Condition Performance Models for Network-Level Management of Unpaved Roads
Bibliographic record
Abstract
Unpaved roads may represent more than 80% of a country's road network. Given the socioeconomic importance of unpaved roads to the well-being and development of rural populations, agencies in charge of their management should maintain them in optimum condition. A good management system should consider the use of effective evaluations of road conditions and reliable condition performance models. Available performance models for unpaved roads estimate the progression of one distress type subject to variations of independent variables affecting their performance over time. These variables commonly require detailed evaluations of road materials, geometric design, and traffic, demanding considerable expense and limiting the application of the models to project-level management. The objective of this study is to develop condition performance models for network-level management of unpaved roads on the basis of probabilistic deterioration trends observed in the field. The scope is to design practical models that are applicable to different climatic conditions and various road types and that can be effectively used by agencies in developing countries. The condition of an unpaved road network in Chile was assessed during three evaluation periods by use of the unpaved condition index methodology. Finally, condition performance curves for gravel and earth roads were developed with Markov chains and Monte Carlo simulation by consideration of a 10-year analysis period and three different climates.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".