Environmental Sampling and Next-generation Sequencing as a Novel Approach for the Detection and Characterization of Influenza A Virus (IAV) in Swine
Bibliographic record
Abstract
Current influenza surveillance systems detecting zoonotic sources such as swine, are costly and labour-intensive. The genetic diversity due to reassortment of influenza A virus in swine underscores the need for a non-invasive, population based surveillance approach to improve pandemic preparedness. Aggregate, environmental samples from agricultural settings can be analyzed using high-throughput sequencing (HTS) techniques, providing an alternative approach to classic surveillance methods for influenza viruses of public health importance. We collected environmental samples from a swine barn in Southern Ontario, Canada. All samples were analyzed by RT-PCR for detection of the matrix gene. A subset of samples were sequenced using two HTS techniques for comparison. We derived viral genomic sequence from environmental samples and identified segment diversity of IAV of swine origin. We also demonstrated that personal samplers retained IAV from the breathing zone of personnel working with swine. The feasibility of environmental sample collection underscores its utility in IAV surveillance in swine production facilities.
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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 | 0.000 | 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) | 0.001 | 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".