Evaluating the impact of rabbit hemorrhagic disease (RHDV2) on a feral population of naïve European rabbits ( <i>Oryctolagus cuniculus</i> )
Bibliographic record
Abstract
Diseases can cause rapid declines in wildlife populations, especially in populations with no previous exposure to the disease. An acute, mostly fatal emerging lagomorph disease, rabbit hemorrhagic disease (RHD), has recently been detected in North American hares and rabbits. In February 2018, feral European Rabbits ( Oryctolagus cuniculus (Linnaeus, 1758)) were found dead on a university campus in Nanaimo, British Columbia marking the first Canadian occurrence of RHD in free-living rabbits. We used sight-resighting and rabbit count data to examine the short- and long-term impact of RHD on rabbit population size. We evaluated the medium-term impact of RHD on population size through sight-resighting surveys 6 months after the RHD outbreak started. During the initial phase of the outbreak, the population declined up to 5.75% per day and population size decreased 86% in less than 2 months. Population size remained low after the breeding season (mid-February through September) and through the following year. Population numbers began to recover within 2 years of the outbreak, and population size continued to increase through January 2025. Our results show RHDV2 can cause very high immediate mortality rates in feral rabbits and population level impacts of an outbreak can persist for years.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".