Dengue Reduction through Vector Control
Bibliographic record
Abstract
Dengue fever is a disease transmitted by the mosquito aegypti. There is a secondary vector: Aedes albopictus with some epidemiological importance in the transmission of dengue. Pharmacological treatment for dengue is a palliative treatment for the disease and there is an absence of a universally accepted vaccine for the different clinical infections. In these circumstances, the interruption of the infection cycle is possible basically through the reduction of the Aedes aegypti, reducing its breeding sites or physically reducing its population through chemical or biological means. Traditional approaches to vector control are becoming less effective as a result of the combination of resistance to insecticides and the logistic complexity of covering increasingly large urban centers with the same number of health workers as in past decades. Experiences in different countries reflect the need to involve more actively families and communities in the reduction of breeding sites. Several innovations have been introduced using biological methods, physical control of sources, and involvement of families and schools in vector control. The possibility to scale up successful experiences requires a joint effort of governments and communities to tackle mosquito source reduction and add a multipurpose concept of domestic hygiene.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.013 |
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".