Developing an Optimization Model to Manage Unpaved Roads
Bibliographic record
Abstract
While approximately two-thirds of the total centerline miles are unpaved in the state of Wyoming, there is no optimization program for managing these roads. Unlike paved roads, unpaved roads deteriorate from excellent to failed conditions in sometimes less than a year. This deterioration rate necessitates developing a novel methodology for managing them efficiently. When funding is limited, it is important to identify the best mix of road preservation projects that provides the most benefits to society in terms of overall life cycle cost of the road network. This research intends to develop a management system using optimization techniques for managing unpaved roads within limited budget. The common factors that play the most important role for identifying projects are road condition parameters, unpaved road deterioration model, treatment types, cost-factors associated with selecting treatment types, traffic counts, budget, and treatment cost. Road condition parameters include cross section, roadside drainage, rutting, potholes, loose aggregate, dust, corrugation, and ride quality. This methodology will facilitate a statewide implementation of unpaved road management system for counties in Wyoming. The methodology can be easily adopted by other states interested in the management of gravel roads.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".