Prevalence and diversity of avian influenza viruses in environmental reservoirs
Bibliographic record
Abstract
Little is known about the ecology and evolution of avian influenza in the natural environment, despite how these affect the potential for transmission. Most work has focused on characterizing viruses isolated from hosts such as waterfowl, and there have also been several instances of isolation and detection from abiotic sources such as water and ice. We used RT-PCR to amplify and characterize the influenza virus sequences present in sediments of ponds that are used heavily by waterfowl. The detection rate of influenza virus was high (>50%). Characterization of the viruses present by sequencing part of the haemagglutinin (HA) gene showed that there is a diverse collection of viruses in these sediments. We sequenced 117 partial HA gene clones from 11 samples and detected four different HA subtypes (H3, H8, H11 and H12), with approximately 65% of clone sequences being unique. This culture-independent approach was also able to detect a virus subtype that was not found by sampling of birds in the same geographical region in the same year. Viruses were detected readily in the winter when the ponds were frozen, indicating that these sediments could be a year-to-year reservoir of viruses to infect birds using the ponds, although we have not shown that these viruses are viable. We demonstrate that this approach is a feasible and valuable way to assess the prevalence and diversity of viruses present in the environment, and can be a valuable complement to more difficult viral culturing in attempting to understand the ecology of influenza viruses.
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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 | 0.001 |
| 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".