Study of Public Perception Toward End-of-Life Vehicles (ELV) Management in Indonesia
Bibliographic record
Abstract
An ELV is a vehicle that has reached the end of its service life or service due to age or because it is unable to be used due to a catastrophic accident and high repair costs. The current methods of destroying ELV vehicles are unregistered, disassembly, destruction, and disassembly. Each procedure must adhere to predetermined guidelines. The purpose of this study is to conduct a survey of dietary knowledge about end-of-life vehicles (ELVs) in Indonesia. As a result, the purpose of this research is to learn about ELV laws and their implementation in countries that have done so successfully, as well as to learn about public perception of ELV application in Indonesia. A literature search of ELV laws in neighboring countries was conducted, as well as a survey of 98 respondents in Jakarta, Bogor, Depok, Tangerang, and Bekasi. SPSS was used to analyze the survey results. The questions in this study were divided into four sections: respondents' backgrounds; knowledge of ELV; concerns about ELV; and ELV campaigns. The findings revealed that public awareness of the use of ELV was quite low. In general, it can be concluded that the application of ELV in Indonesia needs to be carefully studied before it is implemented in order for it to be accepted by the public. Additionally, more ELV-related campaigns are required to increase the knowledge and awareness of the Indonesian people.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".