Development of Frost and Thaw Depth Predictors for Decision Making about Variable Load Restrictions
Bibliographic record
Abstract
Low-volume roads covering the northern part of Ontario, Canada, are a critical asset; they enable the movement of goods from remote resource areas to markets. However, challenged by a combination of heavy, low-frequency traffic loading and a high number of freeze-thaw cycles for which most have not been structurally designed, such highways often experience seasonal damage and premature traffic-induced deterioration. To mitigate these impacts, the Ontario Ministry of Transportation and other departments of transportation place seasonal load restrictions (SLRs) every year during the spring thaw. For economic reasons, the duration of SLRs is usually fixed in advance and is not applied according to conditions in a particular year. Rigidity in the schedule may result in economic losses because the payload can be unnecessarily restricted or pavement deterioration can occur. The latest attempts to address this issue include the use of climatic and deflection data to assess the bearing capacity of the roadway better. The use of frost and thaw depth predictors to track spring thaw weakening could improve the scheduling of load restrictions. On the basis of field data captured in Northern Ontario, a good correlation was found between the amount of frost depth in the pavement and weather conditions monitored by road weather information systems. An empirical methodology for site-specific calibration of the predictors is proposed, and the steps toward its development and the calculation algorithms are detailed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".