Climate change impacts on West Nile virus transmission in a global context
Bibliographic record
Abstract
West Nile virus (WNV), the most widely distributed virus of the encephalitic flaviviruses, is a vector-borne pathogen of global importance. The transmission cycle exists in rural and urban areas where the virus infects birds, humans, horses and other mammals. Multiple factors impact the transmission and distribution of WNV, related to the dynamics and interactions between pathogen, vector, vertebrate hosts and environment. Hence, among other drivers, weather conditions have direct and indirect influences on vector competence (the ability to acquire, maintain and transmit the virus), on the vector population dynamic and on the virus replication rate within the mosquito, which are mostly weather dependent. The importance of climatic factors (temperature, precipitation, relative humidity and winds) as drivers in WNV epidemiology is increasing under conditions of climate change. Indeed, recent changes in climatic conditions, particularly increased ambient temperature and fluctuations in rainfall amounts, contributed to the maintenance (endemization process) of WNV in various locations in southern Europe, western Asia, the eastern Mediterranean, the Canadian Prairies, parts of the USA and Australia. As predictions show that the current trends are expected to continue, for better preparedness, any assessment of future transmission of WNV should take into consideration the impacts of climate change.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".