Bibliographic record
Abstract
In many occupational settings, clothing must be worn to protect individuals from hazards in their work environment. However, personal protective clothing (PPC) restricts heat exchange with the environment due to high thermal resistance and low water vapor permeability. As a consequence, individuals who wear PPC often work in uncompensable heat stress conditions where body heat storage continues to rise and the risk of heat injury is greatly enhanced. Tolerance time while wearing PPC is influenced by three factors: (i) initial core temperature (Tc), affected by heat acclimation, precooling, hydration, aerobic fitness, circadian rhythm, and menstrual cycle (ii) Tc tolerated at exhaustion, influenced by state of encapsulation, hydration, and aerobic fitness; and (iii) the rate of increase in Tc from beginning to end of the heat-stress exposure, which is dependent on the clothing characteristics, thermal environment, work rate, and individual factors like body composition and economy of movement. Methods to reduce heat strain in PPC include increasing clothing permeability for air, adjusting pacing strategy, including work/rest schedules, physical training, and cooling interventions, although the additional weight and bulk of some personal cooling systems offset their intended advantage. Individuals with low body fatness who perform regular aerobic exercise have tolerance times in PPC that exceed those of their sedentary counterparts by as much as 100% due to lower resting Tc, the higher Tc tolerated at exhaustion and a slower increase in Tc during exercise. However, questions remain about the importance of activity levels, exercise intensity, cold water ingestion, and plasma volume expansion for thermotolerance.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.076 | 0.042 |
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".