BAITING AND BANDING: EXPERT OPINION ON HOW BAIT TRAPPING MAY INFLUENCE THE OCCURRENCE OF HIGHLY PATHOGENIC AVIAN INFLUENZA (HPAI) AMONG DABBLING DUCKs
Bibliographic record
Abstract
A Eurasian lineage highly pathogenic avian influenza virus (HPAIV) of the clade 2.3.4.4b (Goose/Guangdong lineage) was detected in migratory bird populations in North America in December 2021, and it, along with its reassortants, have since caused wild and domestic bird outbreaks across the continent. Relative to previous outbreaks, HPAIV cases among wild birds in 2022 exhibited wider geographic extent within North America and higher levels of mortality, suggesting the potential for population-level impacts. Given the possible conservation implications of HPAIV in wild birds, natural resource managers have sought guidance on actions that may mitigate negative effects of disease among North American bird populations, including modification of existing management practices. Banding of waterfowl is a critical tool for population management for several harvested species in North America, but some banding techniques, such as bait trapping, can lead to increased congregation of waterfowl, potentially altering HPAIV transmission. We used an expert opinion exercise to assess how bait trapping of dabbling ducks in Canada may influence HPAIV transmission and wild bird health. The expert group found that it is moderately likely that bait trapping of dabbling ducks in wetlands will significantly increase the transmission of HPAIV among individual ducks, but there is a low probability that this will result in significant population-level effects on North American dabbling ducks. Considering the lack of empirical work studying how capture and handling methods may change transmission of HPAIV among waterfowl, as well as the importance of bait trapping for waterfowl management in North America, future work should focus on filling knowledge gaps pertaining to the influence of baiting on HPAIV occurrence to better inform banding procedures and management decision making.
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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 | 0.027 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".