Types of Community Energy Use and Potential for Developing Biofuel Plants in The Bonehau Watershed
Bibliographic record
Abstract
Population growth is increasing beyond previous estimates. The world's energy needs are also extensive and continue to grow. This puts pressure on the energy available in nature in the form of biomass, and the decreasing fossil energy. So, alternative energy sources are needed that can be renewed in the form of Biofuel Sources. In addition, the development of biofuel energy source plants is a solution to restore environmental conditions. Based on this, it is necessary to identify the types of energy sources and the potential for developing biofuel energy source plants in the Bonehau Watershed ecosystem. The method used is non-experimental mapping based on Geographic Information Systems. The initial stage of introducing the identification of types of energy use by the community based on accessibility density classes. Furthermore, an analysis of the potential for developing biofuel plants, namely Nyamplung and Kemiri Sunan, was carried out using a land suitability approach. The Bonehau Watershed community generally uses LPG and firewood energy to meet household energy needs. Most people use more firewood energy than LPG because the watershed area has low accessibility, and the availability of firewood around the community's residence is limited. Analysis of Biofuel Plant Land Suitability in the form of Nyamplung Plants has the potential of 25% and Kemiri Sunan Plants 34% of the watershed area to be developed. The potential for developing these plants can handle 59% of the critical land for Nyamplung plants and 74% for Kemiri Sunan plants, from the total critical land area
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".