Pavement markings at the Ministère des Transports du Québec: Innovative Measures to Improve Environmental Performance
Bibliographic record
Abstract
The MinistA¨re des Transports du QuA©bec (Quebec Department of Transport – MTQ) is very conscious of the importance of reducing the environmental impacts of its activities, and has made sustainability a core component of its Mission. In that regard, road-marking is one of the areas of expertise where the MTQ has shown a high degree of cutting-edge leadership and proactivity in recent years. The road network under the jurisdiction of the MTQ includes over 90,000 km of road markings, which are essential to the safety of all road users. To ensure the continued visibility of road markings, the MTQ conducts an extensive annual repainting program, which calls for vast quantities of materials, including 3.8 million litres of paint and 2.8 million kg of glass microbeads, which are added to the paint during application in order to enhance the night-time visibility of the road markings. Considering the quantities of materials used, it was essential to implement specific measures to reduce their environmental impact. Innovative and cutting-edge measures have been put forward to reduce the release of harmful products without diminishing the quality of the road markings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".