Comparative Study on Charcoal Yield Produced by Traditional and Improved Kilns: A Case Study of Nyaruguru and Nyamagabe Districts in Southern Province of Rwanda
Bibliographic record
Abstract
Deforestation and shortage of wood are serious environmental issues in Southern Province of Rwanda. This is likely to happen due to inadequate strategies and capacity to produce and utilize wood for energy on a sustainable basis. Furthermore, the production of charcoal in rural areas is done through the earth mound kilns causing more pressure on forests due to increased demand of charcoal. The main purpose of this study was to compare the charcoal yield produced from improved and traditional methods. This study was conducted in Nyabimata sector of Nyaruguru District and Tare sector of Nyamagabe District in June and July 2012. Improved charcoal and traditional charcoal were produced in order to determine the best method to be used. Data were analyzed using the Gen Stat Discovery 4th Edition. The results revealed that improved techniques can increase the charcoal production and reduce the air pollution where one can obtain at least 3 bags of charcoal in 1 m3 of wood and 15 liters of tar collected from the chimney containing the major elements responsible for green house gases emission. The yields of charcoal obtained according to the weight of wood used were less in traditional earth mound kiln (T1) techniques with the percentage of 7.5% than what improved earth mound kiln (T2) and casamance kiln (T3) techniques produced with 19% and 20% respectively. Measures should be taken in order to increase the level of improved charcoal making adoption, such as encouraging people to invest in improved charcoal production, organizing the trainings to the charcoal makers and planting more trees.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".