A scenario model to support freedom from African swine fever virus in Western Canada populated with data from Canada West Swine Health Intelligence Network and CanSpotASF
Bibliographic record
Abstract
African swine fewer (ASF) is a serious disease present in Africa, Eurasia, and the Caribbean but not in continental North America. CanSpotASF describes the ASF surveillance in Canada. It enhanced the passive surveillance by adding new surveillance components one by one. The first enhancement was "the risk-based early detection testing (rule-out testing)" where cases with eligible conditions were tested for ASF virus (ASFv). The rule-out testing started in 2020 and we have over 3 years data in the Canada West Swine Health Intelligence Network (CWSHIN) data repository on ASFv rule-out testing. The objectives were to develop and assess a scenario tree model (STM) for ASFv freedom to support the claim that commercial swine in Western Canada were free from ASFv; to assess if the CWSHIN repository data may be useful in the STM; and to assess if the rule-out testing had contributed to more evidence of freedom from ASFv than we would have had with passive surveillance alone. To compare and assess different scenarios we used the model's ability to accumulate evidence of freedom from the July quarter in 2020 to the July quarter in 2023 (13 quarters). We concluded that a method to assess the ability of a scenario and model to accumulate evidence of freedom, based on increasing probability of freedom that approached an equilibrium and a system sensitivity of at least 0.2-0.3, was useful. The CWSHIN data repository was critical to populate the model because it included both number of clinical assessments by herd veterinarians (from quarterly surveys); routine diagnostic data (pathology examinations) and ASF testing results from laboratories. The take home message was that the probability that a herd veterinarian will report a suspicion of ASF to the Canadian Food Inspection Agency (CFIA) (seVet) affected the outcomes of the STM. If the probability that a herd veterinarian reports to CFIA is low (seVet=0.01), then pathology examination and CanSpotASF will improve the STM outcomes. If the probability that a herd veterinarian reports to CFIA is high (seVet is 0.7 or higher), then there is no need for additional pathology and CanSpotASF testing. We expect the actual probability of reporting and subsequent value of additional pathology and testing to between these two scenarios.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".