Identifying patterns of influenza A genotypes in wild birds
Bibliographic record
Abstract
Wild bird reservoirs of influenza A contribute to the overall genetic diversity of influenza, an increased range of endemic areas, as well as, transmission methods not commonly seen in human infections. These additions to influenza transmission increase the threat posed to human populations. Therefore, understanding the patterns of transmission of influenza A subtypes in avian hosts, as well as the environmental variables associated with transmission, is paramount to creating effective surveillance programs and forecasting potential areas of high genetic changes. Using a dataset of ~151,000 birds sample for avian influenza in the US and Canada from 1986-2017, we explore spatial patterns of influenza genotypes and model the environmental niches where certain types are found. Cluster analysis and niche modeling indicate overlap but also imperfect concordance between where each subtype of avian influenza was found and where each was predicted to circulate in wild bird populations. Overall, the Midwest and New England regions indicate higher risks of influenza A in wild birds across all flu types. In addition, the urban, wetland, and water land-cover types, as well as, low levels of human population density increase the likelihood of influenza presence in the avian populations. These results indicate that influenza transmission in wild birds is heavily affected by the activities of humans as well as the general characteristics of land cover types. Together, these results allow researchers to gain a better understanding of the spatial mechanisms of the broad scale patterns associated with influenza and the areas of particular risk associated with subtypes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".