Shifting focus: Time to look beyond the classic physiological adaptations associated with human heat acclimation
Bibliographic record
Abstract
Planet Earth is warming at an unprecedented rate and our future is now assured to be shaped by the consequences of more frequent hot days and extreme heat. Humans will need to adapt both behaviorally and physiologically to thrive in a hotter climate. From a physiological perspective, countless studies have shown that human heat acclimation increases thermoeffector output (i.e., sweating and skin blood flow) and lowers cardiovascular strain (i.e., heart rate) during heat stress. However, the mechanisms mediating these adaptations remain understudied. Furthermore, several possible benefits of heat acclimation for other systems and functions involved in maintaining health and performance during heat stress remain to be elucidated. This review summarizes recent advances in human heat acclimation, with emphasis on recent studies that (1) advanced our understanding of the mechanisms mediating improved thermoeffector output and (2) investigated adaptations that go beyond those classically associated with heat acclimation. We highlight that these studies have contributed to a better understanding of the integrated physiological responses underlying human heat acclimation while leaving key unanswered questions that will need to be addressed in the future.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".