Simplified Model to Predict Frost Penetration for Manitoba Soils
Bibliographic record
Abstract
In spring season, the top layers of pavement start to thaw while the bottom layers are still frozen. As a result, the moisture remains contained in the top layers and can not be drained. Consequently, pavement layers experience high strains therefore Spring Load Restrictions (SLR) are applied to protect pavement from early deterioration. In winter season, the winter load premium can not be allowed until frost penetration reaches a certain depth. Having a reliable model to predict the depth of frost penetration provides a time and cost effective alternative to field measurements. This paper introduces the analysis conducted to develop a simplified model to predict the frost penetration in Manitoba. The climatic and seasonal monitoring data for the Oak Lake test section, which was collected as part of the Long-Term Pavement Performance (LTPP) Program, was utilized for this purpose. The proposed frost penetration model was compared to the Northern Ontario frost penetration model and a good agreement was found between them.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".