Evaluation of spring load restrictions and winter weight premium duration prediction methods in cold regions according to field data
Bibliographic record
Abstract
Road pavements in frost-susceptible regions are challenged by frost-thaw induced damages. Spring load restriction (SLR) is intended to reduce road distresses caused by heavily loaded truck traffic. Winter weight premiums (WWP) is applied during early freezing season by increasing the maximum allowable axle loads to reduce the economic difficulties that trucking industries undergo during thaw season. While there are various timelines for applying and removing SLR, this paper reviews three of the SLR prediction models along with a WWP computer simulation and verifies the calculations based on each method using two years of monitored field moisture and temperature data from the fully instrumented integrated road research facility (IRRF) test road in Edmonton, Alberta, Canada. The results revealed that two of the methods showed a significantly better performance in determining the start and end dates of SLR as opposed to the other method. Computer simulations obtained the pavement critical depth beyond which traffic loading did not affect the subgrade material in early freezing season, and collected data aided in estimating the start date of WWP application on the test road. The results were in good agreement with the recommendations suggested by authorities.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".