Temporally consistent predominance and distribution of secondary malaria vectors in the Anopheles community of the upper Zambezi floodplain
Bibliographic record
Abstract
Regional optimisation of malaria vector control approaches requires detailed understanding both of the species composition of Anopheles mosquito communities, and how they vary over spatial and temporal scales. Knowledge of vector community dynamics is particularly important in settings where ecohydrological conditions fluctuate seasonally and inter-annually, such as the Barotse floodplain of the upper Zambezi river. DNA barcoding of anopheline larvae sampled in the 2019 wet season revealed the predominance of secondary vector species, with An. coustani comprising > 80% of sampled larvae and distributed ubiquitously across all ecological zones. Extensive larval sampling, plus a smaller survey of adult mosquitoes, identified geographic clusters of primary vectors, but represented only 2% of anopheline larvae. Comparisons with larval surveys in 2017/2018 and a contemporaneous independent 5-year dataset from adult trapping corroborated this paucity of primary vectors across years, and the consistent numerical dominance of An. coustani and other secondary vectors in both dry and wet seasons, despite substantial inter-annual variation in hydrological conditions. This marked temporal consistency of spatial distribution and anopheline community composition presents an opportunity to target predominant secondary vectors outdoors. Larval source management should be considered, alongside prevalent indoor-based approaches, amongst a diversification of vector control approaches to more effectively combat residual malaria transmission.
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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 | 0.001 | 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".