Drowsy Drivers: Effect of Light and Circadian Rhythm on Crash Occurrence
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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 it