Expansion of the Concrete Pavement Network in Quebec with the Update of the Departmental Policy on Pavement Selection Type
Bibliographic record
Abstract
In 2001, the Quebec Ministry of Transportation (MTQ), in Canada, adopted a policy subdividing the existing pavement network into dedicated concrete and asphalt networks. Sixteen (16) different combinations yielded 32 pavement designs in asphalt and concrete that were compared in pairs over an analysis period of 50 years using a probabilistic Life Cycle Cost Analysis (LCCA) software. In parallel to the LCCA, a multiple criteria analysis was performed to account for a certain number of factors that are not easily quantifiable in monetary terms. The final selection criteria were applied to the Province’s pavement network and the result was mapped. After minor adjustments to ensure continuity as much as possible, the respective networks (“white” for concrete, “black” for asphalt and “gray” for further analysis) were officially established. The policy making this network allocation official was scheduled for review every 5 years. In 2010, the policy on pavement selection type has been updated based on the new knowledge related to pavement materials and work costs, as well as the environment. Taking into account the environmental issue has been the subject of a special effort to meet the MTQ’s requirements for the sustainable development. To take into account the environmental issue, a pavement Life Cycle Assessment (LCA) was also performed on the 16 cases: the results of the LCA were integrated as a factor in the multiple criteria analysis. The dedicated concrete network went from 799 km in 2001 to 1231 km in 2010.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".