Spatiotemporal patterns of low and highly pathogenic avian influenza virus prevalence in murres in Canada from 2007 to 2022—a case study for wildlife viral monitoring
Bibliographic record
Abstract
Migratory seabirds move across ocean basins and are one of the primary reservoirs of low pathogenic avian influenza virus (LPAIV). This includes the millions of thick-billed murres ( Uria lomvia) and common murres ( Uria aalge) that are distributed across northern hemisphere oceans. In response to increasingly frequent detections of highly pathogenic avian influenza virus (HPAIV) in Europe in 2020–2021, avian influenza virus (AIV) monitoring in wildlife has increased. We compiled data from murres tested for AIV in Canada between 2007 and 2022 to quantify spatiotemporal variation in the prevalence of LPAIV and HPAIV in these birds. No HPAIV was detected in murres prior to 2022, but HPAIV was present in 46% of both live/harvested and found dead murres in the northwestern Atlantic in 2022 with prevalence peaking at 63% among live birds in the summer. In the eastern Canadian Arctic, HPAIV prevalence in 2022 was <1% while LPAIV prevalence was 21%, which was significantly higher than previous sampling years. Power analyses suggest approximately 100 samples from breeding murres should be collected annually per colony or region to detect moderate changes in HPAIV prevalence. These analyses inform robust monitoring of viruses in wildlife, with implications for conservation, harvest management, and public health.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".