Predicting current and future distribution of West Nile disease in Tunisia
Bibliographic record
Abstract
West Nile Disease (WND) is an emerging infectious vector borne disease. Culex pipiens is the most implicated mosquito species in the transmission of WNV in Tunisia. The spatial distribution of this disease has continued to expand in Tunisia since the first epidemic in 1997, while the existing knowledge of environmental factors triggering such events continues to be rather poor. Based on the geographical locations of human WND cases and using ecological factors as predictors, the MaxEnt model was developed to identify environmental factors influencing C. pipiens competence. Potential areas at high risk of WND occurrence under current and future climate background are determined. The key environmental factors affecting vector competence and WND occurrence were the minimum temperature of the coldest quarter and precipitation in the warmest and driest quarter. The risk prediction maps suggested that north-eastern, the eastern and southern coast and oasis areas of Tunisia are potential areas at high risk of WND. Identifying potential environmental factors that influence WND occurrence in Tunisia is the first step for the implementation of a statistically rigorous system for real-time alert and prediction of WND. The potential high risk of WND areas are distributed widely in Tunisia. The epidemiological surveillance system should be enhanced in these high risk regions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".