Effective Animal Health Disease Surveillance Using a Network-Enabled Approach
Bibliographic record
Abstract
There are many benefits that derive from real-time knowledge of the health status of the national livestock population. Effective animal disease surveillance is a requirement for countries that trade in live animals and their products in order to comply with the World Organization for Animal Health (OIE) guidelines. Rapid identification of introduced and emerging disease allows rapid response and mitigation of the economic consequences. Connections between animal and human disease caused by a common pathogen can be recognized and control measures implemented, thereby protecting public health and maintaining public confidence in the food supply. Production-limiting diseases can be monitored, and control programmes be evaluated with benefits accruing from decreased economic losses associated with disease as well as reducing the welfare concerns associated with diseased animals. Establishing a surveillance programme across a wide area with diverse ecosystems and political administrations as Canada is a complex challenge. When funding became available from a government programme to enable early detection of a bio-terrorist attack on livestock, the Canadian Animal Health Surveillance Network (CAHSN) became officially established. An existing web-based information platform that supports intelligence exchange, surveillance and response for public health issues in Canada was adapted to link the network animal health laboratories. A minimum data set was developed that facilitated sharing of results between participating laboratories and jurisdictions as the first step in creating the capacity for national disease trend analysis. In each of the network laboratories, similar quality assurance and bio-containment systems have been funded and supported, and diagnostic staff have been trained and certified on a suite of diagnostic tests for foreign animal diseases. This ensures that national standards are maintained throughout all of the diagnostic laboratories. This paper describes the genesis of CAHSN, its current capability and governance, and potential for future development.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".