Bait Trapping Linked to Higher Avian Influenza Virus Detection in Wild Ducks
Bibliographic record
Abstract
In 2007, we assessed whether trapping method influenced apparent prevalence of low pathogenic avian influenza viruses (AIV) in wild ducks sampled during Canada's Inter-agency Wild Bird Influenza Survey. Combined cloacal and oropharyngeal swabs were collected from 514 ducks captured by bait trapping (356) and netting from airboats (158), and tested by real-time reverse transcriptase polymerase chain reaction for influenza type A viruses. When controlling for species and capture site, ducks caught in bait traps were 2.6 times more likely to test positive for AIV compared with those netted from airboats (95% CI=1.2-6.0). If bait trapping increases AIV transmission among artificially aggregated ducks, this could have important implications for interpretation of disease surveillance results and waterfowl management programs.
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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 | 0.001 | 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".