Quebec’s Multi-Party Observatory on Zoonoses and Adaptation to Climate Change
Bibliographic record
Abstract
Climate change has been linked with the establishment and geographical expansion of zoonotic diseases, an example of which is the well-documented increase in human cases of Lyme disease in Quebec, Canada.As temperatures continue to increase in Quebec, it is anticipated that several zoonotic diseases will be affected.In response to the growing zoonotic issues facing public health authorities, Quebec's Multi-Party Observatory on Zoonoses and Adaptation to Climate Change (Observatoire multipartite québécois sur les zoonoses et l'adaptation aux changements climatiques) (the Observatory) was founded in 2015 as part of the Quebec government's Climate Change Action Plan (Plan d'action 2013-2020 sur les changements climatiques).The Observatory was designed to bring together agencies involved in formulating public policy and experts from the disciplines of human health, animal health and environmental sciences, in a manner similar to the innovative "One World, One Health" approach.The Observatory provides a platform for knowledge sharing and consensus building among representatives of public policy decision makers and scientists.Its main objectives are to anticipate and prioritize potential issues associated with zoonotic diseases in Quebec, in order to support applicable risk management and climate change adaptation.This article describes what the Observatory is, what it does and outlines its plans for the future.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 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".