COMPARISON OF TWO SURVEILLANCE COMPONENTS FOR INVESTIGATING THE EPIDEMIOLOGY OF CANINE DISTEMPER VIRUS IN RACCOONS (PROCYON LOTOR)
Bibliographic record
Abstract
Canine distemper virus (CDV) has a broad mammalian host range. In Ontario, Canada, CDV is frequently encountered in wild carnivores and is the most common infectious cause of death for raccoons (Procyon lotor). The isolation of wild-type CDV strains genetically distinct from vaccine strains in North America has renewed interest in the epidemiological patterns of this virus. However, wildlife surveillance is challenging and often utilizes a combination of surveillance methods with aggregation of data from multiple sources. Our objective was to compare raccoon CDV data generated through two separate surveillance components operated by the Ontario-Nunavut node of the Canadian Wildlife Health Cooperative. The raw data generated by each component in addition to the results of multilevel logistic regression and spatial scan statistics, were compared between the datasets. A total of 498 raccoons obtained via passive surveillance between 2007 and 2017 and 887 raccoons obtained via enhanced-passive surveillance between 2014 and 2017, were tested for CDV. The number and geographic distribution of reports, proportion of yearly reports classified as CDV-positive, and characteristics of CDV-positive raccoons differed between passive and enhanced-passive surveillance components. Geographical data demonstrated that CDV infection was present throughout southern Ontario. The geographic area of both surveillance components combined was more representative than either passive or enhanced-passive surveillance in isolation; but was restricted compared to the overall distribution of raccoons in Ontario. Regression analyses produced statistically significant associations between the presence of CDV and host and environmental variables that were at times discordant between the two datasets. Studying the properties of these datasets will inform future passive wildlife surveillance strategies and highlights the impact that a surveillance strategy can have on the results of epidemiological analyses.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".