Analysis of biogas production potential based on livestock dung availability: A case of household biogas plants in Nepal
Bibliographic record
Abstract
This paper presents an analysis of the potential for household-scale biogas production based on cattle and buffalo dung in three geographical regions of Nepal. A field survey was conducted in 240 livestock-holding households, and data of daily fresh dung yield were obtained from 210 livestock individuals classified into four categories: mature buffalo (>3 year), young buffalo (≤3 year), mature cattle (>3 year) and young cattle (≤3 year). The data were collected in three different seasons with varying temperature and humidity. The energy values of the dung were experimentally measured. The results showed that the average daily dung yield per livestock in the monsoon was higher than that of the other seasons, for all geographical regions, because of higher fodder availability. Despite the highest livestock number per household in the mountains as compared to the hills and lowlands, the net availability of dung in the mountains was only about 30% of that of other two regions due to lower availability of fodder. Fodder availability, livestock herding hours, quality of dung and seasonal variations were found to be important parameters in determining the net potential biogas production. Based on these findings, measures to increase biogas production from available dung are discussed and generalized.
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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 | 0.000 | 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) | 0.001 | 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".