The Impact of Weather Conditions on Culex pipiens and Culex restuans (Diptera: Culicidae) Abundance: A Case Study in Peel Region
Bibliographic record
Abstract
Mosquito populations are sensitive to long-term variations in climate and short-term variations in weather. Mosquito abundance is a key determinant of outbreaks of mosquito-borne diseases, such as West Nile virus (WNV). In this work, the short-term impact of weather conditions (temperature and precipitation) on Culex pipiens L.-Culex restuans Theobald mosquito abundance in Peel Region, Ontario, Canada, was investigated using the 2002-2009 mosquito data collected from the WNV surveillance program managed by Ontario Ministry of Health and Long-Term Care and a gamma-generalized linear model. There was a clear association between weather conditions (temperature and precipitation) and mosquito abundance, which allowed the definition of threshold criteria for temperature and precipitation conditions for mosquito population growth. A predictive statistical model for mosquito population based on weather conditions was calibrated using real weather and mosquito surveillance data, and validated using a subset of surveillance data. Results showed that WNV vector abundance on any one day could be predicted with reasonable accuracy from relationships with mean degree-days >9 degrees C over the 11 preceding days, and precipitation 35 d previously. This finding provides optimism for the development of weather-generated forecasting for WNV risk that could be used in decision support systems for interventions such as mosquito control.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".