Air Bag Induced Fatalities in Canada
Bibliographic record
Abstract
The past decade has seen the widespread introduction of air bags in the Canadian fleet; however, the collision performance of these systems, as supplements to seat belts, has been mixed. In severe crashes, air bags have provided good head protection, whereas in low severity collisions, the energy of the deploying air bag has often been the dominant factor in injury production. This has been especially notable in a number of minor crashes where vehicle occupants have been fatally injured, with the injury mechanism being attributed to adverse interaction with a deploying air bag. Based on the experience from in-depth investigations of real-world collisions, and an intensive crash test programme, a variety of countermeasures have been developed. While these have had a very positive effect, concerns remain over the level of public knowledge of air bag safety and, in particular, the precautions being taken by individuals who are at greatest risk. The current paper reviews a series of low severity crashes involving air bag induced fatalities that have been researched in detail. The resulting implications for the design and testing of future safety systems, their regulation, and the dissemination of relevant information to vehicle users are discussed.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".