Dissociating Biophysical and Training-Related Determinants of Core Temperature
Bibliographic record
Abstract
Dear Editor-in-Chief: In a recent review, Dr. Mora-Rodriguez (5) concluded that core temperature is predicted by the percentage of peak oxygen uptake (%V˙O2peak) in physiologically compensable conditions and absolute heat production in uncompensable conditions (see Fig. 4 in (5)). Heat balance calculations (3) and recent evidence from our laboratory (4) suggest otherwise. High (HI) and low (LO) V˙O2peak groups matched for mass and body surface area (BSA), exercising at 540 W heat production in compensable conditions, showed similar changes in rectal temperature (Tre) and whole-body sweat losses despite vastly different relative intensities (39.7% vs 57.6% V˙O2peak) (4). Furthermore, absolute end-exercise Tre was ∼0.2°C lower in the HI group simply because of lower preexercise values. In contrast, exercise at 60% V˙O2peak (heat production, 844 vs 600 W) yielded greater changes in Tre and absolute end-exercise Tre values in the HI group, and whole-body sweat losses were greater in the HI group because of higher evaporative heat balance requirements (Ereq) (4). In compensable conditions, these findings suggest the following after eliminating differences in mass and BSA: (i) changes in Tre are determined by heat production, not %V˙O2peak; (ii) any differences in end-exercise absolute Tre between fitness groups only arise because of differences in preexercise Tre; and (iii) sweating is not altered by a high V˙O2peak. We further suggested that groups heterogeneous for body morphology may be compared for changes in Tre using a fixed heat production per unit mass (W·kg–1) in compensable environments. This approach explains the greater Tre changes in trained subjects at 40%V˙O2peak (8.2 vs 6.1 W·kg–1) (6), with these greater changes compensated by different preexercise Tre values, leading to similar absolute end-exercise temperatures between training groups. By definition, uncompensable conditions arise when Ereq exceeds the maximum possible evaporation rate (Emax). Dr. Mora-Rodriguez suggests that Ereq > Emax at a similar %V˙O2peak in trained and untrained groups (see Fig. 4 in (5)). However, at a given %V˙O2peak, Ereq is lower in untrained individuals because of their lower heat production, and the primary reason that Ereq > Emax at the same V˙O2peak in the proposed model is the lower maximum skin wettedness (ωmax) assigned to untrained individuals (ωmax = 0.85). Although maximum sweat rate is probably different (1), such large ωmax adjustments as a function of training status do not seem justified by the literature. A ωmax of 0.85 and 1.00 were proposed originally for nonheat-acclimated and heat-acclimated individuals, respectively (2), but physical training only imparts partial acclimation (7). Even if ωmax differences between training groups are as large as proposed, heat balance calculations (3) show the %V˙O2peak at which Ereq > Emax still should be greater in unfit/untrained subjects with the same BSA/mass ratio. The %V˙O2peak at which Ereq > Emax declines with decreasing BSA/mass ratio. Because the BSA/mass ratio of the author’s untrained group (6) was lower, it appears that a combination of different physical characteristics and assigned ωmax values led to a conclusion with restricted validity. A more robust descriptor of the reported differences in Tre between training groups at high relative exercise intensities (6) may be the difference between Ereq and Emax expressed in W·kg–1. Matthew N. Cramer Nathan B. Morris Ollie Jay Thermal Ergonomics Laboratory School of Human Kinetics University of Ottawa Ottawa, Ontario, Canada
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".