Comparison between MERRA-2 and CWEEDS for use in pavement mechanistic-empirical design in Canada
Bibliographic record
Abstract
To improve the climate resiliency of existing and new pavements, it is important to carry out pavement designs using continuous climate records at high temporal frequencies. Over the years, significant research efforts have been dedicated to obtain high-quality climatic data for pavement design including the latest adoption of the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2). The purpose of this study is to assess how MERRA-2 performs when compared to the Canadian Weather Energy and Engineering Datasets (CWEEDS), which provides hourly meteorological data for many parts of the country from various periods. In the first part, climate parameters at nine locations were directly compared to determine the correlation between two data sets. In the second part, long-term performances were simulated for typical flexible pavement to assess the relative impact of each climate scenario. As detailed in this paper, observed differences between MERRA-2 and CWEEDS indicate the need for further improvement of climate data quality and availability for designing resilient pavements in Canada.
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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".