An ecotoxicological view on malaria vector control with ivermectin-treated cattle
Bibliographic record
Abstract
Abstract Malaria remains an enduring challenge in sub-Saharan Africa, affecting public health and development. Control measures can include the use of insecticides that target adult Anopheles mosquitoes transmitting the malaria-causing Plasmodium parasite. Such mosquitoes can also bite livestock, allowing vector populations to be maintained at levels that enable parasite transmission. Thus, one way to control the spread of malaria includes the use of endectocide-treated livestock which renders the blood of cattle toxic to such mosquito populations. Here we present an ecotoxicological perspective on malaria vector control, using cattle treated with the endectocide ivermectin to target zoophagic and opportunistic Anopheles coluzzii mosquitoes. Our study employs an innovative, long-acting injectable ivermectin formulation with over 6 months of sustained mosquitocidal activity. Robust vector population modelling underscores its promising field effectiveness. Environmental implications (soil sorption and dissipation) of excreted ivermectin and potential ecotoxicological risks to non-target dung organisms in West Africa are discussed, in addition to actionable, locally inspired risk mitigation measures to protect sub-Saharan soils and agroecosystems from chemical pollution. We highlight how ecotoxicology and environmental chemistry improve livestock-based vector control with ivermectin for effective and more sustainable malaria management.
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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 | 0.000 |
| 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 | 0.001 | 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".