USE OF GENETICALLY MODIFIED MOSQUITOES TO FIGHT DENGUE IN BRAZIL
Bibliographic record
Abstract
Mosquito-borne diseases are one of the major barrie rs preventing economic progress in the developing w orld. According to the World Health Organization, 200 million people were victims of malaria in 2010 and 655 ,000, mostly children, died from it. Dengue fever is beli eved to affect 50-100 million people per year and r esults in around 20,000 deaths. Dengue is the most important mosquito-borne, human viral disease in many tropical and sub-tropical areas. In Brazil the disease has been essentially described in the form of case series.De spite the presence of dengue in Brazil since the early 1981s, dengue has become a major public health issue, wit h a high morbidity and mortality. Aedes aegypti and Aedes al bopictus are the vectors responsible for the transm ission of dengue viruses (DENV). The genetically modified (GM) mosquitoes being used in these field experiments and cited alternative methods for dengue control. This technology and its impact to the environment studie s have focused on controlling the mosquito populations by genetically modifying the insects. Tactics to prote ct people in endemic areas such as stopping mosquito bites us ing insecticides, net and repellents, developing pr eventive drugs and health education to manage mosquito-borne diseases have not shown full effectiveness.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".