Evaluation of roads safety processes currently used in \nQueensland
Bibliographic record
Abstract
The aim of this project is to evaluate the road safety processes currently used in Queensland. This project focus on the study of the current road safety processes used in \nQueensland to eliminate fatalities and serious injuries and how effective these programs and initiatives have been in reducing the number of serious injuries and fatalities.The \nareas covered include; general road safety worldwide and in Australia; Road safety statistics in Queensland and analyses; Safety initiatives used in Queensland; Camera \nDetected Offence Program (CDOP); Black Spot Program (BSP); Heavy Vehicle Safety and Productivity Program (HVSPP);Toowoomba Regional Council Road Safety Initiatives;. \n Older Driver Safety Programs and Initiatives; Young Driver Road Safety Programs and initiatives; Motorcycle Safety Initiatives; Anti Drink Driving Safety Initiatives; Older \ndriver (OD) safety initiatives ; Designated Driver Program;School Road Safety Initiatives; Australia wide Road Safety Statistics;overview of Canada and England road fatality rates; Global Road Safety; 5E’s of road safety and Geometric Design .
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 | 0.020 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| 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".