Bibliographic record
Abstract
The population of senior drivers is at its highest levels ever and the numbers are going to increase substantially in the next few years. Collision statistics show that while the number of elderly driver crashes is relatively low, as a road user group they are over represented in fatal and serious injury collisions. It is crucial to ensure the senior drivers' safety while maintaining a high level of mobility and independence for this very important segment of the population. The paper looks at the programs of the Ministry of Transportation of Ontario (MTO) and the recent changes that were implemented to better accommodate the senior driver population. The over eighty age group has been one of the fastest growing groups amongst the Ontario driving population during the past decade. Drivers aged eighty and over have a collision risk that is substantially higher than drivers aged 70 to 79. The MTO program addresses the needs of the driver population over eighty. It ensures that the vision and knowledge requirements for licensure are met and provides information on age-related performance deficits and compensatory strategies. Another benefit of the program is the identification and road-testing of drivers who may be at risk. In addition to passing the vision and knowledge test, drivers over eighty have to attend a ninety minute group session every two years which focuses on age-related driving problems and strategies for risk reduction. This new approach provides a reasonable, research-based alternative and remains one of the most effective and stringent senior driver programs in North America. For the covering abstract see ITRD E108389.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".