Socioeconomic and Ecological Factors Influencing Aedes aegypti Prevalence, Abundance, and Distribution in Dhaka, Bangladesh
Bibliographic record
Abstract
This study examined household risk factors and prevalence, abundance, and distribution of immature Aedes aegypti and Aedes albopictus, and their association with socioeconomic and ecological factors at urban zonal and household levels in the city of Dhaka, Bangladesh. During the 2011 monsoon, 826 households in 12 randomly selected administrative wards were surveyed for vector mosquitoes. Results revealed that the abundance and distribution of immature Ae. aegypti and Ae. albopictus, and pupae-per-person indices did not vary significantly among the zones with varied socioeconomic status. Of 35 different types of identified wet containers, 30 were infested, and among the 23 pupae-positive container types, nine were defined as the "most productive" for pupae including: disposable plastic containers (12.2% of 550), sealable plastic barrels (12.0%), tires (10.4%), abandoned plastic buckets (9.6%), flower tub and trays (8.5%), refrigerator trays (6.5%), plastic bottles (6.4%), clay pots (4.9%), and water tanks (1.6%). When the function of the containers was assessed, ornamental, discarded, and household repairing and reconstruction-related container categories were found significantly associated with the number of pupae in the households. The purpose of storing water and income variables were significant predictors of possession of containers that were infested by vector mosquitoes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".