Th Effects Exercise Within Personal Protective Equipment Microclimate On Mental Processing On Different Age Populations
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to determine the effects of exercise within personal protective equipment microclimate leading to rapid head acquisition on mental processing and decision making in different age populations. METHODS: The study was factorial in design and included 15 male participants with an age range from 19-54 who were divided into 2 groups: 30 years old and above group (+30) (n=8), and 29 years old and below group (-29) (n=7). Each group preformed a Go/No-Go test while wearing a Muse headband to obtain P300 ERPs, prior to and post exercise in firefighting turnout gear (PPE) and t-shirts and shorts while wearing a backpack matched in mass to the gear worn in PPE (CON). Subjects completed a graded exercise test until core temperature had rose 39.5 ° C, or voluntary max had been achieved. The muse data was collected/analyzed by Peer-Analytics and later tested within Excel by a 2 tailed T-test between: 30+ and -29, and CON and PPE conditions. RESULTS: There was no significant differences between the 30+ and -29 P300 ERPs or within each groups CON/PPE conditions. However, both groups made significantly more errors (p<0.05) post-PPE than pre-PPE (+30: μpost= 18.375, μpre=5.625 -29: μpost= 25.143, μpre=10.714) while only the 30+ showed significant difference between post CON/PPE trails (μPPE= 18.375, μCON=12.5). There was no difference between -23 and +30 when comparing post-PPE errors. CONCLUSION: Exercising within a personal protective equipment microclimate will negatively affect executive function of decision making regardless of age.
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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".