Response
Bibliographic record
Abstract
Dear Editor-in-Chief: We appreciate the interest of Bain et al. (2) in our work (6). Recently, Bain et al. (1) argued that the reported reductions in body heat storage with cold water ingestion (7,8) were likely due to the inherent underestimation of body heat storage associated with thermometry (5). To circumvent this problem, they used partitional calorimetry to assess changes in body heat storage with intermittent ingestion of water of various temperatures during a 75-min exercise bout. In contrast to previous reports, they showed a disproportionate increase in evaporative heat loss (as estimated by changes in body weight) with ingestion of hot (50°C) relative to cold (1.5°C) water, which amounted to reductions in body heat storage. They repeated a similar protocol (9), which also measured local sweat rate (LSR) at multiple sites. In addition to whole-body sweat losses that paralleled their previous work (1), they observed transient increases and decreases in LSR after each bolus of hot and cold water, respectively, compared with water ingested at 37°C. Importantly, reflex changes occurred in both cases despite similar core and skin temperatures between conditions, ultimately failing to resolve the debate on the ingestion of hot versus cold water in the context of body heat storage. Noteworthy, they reported a similar pattern of response for LSR when water was delivered directly to the stomach via a nasogastric tube, but not when water was swilled within the mouth only, highlighting the importance of gastrointestinal thermoreceptors in modulating sweating. As recently discussed by Kenny and Jay (5), direct calorimetry is a precise way of measuring real-time changes in whole-body sweat rate (WBSR) under conditions permitting full evaporation. Using this technique, we observed an elevated WBSR with hot, relative to cold, water ingestion (6), albeit our measurements of LSR at sites similar to those of Morris et al. (9) yielded inconsistent results. However, our findings support those of Morris et al. (9) in showing that reflex changes in LSR are also observed at the whole-body level with hot and cold water ingestion in the absence of differences in core and skin temperatures. In addition, although our observations for WBSR are consistent with the findings of Bain et al. (1), we reported a proportionate adjustment in heat exchange that led to no differences in body heat storage following exercise, whereas Bain et al. (1) observed a reduction in body heat storage with hot, relative to cold, water ingestion. Presumably, our studies should have arrived at similar conclusions if WBSR were accurately assessed, especially given that both partitional and direct calorimetry rely on environments that permit complete sweat evaporation. In their letter, Bain et al. (2) indicated that their conditions were likely not suitable for ensuring 100% sweat evaporation, despite choosing experimental conditions to accommodate this limitation. It is unclear whether the addition of strategically placed fans would eliminate these conflicting findings. Regardless, we concur that cold water ingestion is the obvious choice under conditions that restrict heat loss, and we strongly recommend cold water under any condition given the many benefits that may impact exercise performance (3,4). Dallon T. Lamarche Robert D. Meade Ryan McGinn Martin P. Poirier Brian J. Friesen Glen P. Kenny Human and Environmental Physiology Research Unit School of Human Kinetics, University of Ottawa Ottawa, ON, CANADA [email protected]
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 | 0.002 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.352 | 0.204 |
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".