Feral wild boar distribution and perceptions of risk on the central Canadian prairies
Bibliographic record
Abstract
ABSTRACT Feral wild boar ( Sus scrofa ) are rapidly expanding their distribution and abundance globally and causing considerable socio‐economic impacts. Prior to this study, the spatial distribution of feral boar on the Canada prairies was largely unknown. We surveyed all 296 rural municipalities in Saskatchewan, Canada, to determine the distribution of feral boar in the province and characterize community leader perceptions of risk. Of the respondents, over the past 3 years 48% never saw feral boar, 48% saw them at least occasionally, and 3% responded “I don't know,” indicating a few respondents were not confident in saying feral boar were present or absent. Feral boar were observed across a range of habitats, in all months, and at all times of day. Variables that best predicted the distribution of feral boar included % farmland ( β = 6.46), % flaxseed crop ( β = −8.63), density of paved roads ( β = −1.92), % deciduous forest ( β = 5.93), and % mustard seed crop ( β = −12.63). Mapping the resource selection probability function (RSPF) across the landscape of rural Saskatchewan predicted 70% of municipalities had RSPF >0.7 (high probability of boar presence) and 12% had RSPF <0.3 (low probability of boar presence). At the scale of the individual municipalities, responses about management actions were positively associated with frequency of feral boar observations, whereas questions about the province as a whole were consistently positive regardless of frequency of feral boar observations. Control efforts in Canada are sporadic and limited in scope and scale, but the current distribution of feral boar in Saskatchewan, in combination with the life‐history strategy of the species, indicates that aggressive and coordinated action is required. © 2014 The Wildlife Society.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".