Bibliographic record
Abstract
Various North American transportation agencies have implemented several preventive maintenance techniques to improve pavement performance and safety. The York Region, located northeast of Toronto, Ontario, Canada, has been resurfacing and remedying pavements with microsurfacing treatments to improve the pavement surface conditions, but without a good understanding of how the treatment affects road safety. With data made accessible by the region, a before-and-after study was done, with the goal of gaining an understanding of how microsurfacing and resurfacing treatments affect road safety. The study concludes that microsurfacing and resurfacing can have a positive safety effect, with crash reduction factors as high as 54%. However, those activities are sensitive to the influence of treatment year data (which may be an anomaly period) and average annual daily traffic per lane. Generally, the findings illustrate that microsurfacing has a positive safety effect on locations susceptible to a number of conditions: regular occurrence of wet or slick (not dry) road surface conditions, a trend toward severe crashes, frequent intersection-related crashes, and a high occurrence of rear-end crashes. Findings of this study have opened the door to additional research; integration of safety under the pavement umbrella seems so logical and yet has barely been explored. For now, the crash reduction factors derived from the study can be applied by the region of York and by other jurisdictions to make more sound decisions at the network level when selecting pavement maintenance treatments.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".