Landscape composition and structure influence the abundance of mesopredators: implications for the control of the raccoon (<i>Procyon lotor</i>) variant of rabies
Bibliographic record
Abstract
Rabies propagation in Canada has forced wildlife managers to develop intervention strategies to reduce the risk of rabies epizootics. We assessed whether some landscape characteristics of a corn-dominated region of Quebec in which the raccoon variant of rabies (RVR) has spread were associated with the abundances of raccoons ( Procyon lotor (L., 1758)) and striped skunks (Mephitis mephitis (Schreber, 1776)). We then examined whether landscape variables that best explained spatial variation in raccoon abundance were also good predictors in the detection of rabid raccoons. Between June and September 2007, 9600 raccoons and 1612 skunks were captured from 111 trapping cells. The abundance of captured raccoons, especially that of adult males and juveniles, increased over the summer in trapping cells characterized by a high density of forest edges bordering corn fields. The probability of detecting rabid raccoons also increased with this landscape characteristic, as well as with adult raccoon abundance. No landscape characteristic, however, explained spatial variation in skunk abundance. Efficient RVR control operations in similar landscapes should ideally include widespread distribution of vaccine baits because of the general distribution of skunks, while also focusing on areas where forest patches intersperse with corn fields to target high concentrations of raccoons, particularly in late summer.
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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 | 0.001 |
| 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".