Development and application of a vehicle safety rating score for public transport minibuses
Bibliographic record
Abstract
Minibuses are widely used for public transport, particularly in developing countries, yet their safety levels are often poor. This study identified a simple set of active and passive safety measures and 566 minibuses in the United Arab Emirates were inspected. Most vehicles were without seat belts or head restraints and had inadequate seat attachment. Low rates of active and passive safety features were recorded. The safety rating system assigned weightings to each of the variables in the survey, based on an assessment of their approximate relative risk. Applied to the benchmarking sample, safety rating scores (out of 50) ranged from below 10 points for the least safe vehicles to around 40 points for the best. Many vehicles inspected scored below 20 points. The safety rating score provided a practical assessment of the safety of the UAE minibus vehicle fleet and could be adapted to other vehicle types. The study outcomes are helping to both justify a new minibus safety standard in the UAE aiming to significantly reduce death and serious injury among the many passengers using this service, as well as to begin the process of removing the least safe vehicles from the fleet.
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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".