DEVELOPING POPULATION CONTROL STRATEGIES FOR WILD BOAR MANAGEMENT IN CANADA
Bibliographic record
Abstract
Abstract\nDEVELOPING POPULATION CONTROL STRATEGIES FOR WILD BOAR MANAGEMENT IN CANADA\nAmanda Wong Advisor: Scott Slocombe\nWilfrid Laurier University 2020\nCanada’s landscape faces major threats from the growing wild boar (Sus scrofa) population, whose current presence predominantly threatens the Prairie provinces. Globally it has become apparent that wild boars are robust animals with high reproductive rates and destructive behaviours in both their native and non-native ranges. This paper analyzes wild boar management strategies that have been conducted around the world to identify the most effective tools, and those that were unsuccessful. The wild boars in Canada are hybridized pigs, a mix of Sus scrofa and domesticated pigs, which were subsequently released in the 1990s after a failed introduction of game meats in the food sector.\nTo achieve the objective of the research paper, a review of wild boar impacts and management research was completed, with a greater focus on studies from North America. Literature that demonstrated successful removal of wild boar or the reduction of damage by boar within a study site were favored. Following the data extraction, an analysis of the Canadian invasive species strategy at a federal and provincial level was conducted to determine the current weaknesses in invasive species plans and how wild boar management could be incorporated into the existing frameworks.\nThe research suggests that a coordinated approach with non-lethal and lethal tools had the best results in eradicating wild boar. The results from the literature analysis demonstrated that a mixed approach would provide the best results, but this requires more advanced frameworks in provincial and federal strategies. To make the necessary management improvements, more research is required to determine i) the total wild boar numbers in Canada ii) the full extent of ecological damage iii) and the economic losses in the agricultural and natural resource sectors.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".