Exploring Variation of Maintenance Action and Its Impacts on Emission and Cost in Jakarta City
Bibliographic record
Abstract
Local government of Jakarta issued a bylaw on air pollution controls for mobile sources, stipulates that all private car owners must get their vehicles’ emission tested biennially. In consequence of non-compliance vehicle in emissions test, vehicle maintenance is essential. Vehicle owner should take an appropriate action in order to reduce pollutant level lower than standard. This study attempts to analyze economic aspect and emission level considerations come into making optimal choice of vehicle owner. First, we examine the influential factors of action taken in maintenance process of non-compliance vehicle on probability of vehicle to pass the second stage emissions test. Second, due to variation of actions taken in the maintenance process, we analyze impact on cost and explore components affects on variation of maintenance cost. Empirical analysis was done by using Inspection and Maintenance data in year 2000 which collected at several auto-mechanic shops in Jakarta city. The bivariate probit model was applied to examine impact of chosen alternatives on the probability to pass Hydrocarbon and Carbon Monoxide in second emission test. To examine variation of maintenance cost and explore components affects on cost, multilevel approach was applied. It is confirm that several actions may increase probability to pass emission test but on the same time reduce average cost. Other actions may simultaneously increase probability to pass emission test and also increase cost. It was also found some actions didn`t have impact on emission test but on the other hand rise maintenance cost. Furthermore, some actions reduce probability to pass emission test but increase maintenance cost. Vehicle owner need to properly select actions in order to maximize benefit and minimize cost.
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 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 | 0.000 |
| 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".