Viral Diseases in Water Buffalo (Bubalus bubalis): New Insights and Perspectives
Bibliographic record
Abstract
) has great adaptability to rustic environments and more variable conditions than cattle, who generally share the habitat. Diseases carried by buffaloes are relatively unknown and ignored and could be transmissible; an imbalance occurs between pathogens, environment, and susceptible hosts, generating a severe animal health problem. Also relevant is the effect of climate change on the populations of vectors that transmit viral diseases. The discovery of new virus variants that can pass from bovine (Bos) to buffalo or vice versa or to humans has highlighted the relevance of viruses crossing the host barrier. This review discusses the clinical viral diseases most reported in the water buffalo, characteristics, epidemiology, and recent findings about disease behavior, interaction with other species, the host, vectors, and pathogens. Diseases reviewed include Foot and Mouth Disease, Rinderpest, Malignant Catarrhal Fever, Infectious Bovine Rhinotracheitis, Bovine Viral Diarrhea, and Rabies. Also, vector-borne diseases include Lumpy Skin Disease, Ephemeral Fever, and Blue Tongue. The review also considers emerging viruses such as Buffalo Pox and Schmallenberg and, finally, other viruses such as papillomatosis. The knowledge and epidemiology of buffalo viral diseases must be constantly reconsidered and updated for adequate prevention and control programs.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".