Thermometry, Calorimetry, and Mean Body Temperature during Heat Stress
Bibliographic record
Abstract
Heat balance in humans is maintained at near constant levels through the adjustment of physiological mechanisms that attain a balance between the heat produced within the body and the heat lost to the environment. Heat balance is easily disturbed during changes in metabolic heat production due to physical activity and/or exposure to a warmer environment. Under such conditions, elevations of skin blood flow and sweating occur via a hypothalamic negative feedback loop to maintain an enhanced rate of dry and evaporative heat loss. Body heat storage and changes in core temperature are a direct result of a thermal imbalance between the rate of heat production and the rate of total heat dissipation to the surrounding environment. The derivation of the change in body heat content is of fundamental importance to the physiologist assessing the exposure of the human body to environmental conditions that result in thermal imbalance. It is generally accepted that the concurrent measurement of the total heat generated by the body and the total heat dissipated to the ambient environment is the most accurate means whereby the change in body heat content can be attained. However, in the absence of calorimetric methods, thermometry is often used to estimate the change in body heat content. This review examines heat exchange during challenges to heat balance associated with progressive elevations in environmental heat load and metabolic rate during exercise. Further, we evaluate the physiological responses associated with heat stress and discuss the thermal and nonthermal influences on the body's ability to dissipate heat from a heat balance perspective.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".