Erosion of the air velocity cooling benefit for workers in the mining industry associated with changing workforce characteristics
Bibliographic record
Abstract
The effects of air velocity in promoting cooling through the evaporation of sweat generated when working at a high metabolic rate and/or in hot environments is well known. Many of the environmental heat stress indices consider the effect of air movement directly or indirectly within their determination, be it through the use of a specific instrument i.e. a wet-bulb globe temperature meter or Kata thermometer, or inclusion of in the calculation of a stress index, including the effective temperature, air cooling power, thermal working limit, ISO 7933?s predicted heat strain model. Within these models, ambient air temperature, relative humidity and barometric pressure are also considered in varying degrees to quantify an airstream's capacity to evaporate sweat. Deep mine studies have shown cumulative increases in heat exhaustion rates with both decreasing air velocity and decreasing air cooling powers. However, despite a capacity to cool, the resultant cooling is very dependant on sweat production and its availability for evaporation; these are included where sweat rates and skin wettedness are considered. The first depends on hydration and physiological factors whereas the second is more an engineering factor related to the degree of clothing required. As sweat rates deteriorate with aging, the benefits of evaporative cooling potential can similarly be expected to be diminished. This paper continues to report upon on-going physiological studies exploring the potential for heat stress in deep underground mines through the simulation of mining activities in environmental chambers and a human calorimeter facility. The focus of this paper is to introduce preliminary data suggesting that increasing air velocity (0.5, 1.5 & 3.0 m/s) is beneficial for young subjects (22 ± 1 yrs), as indicated by lower core and skin temperature measures and heart rate during an intermittent work protocol, and then show a lesser benefit for older subjects (62 ± 2 yrs). Both subject groups performed the same task under the same environmental condition (35°C, 60% RH) wearing a standard mining ensemble. The study similar to others being performed under the Deep Mining Research Consortium funded research continues to provide evidence that a greater understanding of heat management processes is required and that worker education is critical as environmental measures and health and safety regulations may not be expected to include all demographic factors such as age, sex and health which are known to affect human thermoregulatory response.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".