The Ontario Animal Health Network: enhancing disease surveillance and information sharing through integrative data sharing and management
Bibliographic record
Abstract
The Ontario Animal Health Network (OAHN) is an innovative disease surveillance program created to enhance preparedness, early detection, and response to animal disease in Ontario. Laboratory data and, where available, abattoir condemnation data and clinical observations submitted by practicing veterinarians form the core of regular discussions of the species-sector networks. Each network is comprised of government veterinarians or specialists, epidemiologists, pathologists, university species specialists, industry stakeholders, and practicing veterinarians, as appropriate. Laboratorians provide data for diseases of interest as determined by the individual network, and network members provide analysis and context for the large volume of information. Networks assess data for disease trends and the emergence of new clinical syndromes, as well as generate information on the health and disease status for each sector in the province. Members assess data validity and quality, which may be limited by multiple factors. Interpretation of laboratory tests and antimicrobial resistance trends without available clinical histories can be challenging. Extrapolation of disease incidence or risk from laboratory submissions to broader species populations must be done with caution. Disease information is communicated in a variety of media to inform veterinary and agricultural sectors of regional disease risks. Through network engagement, information gaps have been addressed, such as educational initiatives to improve sample submissions and enhance diagnostic outcomes, and the development of applied network-driven research. These diverse network initiatives, developed after careful assessment of laboratory and other data, demonstrate that novel approaches to analysis and interpretation can result in a variety of disease risk mitigation actions.
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 | 0.075 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.007 | 0.021 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".