Motor Vehicle Collisions and Their Demographics: A 5‐Year Retrospective Study of the Hamilton‐Wentworth Niagara Region*
Bibliographic record
Abstract
This retrospective study examined population demographics associated with motor vehicle collision (MVC) fatalities over a 5-year period in the Hamilton-Wentworth Niagara region. Variables were drawn from the five factors proposed by Fierro (1) for investigating deaths caused by transportation: human, chemical, environmental, vehicular, and highway. Factors analyzed included age, gender, position to the vehicle, site(s) of injury, toxicology, environmental contributors, and vehicular findings. From 1999 to 2004, there were 321 MVC fatalities that primarily involved males 20 to 29 years of age and commonly drivers or pedestrians. Cars and trucks were the most frequent vehicles. Fatalities occurred most often on local and regional roads on Fridays and Sundays between 6 pm and 6 am. Mechanical failure and weather conditions were not significant contributors. Toxicological analyses (275/321) were performed on the majority of the study population. Ethanol was present in isolation and with other substances, especially cannabis, mostly in male drivers 20-59 years of age.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".