General outlines for finding out and basic surveillance of whether a traffic accident is caused by driver sleepiness
Bibliographic record
Abstract
Driver sleepiness is one common reason for road crashes. The exact number of crashes is difficult to say. The aim of the present study is to identify criteria and protocols to use in field in order to learn more about driver sleepiness involvement in road crashes. The study has been done in collaboration with the police in Umeå. The police in Linköping and Gothenburg have also contributed during the development of the protocols. The study has three phases: review of earlier studies and experience from other countries, development of a protocol based on the most relevant criteria and finally a pilot test using the protocols at field directly at the road crashes. The result shows that attempts have been done all over the world. However, very few studies have been published or documented in scientific journals. It is more a question of practical experience at best having been documented in technical reports. The most used protocols are based on the work done by the Canadian police. They use two types of checklists; one at road directly at the crash site, one at the office in case the first analysis indicates suspicions that sleepiness has been involved. In the present study the protocol used directly at the crash site was used. The results show that the questions involved in the protocol were easily addressed to the involved drivers. The field study shows problems to motivate the police officers to take their time to use the protocol in field. The reason for this is unknown. One reason could be that this new routine needs time to be settled. The motivation among the police officers was seen to increase during the project. One explanation given was that they were more and more aware of the problem with sleepiness, thanks to the protocols. This will most truly make them more motivated over time.
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.
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 | 0.025 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.009 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.043 | 0.046 |
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".