Large-Scale Deployment of Lehmann’s Funnel Entry Traps to Control Malaria Mosquito Populations
Bibliographic record
Abstract
Effective tools to prevent mosquito bites are essential for malaria control. The Lehmann Funnel Entry Trap (LFET), a window screen proven effective in reducing mosquito density, was tested for its large-scale impact on malaria vector control and community acceptance. A total of 1313 traps were deployed in Vallée du Kou 3 (VK3), with 12 traps randomly selected for detailed evaluation against untrapped houses in Vallée du Kou 5 (VK5). Traps were placed in windows with doors blocked by curtains. From July to October, mosquitoes were collected for nine days per month from VK3 traps and VK5 control houses. Morphological identification, density analysis, resistance gene screening, and female age structure determination were conducted. The trap's impact was assessed via pyrethrum spray catch (PSC) and for nine days per month, while trap fabric integrity and community perceptions were also evaluated. Traps reduced mosquito entry density by more than 90% in VK3 houses. VK3 had 33% higher house mosquito density pre-intervention and 47% lower house mosquito density post-intervention than VK5. Old female mosquito numbers rose in VK5 but not VK3. Pyrethroid resistance was high (kdr mutation frequency > 0.9) in both control and intervention sites. VK3 residents appreciated the traps for reducing bites and improving sleep. The LFET effectively controls malaria vectors and is well-suited for widespread use in malaria elimination efforts.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".