Preservation of Cognitive Performance with Age during Exertional Heat Stress under Low and High Air Velocity
Bibliographic record
Abstract
UNLABELLED: Older adults may be at greater risk for occupational injuries given their reduced capacity to dissipate heat, leading to greater thermal strain and potentially cognitive decrements. PURPOSE: To examine the effects of age and increased air velocity, during exercise in humid heat, on information processing and attention. METHODS: Nine young (24 ± 1 years) and 9 older (59 ± 1 years) males cycled 4 × 15 min (separated by 15 min rest) at a fixed rate of heat production (400 W) in humid heat (35°C, 60% relative humidity) under 0.5 (low) and 3.0 (high) m·s(-1) air velocity wearing coveralls. At rest, immediately following exercise (end exercise), and after the final recovery, participants performed an abbreviated paced auditory serial addition task (PASAT, 2 sec pace). RESULTS: PASAT numbers of correct responses at end exercise were similar for young (low = 49 ± 3; high = 51 ± 3) and older (low = 46 ± 5; high = 47 ± 4) males and across air velocity conditions, and when scored relative to age norms. Psychological sweating, or an increased sweat rate with the administration of the PASAT, was observed in both age groups in the high condition. CONCLUSION: No significant decrements in attention and speeded information processing were observed, with age or altered air velocity, following intermittent exercise in humid heat.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".