Understanding modifiable determinants of fatigue from a physiological perspective in Canadian FireRangers
Bibliographic record
Abstract
Ontario FireRangers exhibit high annual injury rates and cite fatigue as a contributor, however \nseveral factors influence fatigue, including: energy expenditure, energy intake, stress levels and \nrecovery time. The purpose of this research was to evaluate the accuracy of heart rate variability \n(HRV) based estimates of energy expenditure (kilocalories) and to then assess energy balance, \nphysiological responses, and nutritional quality in Ontario FireRangers during different types of \nfire deployments. Firstbeat Bodyguard2 and Zephyr BioHarness3 monitors were used to collect \nHRV data, and individual audio-visual food logs were kept using an iPod Touch and analyzed in \nNutriBase Pro11 software. Sleep quantity was also measured using actisleep monitors, to assist \nwith energy expenditure calculations. The findings of this research support the use of HRV \nmonitoring for free-living, energy expenditure estimation. Furthermore, this research indicates \nthat Ontario FireRangers exhibit high daily energy demands, under-consume kilocalories, deviate \nfrom ideal nutrient consumption profiles and have varying levels of stress and recovery time, \ndepending on deployment type.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".