SARS-CoV-2 Infections in Animals: Reservoirs for Reverse Zoonosis and Models for Study
Bibliographic record
Abstract
The recent SARS-CoV-2 pandemic has brought many questions over the origin of the virus, the threat it poses to animals both in the wild and captivity, and the risks of a permanent viral reservoir developing in animals. Animal experiments have shown that a variety of animals can become infected with the virus. While coronaviruses have been known to infect animals for decades, the true intermediate host of the virus has not been identified, with no cases of SARS-CoV-2 in wild animals. The screening of wild, farmed, and domesticated animals is necessary to help us understand the virus and its origins and prevent future outbreaks of both COVID-19 and other diseases. There is intriguing evidence that farmed mink infections (acquired from humans) have led to infection of other farm workers in turn, with a recent outbreak of a mink variant in humans in Denmark. A thorough examination of the current knowledge and evidence of the ability of SARS-CoV-2 to infect different animal species is therefore vital to evaluate the threat of animal to human transmission and reverse zoonosis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".