Geographic, ecological, and temporal patterns of seabird mortality during the 2022 HPAI H5N1 outbreak on the island of Newfoundland
Bibliographic record
Abstract
Highly pathogenic avian influenza (HPAI) H5N1 caused mass mortality of wildlife across the North Atlantic in 2022. Following European outbreaks, the first North American case was detected on the island of Newfoundland, Canada in November 2021, before spreading throughout North America. During the following summer, Newfoundland and surrounding islands (NFLD) experienced one of the most significant mortalities in Canadian provinces, with seabirds being the most affected taxa. From surveys and citizen reports, we estimate that 13 517 mortalities that can be attributed to HPAI occurred in NFLD from April to September 2022. Most estimated mortalities were among Northern Gannets ( Morus bassanus (Linnaeus, 1758)) (6596), Common Murres ( Uria aalge (Pontoppidan, 1763)) (5992), Atlantic Puffins ( Fratercula arctica (Linnaeus, 1758)) (282), and Black-legged Kittiwakes ( Rissa tridactyla (Linnaeus, 1758)) (217). Mortality reports moved from west to east along the southern, then eastern NFLD coast, and peaked in July and August. We formulated exploratory hypotheses regarding traits that could contribute to infection and mortality. Species differences in mortality most strongly associated with inter-nest distance, breeding phenology, 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".