Does fluoride influence oviposition of Anopheles stephensi in stored water habitats in an urban setting?
Bibliographic record
Abstract
BACKGROUND: The physico-chemical characteristics of lentic aquatic habitats greatly influence mosquito species in selecting suitable oviposition sites; immature development, pupation and adult emergence, therefore are considerations for their preferred ecological niche. Correlating water quality parameters with mosquito breeding, as well as immature vector density, are useful for vector control operations in identifying and targeting potential breeding habitats. METHODS: A total of 40 known habitats of Anopheles stephensi, randomly selected based on a vector survey in parallel, were inspected for the physical and chemical nature of the aquatic environment. Water samples were collected four times during 2013, representing four seasons (i.e., ten habitats per season). The physico-chemical variables and mosquito breeding were statistically analysed to find their correlation with immature density of An. stephensi and also co-inhabitation with other mosquito species. RESULTS: Anopheles stephensi prefer water with low nitrite content and high phosphate content. Parameters such as total dissolved solids, electrical conductivity, total hardness, chloride, fluoride and sulfate had a positive correlation in habitats with any mosquito species breeding (p < 0.05) and also in habitats with An. stephensi alone breeding. Fluoride was observed to have a strong positive correlation with immature density of An. stephensi in both overhead tanks and wells. CONCLUSION: Knowledge of larval ecology of vector mosquitoes is a key factor in risk assessment and for implementing appropriate and sustainable vector control operations. The presence of fluoride in potential breeding habitats and a strong positive correlation with An. stephensi immature density is useful information, as fluoride can be considered an indicator/predictor of vector breeding. Effective larval source management can be focussed on specified habitats in vulnerable areas to reduce vector abundance and malaria transmission.
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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 | 0.000 | 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) | 0.002 | 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".