Land use change alters malaria transmission parameters by modifying temperature in a highland area of Uganda
Bibliographic record
Abstract
As highland regions of Africa historically have been considered free of malaria, recent epidemics in these areas have raised concerns that high elevation malaria transmission may be increasing. Hypotheses about the reasons for this include changes in climate, land use and demographic patterns. We investigated the effect of land use change on malaria transmission in the south-western highlands of Uganda. From December 1997 to July 1998, we compared mosquito density, biting rates, sporozoite rates and entomological inoculation rates between 8 villages located along natural papyrus swamps and 8 villages located along swamps that have been drained and cultivated. Since vegetation changes affect evapotranspiration patterns and, thus, local climate, we also investigated differences in temperature, humidity and saturation deficit between natural and cultivated swamps. We found that on average all malaria indices were higher near cultivated swamps, although differences between cultivated and natural swamps were not statistically significant. However, maximum and minimum temperature were significantly higher in communities bordering cultivated swamps. In multivariate analysis using a generalized estimating equation approach to Poisson regression, the average minimum temperature of a village was significantly associated with the number of Anopheles gambiae s.l. per house after adjustment for potential confounding variables. It appears that replacement of natural swamp vegetation with agricultural crops has led to increased temperatures, which may be responsible for elevated malaria transmission risk in cultivated areas.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".