TARGETED RESEQUENCING OF WETLAND SEDIMENT AS A TOOL FOR AVIAN INFLUENZA VIRUS SURVEILLANCE
Bibliographic record
Abstract
Surveillance methods for avian influenza virus (AIV) based upon collecting and testing samples from individual wild birds have several significant limitations primarily related to the difficulties associated with obtaining samples. Because AIVs are shed in waterfowl feces, the use of environmental substrates where waterfowl feces accumulate may overcome some of these limitations. However, these substrates are difficult to analyze using traditional diagnostic techniques, such as virus culture and PCR, because of virus inactivation, RNA degradation, low concentration of target RNA, microbial complexity, presence of inhibitory substances, and other factors. We investigated the use of a genomics-based approach called targeted resequencing to detect and characterize AIVs in wetland sediments during the 2014-15 North American highly pathogenic avian influenza outbreak. We identified AIV in 20.6% (71/345) sediment samples obtained from wetlands (n15) and outdoor waterbodies on AIV-infected poultry farms (n10) in British Columbia, Canada (the first area affected during the outbreak). Thirteen hemagglutinin (HA) and nine neuraminidase (NA) subtypes were detected, including H5, N1, and N8 sequences that clustered with other sequences associated with the North American outbreak. Additionally, as many as eight HA and eight NA subtypes could be detected in a single sediment sample. This proof-of-concept study shows the potential utility of sediment sampling coupled with genomics-based analysis as a tool for AIV surveillance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".