Road Maintenance with Opti-Grade <sup>®</sup> : Maintaining Road Networks to Achieve the Best Value
Bibliographic record
Abstract
Road management systems rely on the availability of quality information to make good decisions. A lack of information on the condition of the Canadian forest industry’s unpaved road network led to inappropriate management decisions. To fill this information gap the Forest Engineering Research Institute of Canada (FERIC) developed the Opti-Grade road management system. Opti-Grade is a low-cost tool that provides information about the road roughness and travel speed as the equipped road user’s vehicle travels on the road network. This information can then be used to focus grading activities where they will have the greatest impact on the road condition for the money invested. Further, over time, a history of the behavior of the roads can be built. With this history, degradation models can quickly and easily be produced to see which segments of the road network degrade the quickest and the most frequently. Problem segments can be identified. Valuable road evaluation budgets can then be focused on those sections to determine the cause of the problem. That will allow precious rehabilitation budgets to be focused where they can have the greatest impact. Opti-Grade is currently used by a large sector of FERIC member forest companies with payback periods shorter than 4 months. FERIC continues to improve the software to manage the data from the Opti-Grade system and increase the abilities of the decision support tools in the software.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.041 | 0.015 |
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".