Projected Changes in Mid-Twenty-First-Century Extreme Maximum Pavement Temperature in Canada
Bibliographic record
Abstract
Abstract Future climate warming is virtually certain to bring about an increase in the frequency of heat extremes. Highway design and pavement selection are based on a temperature regime that reflects the local climate zone. Increasing heat extremes could, therefore, shift some areas into a different performance grade (PG) for pavement, and more-heat-resistant materials are associated with increased infrastructure costs. This study combines observations, output from global climate models, and a statistical model to investigate changes in 20-yr return values of extreme maximum pavement temperature TP max . From a multimodel range of simulated TP max , future changes in PG are computed for 17 major Canadian cities. Relative to a 1981–2000 baseline, summertime Canada-wide warming of 1°–3°C is projected for 2041–70. As a result, climate change is likely to bring about profound changes to the spatial distribution of PG, with the severity of the changes directly linked to the severity of the projected warming. Even under weak simulated warming, an increase in PG is projected for greater Toronto, which is Canada’s largest urban area; under moderate (strong) warming 7 of 17 (9 of 17) major cities exhibit an increase. The influence of model spatial resolution is evaluated by comparing the results from global climate models with output from a set of regional climate models focused on North America. With the exception of mountainous terrain in western Canada, spatial resolution is not a major determining factor for projections of future PG changes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".