Hyperthermia and voluntary exhaustion: integrating models and future challenges
Bibliographic record
Abstract
Over the past decade, research interest has risen on the direct effects of temperature on exercise capacity and tolerance, particular in the heat. Two major paradigms have been proposed for how hyperthermia may contribute to voluntary fatigue during exercise in the heat. One suggests that voluntary exhaustion occurs upon the approach or attainment of a critical internal temperature through impairment in a variety of physiological systems. An alternate perspective proposes that thermal inputs modulate the regulation of self-paced workload to minimize heat storage. This review seeks to summarize recent research leading to the development of these two models for hyperthermia and fatigue and explore possible bridges between them. Key areas for future research and development into voluntary exhaustion in the heat include (i) the development of valid and non-invasive means to measure brain temperature, (ii) understanding variability in perception and physiological responses to heat stress across individuals, (iii) extrapolating laboratory studies to field settings, (iv) understanding the failure in behavioural and physiological thermoregulation that leads to exertional heat illness, and (v) the integration of physiological and psychological parameters limiting voluntary exercise in the heat.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".