The spatial–temporal relationship of blue‐winged teal to domestic poultry: Movement state modelling of a highly mobile avian influenza host
Bibliographic record
Abstract
Abstract Migratory waterfowl facilitate long‐distance dispersal of zoonotic pathogens and are increasingly recognized as contributing to the geographic spread of avian influenza viruses (AIVs). AIVs are globally distributed and have the potential to produce highly contagious poultry disease, economically impact both large‐scale and backyard poultry producers, and raise the spectre of epidemics and pandemics in human populations. Because migratory waterfowl behaviour varies across multiple spatial and temporal scales, the timing and distribution of wild bird AIV introductions to poultry are also heterogeneous in time and space. To help reduce economic impacts to the poultry industry and enable poultry producers to better anticipate when and where poultry outbreaks may occur, it is critically important to consider the movement ecology of the waterfowl species transporting and transmitting AIVs. We used telemetry for a geographically widespread and common AIV host, blue‐winged teal Spatula discors (BWTE), to model reservoir host movement states with respect to backyard and commercial poultry facilities in the United States. Our modelling framework enabled us to estimate wild bird proximity to poultry facilities while concurrently assessing the influence of poultry facilities on BWTE movement state transition. Our primary objective was to estimate the likelihood of duck and poultry overlap by estimating when and where BWTE were geographically closest to poultry. Synthesis and applications . Migratory waterfowl facilitate dispersal of the avian influenza viruses that cause highly contagious poultry disease. Movement analysis of blue‐winged teal indicates that spatio‐temporal overlap between wild birds and poultry facilities varies by season, the poultry type produced (e.g. turkey, chicken) and if the facility is a commercial or backyard operation. These findings are broadly applicable to disease ecology research and can be applied by poultry producers to improve biosecurity, enhance poultry management and prioritize disease surveillance efforts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".