Assessing Climate Change Impact on Asphalt Binder Grade Selection and its Implications
Bibliographic record
Abstract
Anthropogenic climate change is having and will continue to have unpredictable effects on Canadian weather. Trends in average annual temperatures have been rapidly increasing over the last 50 years. The severe climatic variations in Canada are in line with global changes in climate occurring as a result of increased greenhouse gas concentrations in the atmosphere. Under the current CO2 emission scenarios, scientists predict the climate trends to further intensify in the near future. It is well known that asphalt binder is highly sensitive to climate factors. For this reason, reviewing asphalt binder grade is a vital step, and can help decelerate pavement deterioration. The objective of this study was to assess the change in asphalt binder grade for the future climate and to determine the influence of change in binder grade on the performance of pavements in Canada. To achieve this, the analysis was carried out in five phases. In the first phase, statistically downscaled climate change models were gathered from the Pacific Climate Impacts Consortium database. In the second phase, the temperature and precipitation data were extracted for the selected locations in southern Canada. In the third phase, the asphalt binder grade was determined for future climate data. In the fourth phase, the pavement materials, traffic, and structural data were collected from the Long-Term Pavement Performance database. Lastly, the pavement performance with the base binder and the upgraded binder were assessed using AASHTOware Mechanistic–Empirical Pavement Design. The results reemphasize the necessity of upgrading the asphalt binder grade in various provinces of Canada.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".