Serological screening in wild ruminants in Germany, 2021/2022: No evidence of SARS‐CoV‐2, bluetongue virus or pestivirus spread but high seroprevalences against Schmallenberg virus
Bibliographic record
Abstract
Wildlife animals may be susceptible to multiple infectious agents of public health or veterinary relevance, thereby potentially forming a reservoir that bears the constant risk of re-introduction into the human or livestock population. Here, we serologically investigated 493 wild ruminant samples collected in the 2021/2022 hunting season in Germany for the presence of antibodies against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and four viruses pathogenic to domestic ruminants, namely, the orthobunyavirus Schmallenberg virus (SBV), the reovirus bluetongue virus (BTV) and ruminant pestiviruses like bovine viral diarrhoea virus or border disease virus. The animal species comprised fallow deer, red deer, roe deer, mouflon and wisent. For coronavirus serology, additional 307 fallow, roe and red deer samples collected between 2017 and 2020 at three military training areas were included. While antibodies against SBV could be detected in about 13.6% of the samples collected in 2021/2022, only one fallow deer of unknown age tested positive for anti-BTV antibodies, and all samples reacted negative for antibodies against ruminant pestiviruses. In an ELISA based on the receptor-binding domain (RBD) of SARS-CoV-2, 25 out of 493 (5.1%) samples collected in autumn and winter 2021/2022 scored positive. This sero-reactivity could not be confirmed by the highly specific virus neutralisation test, occurred also in 2017, 2018 and 2019, that is, prior to the human SARS-CoV-2 pandemic, and was likewise observed against the RBD of the related SARS-CoV-1. Therefore, the SARS-CoV-2 sero-reactivity was most likely induced by another hitherto unknown deer virus belonging to the subgenus Sarbecovirus of betacoronaviruses.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".