Targeted genomic sequencing of avian influenza viruses in wetlands sediment from wild bird habitats
Bibliographic record
Abstract
ABSTRACT Diverse influenza A viruses (IAVs) circulate in wild birds, including dangerous strains that infect poultry and humans. Consequently, surveillance of IAVs in wild birds is a cornerstone of outbreak prevention and pandemic preparedness. Surveillance is traditionally done by testing birds, but dangerous IAVs are rarely detected before outbreaks begin. Testing environmental specimens from wild bird habitats has been proposed as an alternative. These specimens are thought to contain diverse IAVs deposited by broad range of avian hosts, including species that are not typically sampled by surveillance programs. We developed a targeted genomic sequencing method for recovering IAV genome fragments from these challenging environmental specimens, including purpose-built bioinformatic analysis tools for counting, subtyping, and characterizing each distinct fragment recovered. We demonstrated our method on 90 sediment specimens from wetlands around Vancouver, Canada. We recovered 2,312 IAV genome fragments originating from all 8 IAV genome segments. 11 haemagglutinin (HA) subtypes and 9 neuraminidase subtypes were detected, including H5, the current global surveillance priority. Recovered fragments originated predominantly from IAV lineages that circulate in North American resident wild birds. Our results demonstrate that targeted genomic sequencing of environmental specimens from wild bird habitats can be a valuable complement to avian influenza surveillance 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".