Assessing Spring Load Restrictions Using Climate Change and Mechanistic-Empirical Distress Models
Bibliographic record
Abstract
During the spring, as the average daily air temperature and declination of the sun increases, so does the temperature of the asphalt surface layer. As the increase in temperature travels through the surface layer and reaches the unbound aggregate base layer, the saturated base layer begins to thaw. For a flexible pavement with a fine-grained subgrade, the moisture in the base becomes trapped between the impermeable asphalt concrete layer and the frozen, fine-grained subgrade. The excess water requires a long period of time to properly drain. During this period, the subgrade becomes considerably softer and decreases in stiffness, and the roadway experiences an increase in damage and reduction in service life. The duration of thaw for a typical pavement structure depends on soil type, moisture and thermal properties, air temperature, solar radiation, drainage and the location of the site. Spring load restrictions are applied to protect weak pavement structures that would otherwise experience excessive deflections. Nearly 58 % of the Manitoba Provincial pavement network is subjected to spring load restrictions, and most of these roads consist of a thin flexible pavement or an asphalt surface treatment. This paper relates pavement deflection data from FWD testing to environmental indices such as the thaw index. Deflection data collected since 1990 on pavement sections and the LTPP site in Manitoba are used to establish
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".