Bibliographic record
Abstract
All-terrain vehicles (ATVs) are widely used in Canada for recreation, transportation and occupations such as farming. As motorized vehicles, they can be especially dangerous when used by children and young adolescents who lack the knowledge, physical size, strength, and cognitive and motor skills to operate them safely. The magnitude of injury risk to young riders is reflected in explicit vehicle manual warnings and the warning labels on current models, and evidenced by the significant number of paediatric hospitalizations and deaths due to ATV-related trauma. However, helmet use is far from universal among youth operators, and unsafe riding behaviours, such as driving unsupervised and/or driving with passengers, remain common. Despite industry warnings and public education that emphasize the importance of safety behaviours and the risks of significant injury to children and youth, ATV-related injuries and fatalities continue to occur. Until measures are taken that clearly effect substantial reductions in these injuries, restricting ridership by young operators, especially those younger than 16 years of age, is critical to reducing the burden of ATV-related trauma in children and youth. This document replaces a previous Canadian Paediatric Society position statement published in 2004.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".