, with fitness‐related differences occurring between young men who have low and high fitness when the heat load is ∼≥500 W. Although aerobic fitness has been known for some time to modulate heat loss, our findings define the precise heat‐load threshold at which fitness‐related differences occur. The effect of aerobic fitness (defined as rate of peak oxygen consumption) on heat loss during exercise is thought to be related to the level of heat stress. However, it remains unclear at what combined exercise and environmental (net) heat‐load threshold these fitness‐related differences occur. To identify this, we assessed whole‐body heat exchange (dry and evaporative) by direct calorimetry in young (22 ± 3 years) men matched for physical characteristics with low (Low‐fit; 39.8 ± 2.5 ml O 2 kg −1 min −1 ), moderate (Mod‐fit; 50.9 ± 1.2 ml O 2 kg −1 min −1 ) and high aerobic fitness (High‐fit; 62.0 ± 4.4 ml O 2 kg −1 min −1 ; each n = 8), during three 30 min bouts of cycling in dry heat (40°C, 12% relative humidity) at increasing rates of metabolic heat production of 300 (Ex1), 400 (Ex2) and 500 W (Ex3), each followed by a 15 min recovery period. Each group was exposed to a similar net heat load (metabolic plus ∼100 W dry heat gain; P = 0.83) during each exercise bout [∼400 (Ex1), ∼500 (Ex2) and ∼600 W (Ex3); P < 0.01]. Although evaporative heat loss was similar between groups during Ex1 ( P = 0.33), evaporative heat loss was greater in the High‐fit (Ex2, 466 ± 21 W; Ex3, 557 ± 26 W) compared with the Low‐fit group (Ex2, 439 ± 22 W; Ex3, 511 ± 20 W) during Ex2 and Ex3 ( P ≤ 0.03). Conversely, evaporative heat loss for the Mod‐fit group did not differ from either the High‐fit or Low‐fit group during all exercise bouts ( P ≥ 0.09). We demonstrate that aerobic fitness modulates heat loss in a heat‐load‐dependent manner, such that young, highly fit men display greater heat‐loss capacity only at heat loads ∼≥500 W compared with their lesser trained counterparts.
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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) | 0.000 | 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".