Modelling factors that affect the presence of larval mosquitoes (Diptera: Culicidae) in stormwater drainage systems to improve the efficacy of control programmes
Bibliographic record
Abstract
Abstract Stormwater catch basins form part of artificial drainage systems in urban areas and can provide larval habitat for mosquito vector species of West Nile virus (WNv), such asCulex pipiensLinnaeus (Diptera: Culicidae). We evaluated the impact of management techniques and targeted applications of larvicide on larval populations of this potential WNv mosquito vector species in catch basins from the Lower Mainland of Vancouver and on Vancouver Island of British Columbia, Canada. A mixed effects logistic regression model described the relationship between larval presence and larvicide treatment while controlling for other parameters. Parameter estimates showed that larvicide treatment reduced the odds of larvae presence by a factor of ∼7.23. The model also revealed relationships between larval presence and water temperature and adjacent land use but larvicide treatment consistently reduced the presence of larvae regardless of these other factors. This knowledge can now be used to prioritise and target control efforts to most efficiently reduce WNv mosquito vector populations, and most effectively reduce the risk of WNv transmission to humans. A similar research strategy could be applied to emerging threats from other potential mosquito vectors of disease around the world, to help lower the incidence of mosquito-borne disease.
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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 | 0.003 |
| 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 | 0.001 | 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".