Comparison of two methods for applying spring load restrictions on low volume roads
Bibliographic record
Abstract
Northern jurisdictions implement spring load restrictions (SLRs) on low volume roads to minimize damage during spring thaw. For this study, SLRs are applied when the thawing front depth reaches 300 mm. Spring load restrictions should be removed when the pavement structure is completely thawed and the stiffness is sufficiently restored. Thawing and freezing index and thermal numerical modelling-based SLR methods are compared. The methods were first calibrated using air temperatures and measured freezing and thawing depths from two instrumented pavement sections in northern Ontario. The calibrated methods were then tested by predicting SLR application and complete thawing for the two sites for spring 2010. Comparisons with measurements show that both methods are able to predict SLR application within several days for both sites, but were off in excess of one month for complete pavement thawing. This study indicates that a calibrated thawing-index based method is most conducive for regional scale application of SLRs.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".