Longitudinal screening of retail milk from Canadian provinces reveals no detections of influenza A virus RNA (April–July 2024): leveraging a newly established pan-Canadian network for responding to emerging viruses
Bibliographic record
Abstract
Highly pathogenic avian influenza (HPAI) H5N1 has caused the deaths of more than 100 million birds since 2021, and human cases since 1997 have been associated with significant morbidity and mortality. Given recent detections of HPAI H5N1 in dairy cattle and H5N1 RNA detections in pasteurized retail milk in the United States, we established the pan-Canadian Milk Network in April 2024. Through our network of collaborators from across Canada, retail milk was procured longitudinally, approximately every 2 weeks, and sent to a central laboratory to test for the presence of influenza A virus RNA. Between 29 April and 17 July 2024, we tested 109 retail milk samples from all 10 Canadian provinces (NL, NS, PEI, NB, QC, ON, MB, SK, AB, and BC). All samples tested negative for influenza A virus RNA. This nationwide initiative was established for rapid retail milk screening as per the earliest reports of similar undertakings in the United States. Our independent testing results have aligned with reporting from federal retail milk testing initiatives. Despite no known HPAI infections of dairy cattle in Canada to date, H5N1 poses a significant threat to the health of both humans and other animals. By performing routine surveillance of retail milk on a national scale, we have shown that academic networks and initiatives can rapidly establish nationwide emerging infectious disease surveillance that is cost-effective, standardized, scalable, and easily accessible. Our network can serve as an early detection system to help inform containment and mitigation activities if positive samples are identified and can be readily reactivated should HPAI H5N1 or other emerging zoonotic viruses be identified in agricultural or livestock settings, including Canadian dairy cattle.
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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 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".