Evaluation of Existing Spring Load Restriction Models Based on Experimental Field Data
Bibliographic record
Abstract
A large number of road structures are subjected to seasonal freezing and thawing in Canada. Low volume (secondary) roads in frost-prone regions are susceptible to loss of structural capacity during thaw season. During freezing season, ice lenses form in the subgrade soil. Melting of these ice lenses during thawing season is deemed to be the main cause of losing structural capacity. There are several suggested empirical methods in the literature for predicting the onset and duration of thawing condition in the subgrade soil. Generally, transportation agencies use the outcome of these prediction models to apply limitations on maximum allowable loads of trucks passing on the vulnerable roads which is known as Spring Load Restriction (SLR) or Spring Road Ban (SRB). As part of an ongoing research on the Integrated Road Research Facility (IRRF)’s test road constructed in August 2012 which is a new access road to Edmonton Waste Management Center (EWMC), the temperature data were recorded over the winter of 2013 using thermometers installed across the depth of the pavement structure in order to monitor the temperature variation through different layers (Hot Mix Asphalt (HMA), Granular Base Course (GBC) and subgrade.) This research is a comparative study between 4 different methods to evaluate their credibility in determining the imposition date of SLR using the recorded field temperature data. It was found that all models were able to accurately predict the onset of thawing through the pavement.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| 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".