Penentuan Beban Emisi Karbon Dioksida PLTU Batubara Pulau Jawa dari Hasil Pengukuran CEMS
Bibliographic record
Abstract
Climate change is a real phenomenon that occurs and is felt by all living things that live on earth. The increase in earth's surface temperature is one of the impacts of climate change that continues to occur and has the potential to threaten the sustainability of human life. Carbon dioxide is the main greenhouse gas that exacerbates this condition. One of the largest sources of carbon dioxide emissions comes from the energy sector, namely coal-fired power plants (CFPP). Java Island has CFPP’s with the largest total installed capacity in Indonesia, even the capacity will continue to be added by 8,5 GW or 39,4% until 2030. In its operation, the CFPPs have an air emission measuring device before being discharged into the atmosphere which works continuously called the Continuous Emission Monitoring System (CEMS). To be able to take appropriate climate change mitigation and adaptation steps, CO2 emission load data that has high accuracy and is analyzed directly is needed. The CO2 emission load generated from 20 CFPP units as the object of research is 88,56 million tons of CO2/year. The greater the generating capacity, the greater the CO2 emissions produced. The higher the quality of coal used, the lower the CO2 emissions tend to be. To support global efforts to combat climate change, mitigation actions are needed to reduce CO2 emissions into the atmosphere.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.073 | 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".