Impact of highly pathogenic avian influenza virus (HPAIV) on Black-headed Gulls <i>Chroicocephalus ridibundus</i> population in Poland in 2023
Bibliographic record
Abstract
The highly pathogenic avian influenza virus (HPAIV) A(H5N1) has caused the most extensive and severe epizootic event affecting both poultry and wild birds globally. This study investigated the impact of HPAIV on the breeding population of the Black-headed Gull Chroicocephalus ridibundus, the most abundant gull species in Poland. During the 2023 outbreak, this species was reported as the most frequently infected in the country. A higher-than-natural adult mortality rate (greater than 1.5% of the breeding individuals) was observed in 114 surveyed colonies across all regions of Poland. Laboratory tests confirmed the presence of HPAIV in all 17 colonies sampled, with average adult mortality estimated at 26.1%, and ranging from 1.7% to 77.8%. The estimated mortality rate across all surveyed colonies was 22.2%. Extrapolations across the entire Polish breeding population (at least 115,000 pairs according to the national census) indicated that approximately 51,000 adult Black-headed Gulls might have perished due to HPAIV in 2023. The number of adults found dead was positively correlated with colony size (r = 0.733, P < 0.001). The deaths were associated with a single HPAIV genotype (BB) across all confirmed cases. Understanding the spread and severity of HPAIV in colonially breeding waterbirds, such as gulls, is essential for assessing the full extent of the threats this virus poses to wild bird populations.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".