Tinjauan Aspek Finansial Penggunaan Mobil Listrik dalam Upaya Mendukung Penurunan Emisi Gas CO2
Bibliographic record
Abstract
ABSTRACT The increase in the price of fuel oil (BBM) in the third quarter of 2022 will have a huge impact on the economy of the community. In addition to inflation, which was also followed by an increase in the price of basic commodities, this also reduced people's mobility in the use of oil-fueled vehicles. The government should use this as a momentum to promote the use of electric vehicles. On the other hand, the price of electric vehicles is still relatively high (expensive) when compared to oil-fueled vehicles. Although from a financial perspective, the use of electric vehicles can be more profitable if it is reviewed within a certain period. This can be shown by the lower cost per kilometer between electric vehicles compared to oil-fueled vehicles. From the point of view of cost comparison analysis over a certain period, the use of electric vehicles is more profitable than oil-fueled vehicles. It is hoped that the tendency of the community can be diverted to using electric vehicles. So that it can indirectly reduce the use of oil-fueled vehicles which is in line with efforts to reduce global warming.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.089 | 0.012 |
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".