Using LTPP Data to Develop Spring Load Restrictions: A Pilot Study
Bibliographic record
Abstract
In northern parts of North America, the road network is weakened and extensively damaged by the seasonal loading. The yearly cycle of above and below freezing temperatures causes cycles of freezing and thawing of the groundwater underneath the pavement. As the groundwater freezes, the pavement undergoes uplift, while as the temperature rises to above freezing, the frozen ground thaws. As the pavement thaws from the surface down, the soil eventually becomes saturated as the water becomes trapped between the pavement surface and the frozen soil. The pavement is in a weakened state during this saturated period, which can last up to several weeks every year. Most authorities have opted to impose load restrictions on vehicles during this thawing period. Yet, since the pavement temperature tends to lag behind the air temperature, it is difficult to determine the exact time duration that the pavement is in this weakened state. An accurate time for imposing the load restriction is required since delayed restrictions will cause pavement damage, whereas premature load restrictions will cause undue economic hardship on industries that require transportation of their goods. A promising and convenient method is the thawing index method developed by the Minnesota Road Research Section. This paper will investigate ways to improve the determination of the start of load restrictions based on weather information, and the possibility of adopting it for use in Ontario.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".