Bovine Enteroviruses as Indicators of Fecal Contamination
Bibliographic record
Abstract
Surface waters frequently have been contaminated with human enteric viruses, and it is likely that animal enteric viruses have contaminated surface waters also. Bovine enteroviruses (BEV), found in cattle worldwide, usually cause asymptomatic infections and are excreted in the feces of infected animals in large numbers. In this study, the prevalence and genotype of BEV in a closed herd of cattle were evaluated and compared with BEV found in animals in the immediate environment and in environmental specimens. BEV was found in feces from 76% of cattle, 38% of white-tailed deer, and one of three Canada geese sharing the same pastures, as well as the water obtained from animal watering tanks, from the pasture, from streams running from the pasture to an adjacent river, and from the river, which emptied into the Chesapeake Bay. Furthermore, BEV was found in oysters collected from that river downstream from the farm. These findings suggest that BEV could be used as an indicator of fecal pollution originating from animals (cattle and/or deer). Partial sequence analysis of the viral genomes indicates that different viral variants coexist in the same area. The possibility of identifying the viral strains found in the animals and in the contaminated areas by sequencing the RNA genome, could provide a tool to find the origin of the contamination and should be useful for epidemiological and viral molecular evolution studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".