Deconstructing the Impact of Malaria Vector Diversity on Disease Risk
Bibliographic record
Abstract
AbstractRecent years have seen significant progress in understanding the impact of host community assemblage on disease risk, yet diversity in disease vectors has rarely been investigated. Using published malaria and mosquito surveys from Kenya, we analyzed the relationship between malaria prevalence and multiple axes of mosquito diversity: abundance, species richness, and composition. We found a net amplification of malaria prevalence by vector species richness, a result of a strong direct positive association between richness and prevalence alongside a weak indirect negative association between the two, mediated through mosquito community composition. One plausible explanation of these patterns is species niche complementarity, whereby less competent vector species contribute to disease transmission by filling spatial or temporal gaps in transmission left by dominant vectors. A greater understanding of vector community assemblage and function, as well as any interactions between host and vector biodiversity, could offer insights to both fundamental and applied ecology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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) | 0.000 | 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 teacher head, 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".