Identification and genomic characterization of a novel bisegmented coronavirus in the lesser panda
Bibliographic record
Abstract
Abstract Coronaviruses (CoVs) are enveloped positive-sense single-stranded RNA viruses and are renowned for their capacity to infect a diverse range of animals, including humans. In this study, we report the identification and characterization of a novel bisegmented coronavirus, designated LpCoV, from a dead lesser panda, which exhibited severe clinical manifestations and lesions in multiple organs. The LpCoV represents the first documented coronavirus with a unique bisegmented genome while maintaining the typical coronavirus morphology. One genomic segment encodes the ORF1ab polyprotein, while the other segment encodes the spike and nucleocapsid proteins, notably lacking the envelope (E) and membrane (M) genes. The pairwise patristic distances of the five concatenated domains in the replicase region (3CLpro, NiRAN, RdRP, ZBD, and HEL1) between LpCoV and α-δ coronaviruses range from 1.19 to 1.70. Based on the classification criteria established by the International Committee on Taxonomy of Viruses (ICTV), LpCoV is proposed to constitute a new genus within the Coronaviridae family. An epidemiological investigation identified five LpCoV-like viruses in lesser pandas from two different provinces (Sichuan and Jiangsu) indicating the long-term circulation and expansion of bisegmented coronaviruses in wildlife. These findings highlight the imperative for comprehensive viral surveillance in wildlife, which is essential for understanding and mitigating the risk of animal diseases and zoonotic spillover.
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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 | 0.001 | 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".