Sustainable urban pavement management framework: A Chilean case study
Bibliographic record
Abstract
Urban pavements in Chile currently present a low level of service, which result in negative externalities to the population and high economic costs to the country. This problem is explained by two main causes: (1) there are several agencies involved in the management and funding of the urban pavement management; and (2) there currently is not a sustainable management system available. The main objective of the present study is to develop an Urban Pavement Management Framework that combines all competing factors into a long-term analysis approach. These key factors include: institutional, political, technical, economic, geographical, social and environmental. The study presented is part of a three-year project being developed in partnership with the University of Waterloo, Canada and the Pontificia Universidad Católica de Chile, Chile. This project is funded by the Chilean Government thought Fondef - Conicyt, the PUC, and the associated institutions: Ministry of Housing and Urban Development (MINVU), Regional Government for Metropolitan Region (GORE), Municipality of Santiago and Municipality of Macul. This paper presents the framework for the development of a practical tool for institution in charge of urban pavement management. The framework proposes a sustainable approach for all management levels. This project focuses on network level development, such as the design of technical evaluation of pavements condition, development of performance models, definition of maintenance standards, economic optimization using a cost-effectiveness method, implementation of a Geographical Information System and prioritization through multicriteria analysis.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".