Comparison of Different Sampling Methods to Catch Lymphatic Filariasis Vectors in a Sudan Savannah Area of Mali
Bibliographic record
Abstract
There is a need for better tools to monitor the transmission of lymphatic filariasis and malaria in areas undergoing interventions to interrupt transmission. Therefore, mosquito collection methods other than human landing catch (HLC) are needed. This study aimed to compare the Ifakara tent trap type C (ITTC) and the Biogents sentinel trap (BGST) to the HLC in areas with different vector densities. Mosquitoes were collected in two villages in Mali from July to December in 2011 and 2012. The three methods were implemented at each site with one ITTC, one BGST, and one HLC unit that consisted of one room with two collectors-one indoor and the other outdoor. The Anopheles collected in 2011 were individually dissected, whereas those from 2012 were screened in pools using reverse transcription-polymerase chain reaction (RT-PCR) to determine the maximum infection prevalence likelihood (MIPL) for Wuchereria bancrofti and Plasmodium falciparum. The dissection of the females also allowed to assess the parity rates, as well its results. Over the 2 years, the HLC method collected 1,019 Anopheles, yields that were 34- and 1.5-fold higher than those with the BGST and ITTC, respectively. None of the dissected Anopheles were infected. The RT-PCR results showed comparable MIPL between HLC and ITTC for W. bancrofti with one infected pool from each trap's yield (respectively 0.03% [0.0009-0.2%] and 0.04% [0.001-0.2%]). For P. falciparum, no infected pool was recovered from BGST. The ITTC is a good alternative to HLC for xenomonitoring of program activities.
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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 | 0.002 |
| 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 | 0.000 | 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 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".