Bibliographic record
Abstract
This paper contains main points of the scientific opinion prepared by EFSA in 2018 on the request of the European Commission on the ASF epidemic in wild boar in Eastern – Central Europe, which started in 2014 and is existing and increasing until the present time. The first point is providing an estimate of the wild boar density in the European Union (EU). The next item is presenting the opinion on the latest epidemiological data to identify thresholds in wild boar density that do not allow sustaining the disease in different settings. The third question and answer concerns wild boar and feral pigs depopulation methods or population reduction methods. The fourth chapter is reviewing fencing methods demonstrating to temporarily protect crops from damage, caused by wild boar or feral swine. Currently there is no evidence , that large –scale fences have been effective for the containment of wild boar or feral pigs. The answer to the fifth question on surveillance strategy contains information on sample size, frequency of sampling and identification of possible risk groups for early detection of ASFV in naïve wild boar population. Passive surveillance is according to the opinion of EFSA the most effective and efficient method of surveillance for early detection of ASF in wild boar. For early detection through passive surveillance , the aim is to test as many “found dead” animals as possible. Based on current knowledge and experiences for an intervention to be successful, there is a need to detect an ASF incursion while it is spatially contained. The sixth topic of the EFSA scientific opinion requests to review successful methodologies used in the past, as for example the mentioned passive surveillance. It is recommended that this should be done in discussion among specialists from the UE countries. Good collaborations with hunters is recommended.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".