Impact of biogas digesters on wood utilisation and self-reported back pain for women living on rural Kenyan smallholder dairy farms
Bibliographic record
Abstract
Women living on rural Kenyan dairy farms spend significant amounts of time collecting wood for cooking. Biogas digesters, which generate biogas for cooking from the anaerobic decomposition of livestock manure, are an alternative fuel source. The objective of this study was to quantify the quality of life and health benefits of installing biogas digesters on rural Kenyan dairy farms with respect to wood utilisation. Women from 62 farms (31 biogas farms and 31 referent farms) participated in interviews to determine reliance on wood and the impact of biogas digesters on this reliance. Self-reported back pain, time spent collecting wood and money spent on wood were significantly lower (p < 0.01) for the biogas group, compared to referent farms. Multivariable linear regression showed that wood consumption increased by 2 lbs/day for each additional family member living on a farm. For an average family of three people, the addition of one cow was associated with increased wood consumption by 1.0 lb/day on biogas farms but by 4.4 lbs/day on referent farms (significant interaction variable - likely due to additional hot water for cleaning milk collection equipment). Biogas digesters represent a potentially important technology that can reduce reliance on wood fuel and improve health for Kenyan dairy farmers.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".