Geographic, ecological, and temporal patterns of seabird mortality during the 2022 HPAI H5N1 outbreak on the island of Newfoundland
Bibliographic record
Abstract
Abstract Highly pathogenic avian influenza (HPAI) H5N1 caused mass seabird mortality across the North Atlantic in 2022. Following outbreaks in Europe, the first case in North America was detected on the island of Newfoundland (NFLD), Canada in November 2021, before spreading through all North American flyways. During the following breeding season, NFLD experienced the second-highest number of seabird mortalities in Canadian provinces. Surveys and citizen reports identified 13543 seabird mortalities from April to September 2022. Many carcasses occurred on the west coast of NFLD in May and June 2022. Reported mortalities peaked in July along the southeastern coast. In August and September, mortalities were concentrated along the northeastern coast. With the exception of two colony surveys, reported mortalities decreased in September. Most mortality was found among Northern Gannet (6622), Common Murre (5992), Atlantic Puffin (282), and Black-legged Kittiwake (217). Using comprehensive knowledge of seabird ecology, we formulated exploratory hypotheses regarding traits that could contribute to mortality. Species differences in mortality were most strongly associated with nesting density, timing of breeding, and at-sea overlap with allospecifics from other colonies. Unprecedented seabird mortality and ongoing transmission within the circulating avian influenza viruses highlight the need for continued monitoring and development of conservation strategies.
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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 | 0.000 | 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".