Strengthening a One Health approach to emerging zoonoses
Bibliographic record
Abstract
Given the enormous global impact of the COVID-19 pandemic, outbreaks of highly pathogenic avian influenza in Canada, and manifold other zoonotic pathogen activity, there is a pressing need for a deeper understanding of the human-animal-environment interface and the intersecting biological, ecological, and societal factors contributing to the emergence, spread, and impact of zoonotic diseases. We aim to apply a One Health approach to pressing issues related to emerging zoonoses, and propose a functional framework of interconnected but distinct groups of recommendations around strategy and governance, technical leadership (operations), equity, education and research for a One Health approach and Action Plan for Canada. Change is desperately needed, beginning by reorienting our approach to health and recalibrating our perspectives to restore balance with the natural world in a rapid and sustainable fashion. In Canada, a major paradigm shift in how we think about health is required. All of society must recognize the intrinsic value of all living species and the importance of the health of humans, other animals, and ecosystems to health for all.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.059 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.020 | 0.081 |
| Scholarly communication | 0.026 | 0.019 |
| Open science | 0.007 | 0.028 |
| Research integrity | 0.017 | 0.033 |
| Insufficient payload (model declined to judge) | 0.009 | 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".