iRAP road and design assessments and outcomes: a case study from Moldova
Bibliographic record
Abstract
This work, supported by the Millennium Challenge Corporation, assessed the safety of the road infrastructure of a 93km section of the M2-R7 in Moldova in 2010 and 2015, before and after rehabilitation. The iRAP Star Rating with a Safer Roads Investment Plan guided provision of more than 22km of footway (sidewalk), a doubling in the number of pedestrian crossings to more than 50, installation of 12.3km of safety barrier, improvements in the quality of curves, the overall quality of the road surface, delineation and enhancement in the quality of intersections. Prior to upgrading, the safety rating of the road for pedestrians was poor (84% of the road rated only 1- and 2-star) and, for vehicle occupants, the road was predominantly 1- and 2-star (87%). Since reconstruction, the Star Ratings have improved. The percentage of the road rating 3-star and above has increased by around 30 percentage points for pedestrians, cyclists, motorcyclists and vehicle occupants. The post-construction Road Safety Audit by AECOM includes recommendations for improvements at intersections, in villages, on roadsides and for some measures related to the route. The pre-construction EuroRAP investment proposal showed that, for an overall package of safety countermeasures, there would be a reduction of around 300 killed or seriously injured casualties over 20 years, with a Benefit Cost Ratio approaching 4, a saving of almost a quarter of casualties on the road had there not been upgrading.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".