Predicting high pathogenicity avian influenza H5N1 susceptibility in wild birds, with special reference to Australia
Bibliographic record
Abstract
High pathogenicity avian influenza (HPAI) has caused widespread sickness and mortality in poultry and wildlife, especially since the emergence of a novel H5 virus belonging to clade 2.3.4.4b in 2021. The ongoing panzootic caused by this lineage has infected an unprecedented diversity of species across the globe. Here, we analyse outbreak notifications of HPAI in wild birds to understand the impacts of species’ ecologies and phylogeny on HPAI notifications and predict host susceptibility to HPAI H5N1 for Australia, as the only continent thus far unaffected by this virus. We found a significant family-level phylogenetic signal in HPAI notifications in wild birds. Furthermore, we found that adding ecological traits to this phylogenetic information does not improve explanatory power of HPAI notifications. Using the family-level phylogenies to predict HPAI H5N1 susceptibility in Australian birds, we predict that families of Australian seabirds, shorebirds, and waterbirds will be most susceptible to HPAI H5N1 once it arrives on the continent. Our results provide an empirical indication of species susceptible to HPAI H5N1, with special reference to Australia, which can be used in conjunction with conservation status and other species-specific information to inform preparedness activities, monitoring, and response upon incursion.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".