THE EFFECTS OF CLIMATE CHANGE ON THE PERFORMANCE OF ASPHALTIC PAVEMENT STRUCTURES
Bibliographic record
Abstract
An integrated climate-pavement model is presented to predict service performance. A total of 144 scenarios were considered from a combination of different pavement shoulder conditions (0.0 m; 0.5 m; 1.0 m; 1.5 m), water depth levels (3.0 m; 3.5 m; 4.0 m; 4.5 m), varied thicknesses of asphalt surface courses (2.0 cm; 5.0 cm; 10.0 cm), and three rainy periods (above average, below average, average), applicable to the Metropolitan Region of Fortaleza (MRF). In addition, it was considered the MRF climate change predictions for the 21st century (2031-2070) using 5 climate change models based on the CMIP5 (Coupled Model Intercomparison Project) RCP (Representative Concentration Pathways) scenarios 8.5 and 4.5. Without taking into account the climatic variability the performance analysis estimated a structural service life ranging from 1.0 to 5.6 years less, indicating that without the integrated methodology, the project is likely to be undersized. For MRF, the climate change models show that the average rainfall should decrease and years of above-average rainfall will be less frequent. In general, it was observed that the integrated methodology provides gains in the development of infrastructure projects and allows to predict how climate change scenarios may affect these projects.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".