Global Warming Potential from Public Transportation Activities During COVID-19 Pandemic in Jakarta, Indonesia
Bibliographic record
Abstract
During a pandemic, social distancing will affect the occupancy rate of public transportation in DKI Jakarta. The number of usually total passengers is partially occupied. Of course, this can change the carbon footprint generated for each person. For this reason, this research was conducted to determine the carbon footprint and greenhouse gas emissions released during the COVID-19 pandemic. A direct survey has been conducted to determine the occupancy rate of mass rapid transit (MRT) vehicles and Trans Jakarta buses. Online vehicles such as cars and motorbikes were based on government policy. The results show that the MRT occupancy rate was 63±32 passengers, and for Trans Jakarta, it was 21±9 passengers. The carbon footprint from transportation that produces the most negligible CO2 emissions was MRT. The comparison obtained between the MRT and Trans Jakarta Bus's emission values were 0.026 and 0.091 kg CO2 eq/passenger. As for the online taxi transportation mode with four people, it produced the highest CO2 emissions. Therefore, the government needs have planned MRT to improve the quality of public transportation and capacity, especially in the main corridors of DKI Jakarta.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".