Development of Climate Adaptation and Asphalt Selection Tool (CAAST)
Bibliographic record
Abstract
This paper introduces the Climate Adaptation and Asphalt Selection Tool (CAAST), which is a new computer software used to select climate resilient Performance Grade (PG) of asphalt binder for Superior Performing Asphalt Pavements (Superpave) based on extreme high and low pavement temperatures. Developed by National Research Council Canada (NRC), CAAST uses projected climate change temperature data provided by Environment and Climate Change Canada (ECCC) for Canadian cities, obtained from the Canadian Regional Climate Model (CanRCM), version 4, from 1950 to 2100. The CAAST software analyzes the projected temperature data file within a certain time period (i.e.: road design life) to extract and calculate environmental parameters that will be used to assess the impact of climate change and to select the climate resilient asphalt binder performance grade (PG). CAAST incorporated the shortcomings of the existing Long-Term Pavement Performance Bind Online (LTPPBind) such as standing traffic speed, besides slow and fast traffic speed and annual temperature variations in terms of Degree Days (change within a year not only during 6 months). The paper compares and analyzes Superpave PG results obtained from ECCC's projected temperature data via CAAST against the existing method (LTPPBind) based on Modern-Era Retrospective Analysis for Research and Applications (MERRA-2) temperature data. The study shows that the indices such as Mean Annual Lowest Air Temperature (MALAT) and Mean Annual Degree Days (MADD) for cities in Atlantic, Central, Prairies, Western and Northern Canada are significantly higher when using the new proposed method compared to MERRA-2. The analysis also suggests that climate change can induce higher upgrades in high and low PG grades over the next 25 years than those predicted by MERRA-2 via LTPPBind. Consequently, the use of CAAST with embedded projected air temperature data can provide an effective solution for selecting resilient PG binder types in response to climate change, which is an improvement over traditional approaches that rely on historical climate. Overall, this study highlights the importance of considering climate change projections in pavement design and emphasizes the need for tools like CAAST to ensure pavement performance under changing environmental conditions.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".