Mapping of stakeholders in avian influenza surveillance in Canada
Bibliographic record
Abstract
INTRODUCTION: Highly pathogenic avian influenza viruses are highly transmissible and lethal in wild and domestic birds and can infect other mammals. Effective avian influenza surveillance and response requires coordinated, cross-sectoral efforts involving many organizations and individuals. A detailed understanding of who is involved and their role in surveillance and response is necessary for optimizing efforts. However, a comprehensive map of stakeholders and their roles in AI surveillance and response is currently lacking in Canada. PURPOSE: The aim of this study was to identify stakeholders and their roles in avian influenza surveillance to support effective surveillance and response in Canada. This map supplements existing information, including the Canadian Animal Health Surveillance System Poultry Surveillance Stakeholder Map, by comprehensively mapping specific sectors and organizations involved in avian influenza surveillance. FINDINGS: The final stakeholder list included 234 stakeholders involved in avian influenza surveillance (7 international, 60 national, 167 provincial/territorial). Stakeholders could have one role, multiple roles, or be involved in all steps of the surveillance cycle. The most common AI surveillance role was action and dissemination of information (n=141; 60.3%). There were 66 stakeholders (28.2%) involved in all steps of the surveillance cycle. SIGNIFICANCE: This process identified and characterized stakeholders involved in surveillance and response to avian influenza outbreaks in Canada, improving awareness amongst stakeholders of who is involved and what their roles are. This map is intended to facilitate proactive communication and collaboration with the long-term goal of mitigating the impact of highly pathogenic avian influenza outbreaks in Canada.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".