Bibliographic record
Abstract
Africa's vast geographic and climatic diversity makes it a critical region for the ecology and spread of avian influenza viruses, particularly due to its role as a wintering ground for Palearctic migratory waterbirds. The continent has experienced multiple waves of clade 2.3.4.4b A/H5Nx high pathogenicity avian influenza (HPAI), which has caused widespread outbreaks in poultry and wild birds since 2017. From 2023 to mid-2025, clade 2.3.4.4b A/H5N1 outbreaks were reported across West and Southern Africa, with severe impacts on poultry production and conservation of endangered wild bird species. Concurrently, South Africa and Mozambique battled an unprecedented A/H7N6 HPAI epizootic in 2023, leading to the culling or loss of over 6.8 million chickens. Additionally, the A/H9N2 subtype, particularly G5.5 sublineage strains, continues to circulate in North, West, and East Africa, with reassortant viruses with A/H5Nx viruses reported in Egypt. Underreporting and limited surveillance hamper accurate epidemiological assessment. Despite these challenges, advancements in noninvasive environmental sampling offer promising tools for early detection. Strengthening regional cooperation and surveillance is essential for mitigating future outbreaks and protecting both animal 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".