Land use change, fuel use and respiratory health in Uganda
Bibliographic record
Abstract
This paper examines how biomass supply and consumption are affected by land use change in Uganda. We find that between 2007 and 2012 there was a 22% reduction in fuelwood sourced from proximate forests, and an 18% increase in fuelwood sourced from fallows and other areas with lower biomass availability and quality. We estimate a series of panel regression models and find that deforestation has a negative effect on total fuel consumed. We also find that access to forests, whether through ownership or proximity, plays a large role in determining fuel use. We then explore whether patterns of biomass fuel consumption are related to the incidence of acute respiratory infection using a cross-sectional data set of 1209 women and 598 children. We find a positive and significant relationship between ARI and the quantity of fuelwood from non-forest areas; a 100 kilogram increase in fuelwood sourced from a non-forest area results in a 2.4% increase in the incidence of ARI for children. We find the inverse effect of increased reliance on crop residues. As deforestation reduces the availability of high quality fuelwood, rural households may experience higher incidence of health problems associated with exposure to biomass burning.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".