City-scale modelling of road thermal and hydrologic characteristics and failure mechanisms: Case study of Montreal
Bibliographic record
Abstract
• First city-scale modeling of Montreal's roads at 250 m super-resolution. • Reductions in freeze-thaw related distress anticipated for the cold season. • Heightened risks of rutting in a future warmer climate. • Increases in road and land runoff projected for both cold and warm seasons. • Underscores the necessity for reassessing the existing road drainage system. This study focuses on understanding and quantifying projected changes to road thermal and hydrologic characteristics that are associated with road failure, for the City of Montreal, Canada. To this end, city-scale modelling using a land surface model with realistic representation of urban regions, including roads, enabled by the super-resolution of 250 m, for the current 2001–2020 and future 2041–2060 periods are used. Analysis for the cold season suggests potential reduction in failures associated with thermal characteristics such as daily freeze-thaw and partial thaws, with a projected decrease of 12.5% and 2 %, respectively. Higher potential for hydrologic characteristics-related failures are likely, given projected increases in road/surface runoff in the 12–15% range. For the warm season, projected increases in thermal characteristics such as daily maximum and minimum temperatures (4–4.5 °C), and hydrological characteristics (25 %), suggest increased potential for road failure from rutting and drainage issues. Analysis performed separately for the highways and local roads reveal important spatial variability of the considered characteristics and associated failures, which can be helpful in prioritizing road sections for detailed analysis and in the development of sustainable adaptation strategies for the Montreal road network and enhancing the city's resilience to future climate change.
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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".