Seabird and sea duck mortalities were lower during the second breeding season in eastern Canada following the introduction of Highly Pathogenic Avian Influenza A H5Nx viruses
Bibliographic record
Abstract
Summary H5N1 clade 2.3.4.4b viruses have caused significant mortality events in various wild bird species across Europe, North America, South America, and Africa. In North America, the largest impacts on wild birds have been in eastern Canada, where over 40,391 wild birds were reported to have died from highly pathogenic avian influenza (HPAI) between April and September 2022. In the year following, we applied previously established methods to quantify total reported mortality in eastern Canada for a full year October 2022, to September 2023. In this study, we (i) document the spatial, temporal and taxonomic patterns of wild bird mortality in the 12 months that followed the mass mortality event in the summer of 2022 and (ii) quantify the observed differences in mortality across the breeding season (April to September) of 2022 and 2023. In eastern Canada, there was high uncertainty about whether 2023 would bring another year of devastating HPAI-linked mortalities. Mortalities in the breeding season were 93% lower in 2023 compared to 2022 but encompassed a more taxonomically diverse array of species. We found that mortalities in the fall and winter (non-breeding season) were dominated by waterfowl, while mortalities during the spring and summer (breeding season) were dominated by seabirds. Due to a low prevalence of HPAI among the subset of tested birds, we refrained from broadly attributing reported mortalities in 2023 to HPAI. However, our analysis did identify three notable mortality events linked to HPAI, involving at least 1,646 Greater Snow Geese, 232 Canada Geese, and 212 Northern Gannets. This study emphasizes the ongoing need for H5NX surveillance and mortality assessments as the patterns of mortality in wild populations continue to change.
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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) | 0.002 | 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".