Influenza monitoring in Sardinia, Italy identifies H3 subtype in Mediterranean wild migratory birds
Bibliographic record
Abstract
INTRODUCTION: Wild migratory birds are global distributors of pathogens. Sardinia, Italy, is the second largest Island in the Mediterranean and is a land bridge between Europe and Africa. METHODOLOGY: We designed a surveillance protocol to investigate wild migratory birds for presence, frequency, and type of avian influenza viruses. We collected over 4,000 avian samples and compared three sampling methods, fecal, cloacal, and tracheal, to determine the most productive for virus identification. To determine frequency of infection, RNA was extracted and RT-PCRs for avian influenza virus genes were run. Positive samples were cultivated for live virus, sub typed and sequenced. RESULTS: Forty-four samples were positive for influenza nucleoprotein gene. We identified two previously unidentified H3 subtype strains and found cloacae to have the highest rate of virus identification and fecal sampling to provide quality RNA and repeatable results for determination of virus presence. CONCLUSION: Our investigation provides information on the frequency of Mediterranean avian influenza viruses, and validates the initiation of an avian influenza surveillance protocol. Taken together with global avian influenza findings, these results give insight into infectious disease distributions which is important for viral pandemic monitoring and design of preventative measures.
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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 | 0.001 |
| 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 | 0.000 | 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 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".