Improving mosquito population predictions in the Greater Toronto Area using remote sensing imagery
Bibliographic record
Abstract
West Nile Virus (WNV) and St. Louis Encephalitis (SLE) are two of the most common mosquito-borne diseases in North America. WNV and SLE have sporadic spatial and temporal outbreaks, making their outbreaks difficult to predict. However, recent studies have found that mosquito abundance is correlated with WNV and SLE transmission, providing researchers with a starting point for the development of mosquito-borne disease forecasting systems. Mosquito populations are controlled by a variety of environmental variables, including humidity, temperature, vegetation, and available water habitat for breeding. Current mosquito population forecasting models heavily weigh precipitation and temperature inputs, as they are traditionally seen as the best estimates of available breeding space in a region of interest. Although rainfall data are easy to acquire, precipitation data may not actually be the best estimates of mosquito habitat, as water does not flow evenly across landscapes. Furthermore, precipitation data generally come at a spatial resolution of 800 m to 2,500 m, and while this resolution can help predict mosquito abundances on large spatial scales, it inhibits the estimation of mosquito populations in urban areas with granular landscape heterogeneity. To overcome these limitations, this research explores the use of multispectral imagery for predicting mosquito populations, specifically in the Greater Toronto Area. Multispectral imagery is an attractive data source for predicting mosquito abundance due to its consistent collection and comparatively high spatial resolution (e.g., 30 m for Landsat). We derive a monthly time series of standard spectral indices from multispectral imagery over the Greater Toronto Area from 2004 to 2011. We then explore how spectral indices perform as a predictor for combined Cx. restuans and Cx. pipiens mosquito populations, with the ultimate aim of using multispectral imagery to forecast mosquito-borne diseases in highly urbanized areas.
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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) | 0.001 | 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) | 0.001 | 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".