Proposal of New Star Rating Bands for iRAP on Two-lane Rural Roads in Ecuador
Bibliographic record
Abstract
The iRAP model aims to reduce the number of road accidents or their severity by assigning a Star Rating (SR) to every 100 m of the road.The SR were derived from accident statistics and studies from several countries and organizations.However, these results may not be applicable to other countries or regions due to differences in the safety of vehicles and the behaviour of road users.This research proposes new iRAP Star Rating (SR) bands for two-lane rural roads in Ecuador.The study analysed more than 600 kilometres on the country's road network and estimated the SRS values every 100 m after collecting the road infrastructure attributes that could impact the likelihood of severity of a crash.The iRAP results were compared with the actual traffic accidents in the country, and, after statistical analysis, the study proposed new thresholds that better fit the Ecuadorian data.This process can be used by other countries to calibrate their own thresholds.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".