RACCOON AND SKUNK POPULATION MODELS FOR URBAN DISEASE CONTROL PLANNING IN ONTARIO, CANADA
Bibliographic record
Abstract
Population data on raccoon (Procyon lotor) and striped skunk (Mephitis mephitis), collected between 1987 and 1996 in the city of Scarborough (Ontario, Canada), were used to develop spatially explicit population models for use in disease control planning. The objective of model development was to: (1) provide a standard analytical method to identify areas of high-density raccoon and skunk subpopulations within cities, and (2) to identify those subpopulations predicted to function as sites of high dispersal (either into or out of subpopulations). These areas could be targeted in disease control programs. The models combined landscape map data with a stochastic, age-structured population model, which incorporated habitat-specific demographic data and functions relating to animal dispersal. Using this approach, the assemblage of raccoons and skunks inhabiting Scarborough was modeled as occupying discrete subpopulations linked by dispersal (i.e., a metapopulation). The landscape data used in this study were derived from classified LANDSAT satellite imagery data. Population data were derived from the literature and from trapping data collected within the Scarborough study area. The resulting models depicted metapopulations containing 7432 ± 1529 raccoons (mean ± 1 sd) distributed throughout eight subpopulations, and 533 ± 125 skunks distributed throughout 10 subpopulations. Raccoon density within subpopulations ranged from 37 to 94 animals/km2. Skunk density within subpopulations ranged from 6.4 to 12.6 animals/km2. Five raccoon subpopulations and one skunk subpopulation were predicted to stabilize at high relative population densities (>125% carrying capacity), implying that these subpopulations were functioning as net importers of dispersing animals. As such, these subpopulations were at higher risk of being sites of rabies outbreaks than surrounding subpopulations, owing to their high population densities and greater likelihood of receiving infected individuals. In contrast, one raccoon subpopulation stabilized at low relative population density and therefore appeared to be functioning as a net exporter of dispersing animals. The disease control implications of these findings are discussed.
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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 | 0.000 |
| 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".