Aurora SPR-3(042), Phase 1: Review of Seasonal Weight Restriction Models for Comparison and Demonstration Project
Bibliographic record
Abstract
Although transportation agencies often focus on large interstate highways, there are several million miles of low-volume roads in the United States and Canada, and many of them are located in seasonal frost areas. In these locations, many agencies take advantage of the period of higher strength in mid-winter (when interstitial moisture beneath the pavement is frozen) by applying winter weight premiums (WWPs), increasing the allowable weight that trucks can haul. On the other hand, to reduce potential damage during the spring thaw, road management agencies apply Spring Load Restrictions (SLRs), which restrict the allowable load on the road during the critical time interval when the pavement is most vulnerable. There are a wide variety of techniques used by transportation agencies for timing of WWPs and SLRs. A pooled-fund study has recently been initiated by Aurora, a consortium of several States and Provinces, to provide an understanding of the reliability, benefits, costs and risks of alternate approaches to predicting the start and end dates of SLRs and WWPs. The study objectives will ultimately be met through a field demonstration in which a variety of WWP and SLR timing protocols and models are implemented at instrumented sites in up to five highway jurisdictions. The model predictions will be validated against observed subsurface temperature and moisture profiles and measurements of pavement deflection. This paper describes work conducted during Phase 1 of this Aurora project; specifically, providing an overview of the protocols and models available for WWP and SLR timing.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".