Bibliographic record
Abstract
This study was carried out to benchmark the safety performance of Australia's road transport industry against the safety performance of similar industries in a range of OECD countries. Its purpose was to guide the development of future policies to improve the safety of the Australian road transport industry, and to provide a focus for the national heavy vehicle safety strategy. Comparisons were made of truck fatalities in Australia, the United States of America, Canada, New Zealand, the United Kingdom, France, Germany, and Sweden. Fatality rates were used because of the range of comparable data. Injury data were not used because injury reporting criteria and completeness of reporting vary within Australia and in other countries. The study found that Australia's heavy vehicle fatality rate per kilometre travelled is 47% higher than the USA, 39% higher than the UK, comparable to Germany and Canada, 20% lower than Sweden, 45% lower than France, and 55% lower than New Zealand. The higher fatality rates on Australian roads compared to Great Britain and the United States maybe largely explained by the lower proportion of truck travel on divided and limited access roads in Australia, and possibly truck speed limits. Areas that have the potential to improve the safety performance of the heavy vehicle industry include road standards, targeted low cost road safety treatments, single vehicle crashes, day and night time driving, measures to improve the safety of truck occupants, front and rear underrun protection, appropriate speed limits, and data collection.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".