Changes in European wild rabbit population dynamics and the epidemiology of rabbit haemorrhagic disease in response to artificially increased viral transmission
Bibliographic record
Abstract
European wild rabbit (Oryctolagus cuniculus) populations are severely affected by rabbit haemorrhagic disease (RHD), currently aggravated by the spread of the new lagovirus serotype RHDV2 that replaced the classical RHDV strains (RHDV/RHDVa). This virus causes high mortality in both adult and young rabbits and to date, there is no management tool to effectively reduce its impact on wild rabbit populations. This hinders the success of common strategies, such as habitat management or restocking, in areas where rabbits are native. However, the present study, conducted on enclosed wild rabbit populations, showed that spreading RHDV2 on baits during breeding periods induced infection of young rabbits, reducing mortality rates, presumably due to maternal antibody protection. This reduced the young rabbit mortality hazard by a third, and more juvenile rabbits immune to RHDV2 were recruited into the adult breeding population. Young rabbits from populations in which the force of infection of RHDV2 was increased, however, exhibited considerably higher susceptibility to infection by RHDV than those from non-treated control populations. Since co-circulation of classical RHDVs was ruled out, differences in the type and degree of immunization, the level of cross-protection and/or other unknown factors, such as the circulation of undetected non-pathogenic lagoviruses, arose as possible explanations. This meant that although the present study demonstrated the possibility of successfully modulating the impact of RHD in wild populations, the epidemiological complexity of the situation where several lagoviruses circulate requires additional research to determine final applicability of the proposed method.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".