Annual report of the Scientific Network on Animal Health 2023
Bibliographic record
Abstract
According to its Founding Regulation (Regulation (EC) No 178/2002), the European Food Safety Authority (EFSA) was tasked to establish a system of networks of organisations operating in the fields within EFSA's remit, with the objective to facilitate a scientific cooperation framework by coordinating activities, exchanging information, developing and implementing joint projects, and exchanging expertise and best practices. The Scientific Network on Risk Assessment in Animal Health and Welfare (AHAW) aims to build a mutual understanding of risk assessment principles in the areas of animal health and welfare, to promote harmonisation of animal health and welfare risk assessment practices and methodologies, and to reduce the duplication of activities by identifying and sharing current and upcoming priorities. The network organises an annual meeting dedicated to animal health-related issues to discuss and exchange on all topics currently relevant and interesting to its member organisations. In 2023, this annual meeting took place on 21 and 22 September. Among all topics covered, special attention was paid to avian influenza and African swine fever, for which two network subgroups for data collection on those diseases were established in 2023, while other urgent issues (e.g. epizootic haemorrhagic disease) were brought to the table by the network. This report summarises the activities presented by members and observers of the network as well as EFSA's contributions during the meeting.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.031 | 0.023 |
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".