Drowsy Drivers: Effect of Light and Circadian Rhythm on Crash Occurrence
Bibliographic record
Abstract
Fatigue is recognized as a pervasive problem for drivers, with effects judged comparable to those of alcohol. Unlike alcohol, which has a clear legal limit for impairment, there is no functional, objective measure of fatigue to identify drowsy drivers, although it is associated with Circadian rhythm. Severe single vehicle crashes, from the crash reports maintained in Ontario for 1999-2004 were analyzed. Crashes occurring when light varies but Circadian rhythms are low (2-5 am and 2-4 pm) were compared with crashes occurring when light conditions are similar but Circadian rhythm are higher (9-11 pm and 10 am - 12 noon). Logistic regression was used to see how light and other factors would predict single vehicle crashes occurring at times of low Circadian rhythm, when fatigue is more likely. Initial results indicated many circumstances associated with occurrence at these times: the age and sex of the driver and reported driver condition as well as weather. Some of these effects may be partly explained by exposure; e.g., young men may be more likely to drive in the early morning than women or older drivers. There is, however, an interaction between light and presumed alertness. In separate analyses for daytime and night time crashes most variables were significant for nighttime crashes but not for daytime events. The effects of alcohol and youth remained. Light, or its lack, may exacerbate the effects of other factors; this can be further investigated in controlled environments such as sleep laboratories and/or driving simulators.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".