Kajian Potensi Energi Baru Terbarukan Untuk Mendukung Transisi Energi di Kota Samarinda
Bibliographic record
Abstract
The dependence on the use of non-renewable fossil energy in society certainly causes various problems, one of which is environmental problems in the form of increased carbon emissions. Fossil energy will also be increasingly limited in the future due to increased energy use, so a transition to new renewable energy is needed. Indonesia has considerable potential in New Renewable Energy, but the utilization and understanding of New Renewable Energy among the public remain low. The renewable energy was only 13,09% in 2023. This is due to Indonesia’s continued focus on fossil energy. Through this Service Program, it aims to socialize the potential and utilization of New Renewable Energy in Indonesia, especially in the samarinda area. The stages of this program include discussion and socialization of the potential amount of New Renewable Energy in Samarinda that can be utilized for energy needs among the community. This program provides understanding and awareness to the public about the importance of transitioning to New Renewable Energy and creating a commitment between the city government and the community regarding the transition to New Renewable Energy. This activity is expected to be the beginning of a change in the use of energy sources to create more environmentally friendly environmental conditions.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".