Using a biomathematical model to assess fatigue risk and scheduling characteristics in Canadian wildland firefighters
Bibliographic record
Abstract
This study examined the shift parameters that contribute to sleep loss and on-duty fatigue in British Columbia Wildfire Service (BCWS) firefighters using sleep–wake data, work–rest data and alertness and fatigue predictions from a biomathematical model (BMM) of fatigue. A total of 40 firefighters (age: 30.4 ± 11.6 years; 13 F, 26 M) volunteered over a 14-day consecutive fireline deployment, followed by a 3-day rest period, at two separate fires in British Columbia (during the 2015 fire season). Sleep–wake data were obtained using a wrist-worn accelerometer and self-reported sleep logs. Shift start and end times were provided by the BCWS at the completion of the study. Sleep and shift data were manually entered into a validated BMM (Circadian Alertness Simulator) to generate fatigue scores and shift work patterns. Shift duration was the major contributor to fatigue, as 46% (n = 274) of shifts were ≥14 h in length and the average shift length was 13.0 ± 0.62 h. However, none of the firefighters had a high-risk fatigue score (>60). The findings from this study indicated that using a BMM of fatigue can provide important insights into shift-work parameters that contribute to workplace fatigue and sleep loss in wildland firefighters.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".