A multi-criteria model approach for identifying priorities in road maintenance in the province of Lampung, Indonesia
Bibliographic record
Abstract
The source of financing largely determines the implementation of road maintenance. Due to the limited funding capacity of the Regional Government, the performance of road maintenance cannot be handled throughout the provincial road network, so it is necessary to determine the priorities and types of maintenance that must be performed carefully and accurately following the conditions. Therefore, this article conducts a study to determine the priority scale in road maintenance in the province of Lampung (Indonesia), which is limited by the government's financial capacity to make comprehensive improvements through a multi-criteria analysis approach. The approach used is a survey method with purposive sampling, integrated with a multi-criteria analysis approach to find eigenvalues as a priority for improvement. There are at least eight groups with 238 respondents who provide input in determining the priority of road preservation in the province of Lampung. The results show that there are ten main parameter criteria to assess the implementation of road preservation in the Lampung province, including accessibility, social, regional development, economy, number of vehicles, security, congestion, road damage, road safety, and regional disparities. The results of the calculation of the multi-criteria analysis of the parameters found that the "road damage" parameter has the highest weight or eigenvalue. The following parameter that becomes the main consideration is the economic aspect and accessibility, with the second and third largest eigenvalues. The security parameter is a factor that is not considered because it is ranked the lowest.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".