Bibliographic record
Abstract
The CMA's new guide for physicians, Determining Medical Fitness to Drive, recognizes that while the rate of decline in physical and mental function varies from person to person as they age, these changes eventually affect everyone's ability to drive. The guide states that “slowed reaction time, lack of attentiveness, poor judgement and faulty attitudes are responsible for many crashes at all ages. These factors assume an increasing importance with advancing years.” Transport Canada statistics (www.tc.gc.ca/securiteroutiere/stats/stats98/en/st98agee.htm) show that Canadians over the age of 65 have a much higher annual fatality rate per 100 000 population (14.5) than those in their middle years (35–64), whose rate is rate is 8.2 deaths per 100 000. Young Canadians (15–34) come close to matching the fatality rate of seniors (14.1 per 100 000). The rate of injury, however, declines with age. A May 16, 2000, Gallup poll revealed that Canadians appear to favour mandatory testing of elderly drivers. Eighty-five percent of respondents agreed that elderly drivers should be tested; people aged 30 to 39 years were most likely to agree (92%) compared with 68% of those 65 years of age or older. Over a third (36%) thought mandatory testing should begin at age 65, but only 3% thought testing should only be mandatory for those over 80. Respondents from Atlantic Canada were the least likely to agree to mandatory testing (76%) but had the highest proportion of respondents (44%) who felt testing should begin at 65. —
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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.046 | 0.029 |
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".