Novel Tools in Determining the Physiological Demands and Nutritional Practices of Ontario FireRangers during Fire Deployments
Bibliographic record
Abstract
INTRODUCTION: The seasonal profession of wildland fire fighting in Canada requires individuals to work in harsh environmental conditions that are physically demanding. The purpose of this study was to use novel technologies to evaluate the physiological demands and nutritional practices of Canadian FireRangers during fire deployments. METHODS: Participants (n = 21) from a northern Ontario Fire Base volunteered for this study and data collection occurred during the 2014 fire season and included Initial Attack (IA), Project Fire (P), and Fire Base (B) deployments. Deployment-specific energy demands and physiological responses were measured using heart-rate variability (HRV) monitoring devices (Zephyr BioHarness3 units). Food consumption behaviour and nutrient quantity and quality were captured using audio-video food logs on iPod Touches and analyzed by NutriBase Pro 11 software. RESULTS: Insufficient kilocalories were consumed relative to expenditure for all deployment types. Average daily kilocalories consumed: IA: 3758 (80% consumption rate); P: 2945±888.8; B: 2433±570.8. Average daily kilocalorie expenditure: IA: 4538±106.3; P: 4012±1164.8; B: 2842±649.9. The Average Macronutrient Distribution Range (AMDR) for protein was acceptable: 22-25% (across deployment types). Whereas the AMDR for fat and carbohydrates were high: 40-50%; and low: 27-37% respectively, across deployment types. CONCLUSIONS: This study is the first to use the described methodology to simultaneously evaluate energy expenditures and nutritional practices in an occupational setting. The results support the use of HRV monitoring and video-food capture, in occupational field settings, to assess job demands. FireRangers expended the most energy during IA, and the least during B deployments. These results indicate the need to develop strategies centered on maintaining physical fitness and improving food practices.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".