Habitat suitability and ecological niche profiling of the West Nile Virus vector, Culex pipiens, in Forsyth County, NC
Bibliographic record
Abstract
Thought to have originated in Uganda in the late 1930's, West Nile Virus (WNV) was introduced in to North America in 1999 in New York City (Nash et. al, 2001). From its first occurrence within the United States, the virus spread across the contiguous forty-eight states and southern Canada in five years resulting in 18,000 human cases and over 700 fatalities (West Nile Virus, 2013). An important vector for the transmission of the WNV, Culex pipiens is widely distributed throughout the world with the exception of Australia and Antarctica (Farajollahi et al., 2011). This study seeks to utilize Remote Sensing, GIS, and Maximum Entropy (MaxEnt) Modeling in developing a presence-only habitat probability model of the known WNV bridge vector, Cx. Pipiens in Forsyth County, North Carolina by defining ecogeographical parameters that promote the mosquito species' larval development. Mosquito sampling was conducted in sixty-nine localities across the study area over a twenty-eight week period during the 2013 breeding season (April to October). Final habitat suitability maps produced as a result of this research will serve to guide future trap placement toward areas of high Cx. pipiens presence throughout the study area in an effort to optimize vector control measures and reduce the risk of WNV transmission. MaxEnt modeling results for the predicted probability of Cx. pipiens geographical distribution in Forsyth County highlighted the largest concentrations of Cx. Pipiens habitats within and along the periphery of the Winston-Salem municipality. Secondary areas of higher probability were located in the north central portion of the county, an area marked by irrigated cropland and deciduous forest.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".