THE IMPACT OF LAND USE, SEASON, AGE, AND SEX ON THE PREVALENCE AND INTENSITY OF<i>BAYLISASCARIS PROCYONIS</i>INFECTIONS IN RACCOONS (<i>PROCYON LOTOR</i>) FROM ONTARIO, CANADA
Bibliographic record
Abstract
We assessed the impact of land use, demographic factors, and season on the prevalence and intensity of Baylisascaris procyonis infections in raccoons (Procyon lotor) in Ontario, Canada. From March to October 2012, we recorded the number of B. procyonis in the intestinal tracts of raccoons submitted to the Canadian Cooperative Wildlife Health Centre for necropsy. Logistic regression models were used to examine associations between the presence of B. procyonis and age (adult, juvenile), sex, land use (suburban/urban, rural), and season (March-June and July-October); negative binomial regression models were used to examine associations between the number of worms and the same variables. We detected B. procyonis in 38% (95% confidence interval 30-47%) of raccoons examined (n=128). In univariable models, the presence of B. procyonis was significantly associated with age, land use, and season (P<0.05). Age was not retained in the multivariable model, and the impact of sex on the presence of B. procyonis varied with land use and season. For example, from March to June, suburban/urban male raccoons were significantly more likely to be infected with B. procyonis than suburban/urban female raccoons. However, later in the summer (July-October), the opposite was true. The median number of worms in the intestinal tracts of infected raccoons was 3 (range 1-116). Worm number was significantly associated with age and season in univariable models; in the multivariable model, juvenile raccoons had significantly more worms than adults, and the impact of season on the number of worms varied with land use and sex. A better understanding of the epidemiology of B. procyonis in raccoons is important for developing appropriate strategies to reduce the risk of human exposure to B. procyonis from the environment.
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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 | 0.000 | 0.001 |
| 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) | 0.000 | 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".