RISK FACTORS ASSOCIATED WITH WEST NILE VIRUS MORTALITY IN AMERICAN CROW POPULATIONS IN SOUTHERN QUEBEC
Bibliographic record
Abstract
Soon after the appearance of West Nile virus (WNV) in North America, a number of public health authorities designated the American Crow (Corvus brachyrhynchos) a sentinel for WNV detection. Although preliminary studies have suggested a positive association between American Crow mortality and increased risk of WNV infection in humans, we still know little about dynamic variation in American Crow mortality, both baseline levels and mortality associated with WNV. We hypothesized that the complex social behavior of American Crows, which is shaped by age and seasonal factors, influences both baseline mortality and WNV mortality in American Crow populations. We examined American Crow mortality data from Quebec for the 2005 WNV surveillance year, which lasted from 5 June to 17 September 2005. The variables of interest were age, gender, body condition index, time of year, and land cover. We used a log-linear model to examine baseline mortality. Logistic regression and general linear regression models were constructed to examine variables associated with mortality due to WNV. We found that both age and time of year were key variables in explaining baseline mortality. These two variables were also risk factors for WNV mortality. The probability that a carcass tested positive for WNV increased with the age of the dead bird and as summer progressed. WNV-positive carcasses also had a lower body condition index than WNV-negative carcasses. We believe that the first major wave of American Crow mortality observed in the early summer of 2005 was the result of natural mortality among young American Crows. Because this mortality was not linked to WNV, it appears that American Crow may not be a good species for early detection of WNV activity. Our data also suggest that second-year American Crows play a major role in propagating WNV during their movements to urban land covers during midsummer.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".