Evidence that Life History Characteristics of Wild Birds Influence Infection and Exposure to Influenza A Viruses
Bibliographic record
Abstract
We report on life history characteristics, temporal, and age-related effects influencing the frequency of occurrence of avian influenza (AI) viruses in four species of migratory geese breeding on the Yukon-Kuskokwim Delta, Alaska. Emperor geese (Chen canagica), cackling geese (Branta hutchinsii), greater white-fronted geese (Anser albifrons), and black brant (Branta bernicla), were all tested for active infection of AI viruses upon arrival in early May, during nesting in June, and while molting in July and August, 2006-2010 (n = 14,323). Additionally, prior exposure to AI viruses was assessed via prevalence of antibodies from sera samples collected during late summer in 2009 and 2010. Results suggest that geese are uncommonly infected by low pathogenic AI viruses while in Alaska. The percent of birds actively shedding AI viruses varied annually, and was highest in 2006 and 2010 (1-3%) and lowest in 2007, 2008, and 2009 (<0.70%). Contrary to findings in ducks, the highest incidence of infected birds was in late spring when birds first arrived from staging and wintering areas. Despite low prevalence, most geese were previously exposed to AI viruses, as indicated by high levels of seroprevalence during late summer (47%-96% across species; n = 541). Seroprevalence was >95% for emperor geese, a species that spends part of its life cycle in Asia and is endemic to Alaska and the Bering Sea region, compared to 40-60% for the other three species, whose entire life cycles are within the western hemisphere. Birds <45 days of age showed little past exposure to AI viruses, although antibodies were detected in samples from 5-week old birds in 2009. Seroprevalence of known age black brant revealed that no birds <4 years old had seroconverted, compared to 49% of birds ≥4 years of age.
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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 | 0.000 | 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".