Evaluation of Vegetation Diversity and Emission Absorption Recommendations for Pandaan-Malang Toll Road (Segment Study 1-5)
Why this work is in the frame
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Bibliographic record
Abstract
Toll road operational activities also harm the environment, one of which is the release of motor vehicle emissions. One way to reduce this impact is through the application of vegetation along the toll road. This study aims to evaluate vegetation based on integrated ecological functions along the Pandaan-Malang toll road corridor. This research employs quantitative methods, beginning with field observations and secondary data collection on the number of vehicles, followed by quantitative analysis of emission absorption capacity, and culminating in the preparation of recommendation strategies for the Pandaan-Malang toll road segment 1-5. Based on the study's results, it was found that emissions from vehicle volume were 63,320,311.04 kilograms of CO2/ year, with a total absorption capacity of vegetation along the Pandaan-Malang toll road segment of 51,080,719.78 kilograms of CO2/year. There is a shortfall of 12,239,591.26 kilograms of CO2/ year or 19.3% of total annual emissions. This finding indicates that the capacity of existing vegetation remains insufficient to offset the emissions generated by passing motor vehicles. Strategies and recommendations that can address the lack of absorption include the addition of vegetation, such as trembesi, dea shoes, and pule, with the amount and placement tailored to their specific needs. This research demonstrates that vegetation not only enhances the environment's beauty but also plays a crucial role in absorbing carbon dioxide (CO2) emissions and other pollutants. Through this approach, it is expected that the toll road will not only serve as a transportation route but also function as an ecological corridor that supports the improvement of environmental quality and public health. Vegetation recommendations should also consider adaptation factors to the local conditions along the toll road, such as pollutant levels, light intensity, and soil quality. Therefore, future research can involve field trials of vegetation resilience and actual carbon sequestration data.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 it