Avian influenza prevalence among hunter-harvested birds in a remote Canadian First Nation community
Bibliographic record
Abstract
INTRODUCTION: Avian influenza virus (AIV) prevalence has been associated with wild game and other bird species. The contamination of these birds may pose a greater risk to those who regularly hunt and consumed infected species. Due to resident concerns communicated by local Band Council, hunter-harvested birds from a remote First Nation community in subArctic Ontario, Canada were assessed for AIV. Hunters, and especially those who live a subsistence lifestyle, are at higher risk of AIV exposure due to their increased contact with wild birds, which represent an important part of their diet. METHODS: Cloacal swabs from 304 harvested game birds representing several species of wild birds commonly hunted and consumed in this First Nation community were analyzed for AIV using real-time reverse transcription polymerase chain reaction. Subtyping was performed using reverse transcription polymerase chain reaction. Sequences were assembled using Lasergene, and the sequences were compared to Genbank. RESULTS: In total, 16 of the 304 cloacal swab samples were positive for AIV. Of the 16 positive samples, 12 were found in mallard ducks, 3 were found in snow geese (wavies), and 1 positive sample was found in partridge. The AIV samples were subtyped, when possible, and found to be positive for the low pathogenic avian influenza virus subtypes H3 and H4. No samples were positive for subtypes of human concern, namely H5 and H7. CONCLUSIONS: This work represents the first AIV monitoring program results of hunter-harvested birds in a remote subsistence First Nation community. Community-level surveillance of AIV in remote subsistence hunting communities may help to identify future risks, while educating those who may have the highest exposure about proper handling of hunted birds. Ultimately, only low pathogenic strains of AIV were found, but monitoring should be continued and expanded to safeguard those with the highest exposure risk to AIV.
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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 | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".