Informing resilience building: FAO’s Surveillance Evaluation Tool (SET) Biothreat Detection Module will help assess national capacities to detect agro-terrorism and agro-crime
Bibliographic record
Abstract
Attacks using animal pathogens can have devastating socioeconomic, public health and national security consequences. The livestock sector has some inherent vulnerabilities which put it at risk to the deliberate or accidental spread of disease. The growing concern of countries about the risks of agro-terrorism and agro-crime has led to efforts to prepare against potential attacks. One recent international effort is the launch of a joint OIE, FAO and INTERPOL project in 2019 to build resilience against agro-terrorism and agro-crime targeting animal health with the financial support of the Weapons Threat Reduction Programme of Global Affairs Canada. Given the importance of strong animal health surveillance systems for the early and effective response to agro-terrorism and agro-crime, the project will use the FAO Surveillance Evaluation Tool (SET) and its new Biothreat Detection Module to evaluate beneficiary countries' capacities to detect criminal or terrorist animal health events. This paper presents the development of the new SET Biothreat Detection Module and how it will be used to evaluate surveillance for agro-terrorism and agro-crime animal disease threats. The module will be piloted in early 2021 and, once finalized, will be used by beneficiary countries of the joint OIE-FAO-INTERPOL project. Results from evaluations using SET and its Biothreat Detection Module are expected to provide a baseline from which countries can build targeted capacity for animal disease surveillance including early detection and investigation of potential terrorist or criminal events involving zoonotic and non-zoonotic animal pathogens.
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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 | 0.023 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".