Zoonotic and Avian Pathogen Detections in Fecal and Sediment Samples – A Low-risk, High-throughput One Health Approach to Surveillance
Bibliographic record
Abstract
Abstract Many pathogens, both those with human spillover potential as well as avian-specific viruses, are maintained in wild bird populations. While routine surveillance for influenza A viruses (IAVs) is performed annually, surveillance for other pathogens is limited. Sampling of wild birds is time-consuming, labour-intensive, often limited in sample size, and involves handling of wild and potentially infected birds, posing an increased risk of direct exposure for personnel. Additional methods for surveillance are needed given these significant challenges. Longitudinal fecal and sediment sampling was performed at various sites in southern Manitoba, Canada, particularly focused in Winnipeg from May to October 2025. Sites were chosen based on the suitability of the area for waterfowl habitat, the presence of waterfowl in the area, as well as proximity to reported outbreaks of H5N1 influenza virus. Fecal and sediment samples were collected and screened for the presence of influenza A virus (IAV), Newcastle disease virus (NDV), avian reovirus (ARV), and avian poxvirus (APXV). In total, 782 combined fecal and sediment samples were collected. Of the 714 fecal samples, 34 tested positive for IAV RNA (4.8% prevalence). None of the IAV-positive fecal samples tested positive for H5 RNA. Of the 68 sediments, 15 were positive for IAV RNA (22.1% prevalence), four of which were positive for H5 RNA. NDV RNA positivity was low, with only four positive fecal samples (0.6% prevalence) that were all collected on the same day. ARV RNA positivity was also low, with five positive sediment samples (7.4% prevalence in sediment samples). None of the samples tested positive for APXV DNA. This study builds on previous work showing the utility of environmental sampling for a variety of avian and zoonotic pathogens using a One Health approach that is low-risk, efficient, and high-throughput.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".