Fatigue Risk of Long-Distance Driver as the Impact of the Duration of Work
Bibliographic record
Abstract
Studies on the driver's fatigue, must focus on at least two things: the time-of-day that affect by circadian factors, and time-on-task. This paper discusses the risk level of driver fatigue, which generally have to drive in a long duration or more than 4 hours. The risk of fatigue was assessed using Fatigue Likelihood Scoring (FLS) by Transport Canada. Based on interviews with 24 inter-city bus drivers, 18 of the 24 drivers have a very high risk of fatigue that characterized by FLS scores greater than 20, while the rest have a high risk driver that characterized by FLS value greater than 10. A high risk of chronic fatigue that experienced by most of drivers caused by working hours which is more than 36 hours in a week, the duration of the shift of greater than 8 hours a day, lack of time off, the amount hours of driving at night, and the amount of time off.
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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 | 0.001 | 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 | 0.000 | 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".