Updates to Hourly Climate Data for Use in AASHTOWare Pavement Mechanistic–Empirical Design
Bibliographic record
Abstract
The AASHTOWare Pavement Mechanistic–Empirical Design software requires hourly temperature, wind speed, percentage sunshine, precipitation, and relative humidity to properly calculate pavement damage and distresses. Actual or measured values, which vary hourly throughout a day for a given site, are required to properly capture the damage caused by environmental loadings. Currently the mechanistic–empirical design hourly climatic data contain approximately 1,200 U.S. and 300 Canadian stations. The U.S. stations typically contain data from 1995 through 2005, and data from the Canadian stations vary in length from 10 to 50 years, with the exception of some weather stations. Some agencies expanded their historical weather data to include longer periods of time. This paper documents the process and data sources that were used to update the current set of climate stations with climate data dating back to 1979 using the North American Regional Reanalysis (NARR) database. The results of the comparison between new climate files and the existing older climate data files for use in pavement design are presented. Overall, the NARR-generated climate data showed a very good comparison. The paper details the background of the NARR and its limitations and compares the performance predictions made by using the old and new climate data. The results indicate there is no systematic bias between the two climate data sets.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.008 |
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".