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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".