Expert Knowledge Elicitation to assess the ability of matrices to transmit African swine fever virus
Bibliographic record
Abstract
An Expert Knowledge Elicitation (EKE) was carried out regarding the possible contamination with African swine fever virus (ASFV) of products used as pig feed, their traded/imported volumes and their use on pig farms. In addition, the EKE also concerned empty vehicles returning to ASF-unaffected areas of the EU after delivering pigs to ASF-affected areas. The EKE was carried out by three independent groups of six to eight experts each. It was carried out in three steps: assessing the likelihood of contamination of a product at origin; assessing the likelihood of the contaminated product having enough viable virus to infect a pig (the infectious dose); and assessing the volume of trade or imports of each product from an affected area in either the EU or Eurasia which would be delivered to either a small-scale or large-scale pig farm. This report presents the results of the three elicitations. The results of the EKE have been used by the AHAW Panel in a pathway model to determine the likelihood of each product to introduce ASFV into non-affected areas of the EU based on relative risk ranking.
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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 | 0.044 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".