Bibliographic record
Abstract
This paper highlights the concerns about younger drivers in Perth.Evidence suggests that this group of drivers were speeding at different levels particularly the highly excessive.Despite 60 km/h being considered a higher speed limit than the other two limits under this study, it was found that younger drivers are speeding and taking higher risks on roads that belong to the 40 and 50 km/h speed limits.The 'On the spot' detection found roads, which belong to 50 km/h, were also of concern.Male drivers were dominating the speeding on roads for the three speed limits roads studied.Pedestrian crash data also supported this evidence in concluding that the leading number of drivers who hit pedestrians belongs to this younger age group.Speeding and pedestrian crashes on 40 km/h on non-school zone roads are also examined and discussed.It is recommended that along with enforcement, two levels policy need to be targeted in parallel.First, a safety audit to the 50 and 60 km/h limits as a comprehensive municipal programme that may lower the limits to a safer speed on these roads needs to be adopted.Second, authorities may focus on younger drivers' licence regulations in terms of speeding violations and accidents history and real scenario training that relates to that age group.Such policy direction may bring high returns in sustaining safer roads.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".