Public consultation on the draft data section on the ability of ASFV to survive and remain viable in different matrices of the Scientific opinion on Risk assessment of African swine fever and the ability of products or materials to present a risk to transmit ASF virus
Bibliographic record
Abstract
EFSA has been tasked by the European Commission to review the evaluation of the ability of matrices, including vegetables, arable crops, hay and straw as well as sawdust, wood chips and similar materials likely to present a risk to transmit ASF (Mandate 2019-0020). This review should take into account a retrospective analysis of ASF spread mechanisms. A public consultation on the draft data section was held in order to collect feedback and to identify the completeness of the data on ASFV survival in the different categories of matrices identified in the literature review, to identify other studies on the survival of ASFV in these matrix categories exist that had not been considered and to gather knowledge about the production/ processing parameters that might affect ASFV survival. Stakeholders were also invited to suggest additional categories of matrices that should be considered by the AHAW Panel regarding the risk of transmitting ASFV to domestic pigs. The consultation ran from 3 to 28 February 2020. In total, 51 comments and 17 documents were received from 21 different stakeholders. This report lists all comments received and how they have been addressed in the scientific opinion.
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 | 0.113 | 0.239 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.018 | 0.010 |
| Insufficient payload (model declined to judge) | 0.061 | 0.021 |
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".