Mosquito‐Borne Diseases in Canada: Integrated Perspectives on Disease Management and Influences of Environmental and Anthropogenic Factors Affecting the Transmission Cycle
Bibliographic record
Abstract
ABSTRACT Globally, mosquito‐borne diseases (MBD) cause the highest morbidity and mortality in humans and animals. Currently, in Canada, endemic MBDs that are significant public health problems are all zoonoses and are caused primarily by West Nile virus, Eastern equine encephalitis virus, and Californian serogroup viruses, including the Jamestown Canyon and the Snowshoe hare viruses. The transmission cycles of these viruses are changing, linked to global population movements (including vectors) and climate and land use changes. Here, we present the state of knowledge, related to MBDs in Canada, as well as salient surveillance approaches carried out to monitor them and their infection rates. We propose a few theoretical and operational research avenues in order to improve our understanding of transmission cycle changes, as well as the potential of new surveillance tools such as citizen science, metagenomics, artificial intelligence, and remote sensing to help reduce disease burdens on Canadians. This will support public and animal health responses to these zoonoses and help proactively manage such diseases under changing environmental conditions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".