Managing road performance and transportation efficiency with the Opti-Grade system
Bibliographic record
Abstract
FERIC developed a performance based road maintenance management system that focuses maintenance on the road segments that need grading most, thereby making the most efficient use of graders, lowering grading costs, and improving the performance of the roads. Today, the system is used on a variety of operations to manage road maintenance, to facilitate research projects aimed at optimizing maintenance operations, to provide a decision support tool for managing road rehabilitation, and to provide a means of evaluating how the quality of the running surface affects trucking costs. FERIC worked with a company in western Canada (Alpac) to complete the first phase of a study to assess the impact of road roughness on haul costs. The study's main objective was to find the right balance between minimizing road maintenance costs and maximizing the efficiency of the transportation system. The potential benefits for the company were large, since according to Alpac, each reduction of 1 minute in cycle times translated into a saving of more than $75 000 for their fleet. This paper presents a summary of the potential offered by Opti-Grade as well as the results of our ongoing research.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".