Historical Review of Elk-Agriculture Conflicts in and Around Riding Mountain National Park, Manitoba, Canada
Bibliographic record
Abstract
Conflicts between elk (Cervus elaphus) and farmers have been occurring since the 1880s when agriculture began around what is now Riding Mountain National Park (RMNP). Initially, the conflicts were related to low elk numbers caused primarily by unregulated harvest of elk. The creation of RMNP in 1930 and the associated ban on hunting allowed elk numbers to reach critically high levels. Since farming began, elk have been associated with considerable damage to fences and crops around RMNP, with annual damage often >$240,000. Hunting on agricultural lands has been the most common approach to mitigating elk impacts, despite its limited success. Additionally, a damage compensation program was created in 1997. Beginning in 1991, elk–agriculture conflicts accelerated to a new level with the detection of bovine tuberculosis (TB) in local elk. Despite the concerns and economic hardship caused by TB, attitudes toward elk remain largely positive, and farmers obtain important economic and noneconomic benefits from the elk population. Conflicts between farmers and government about elk management often have been characterized by heated debates, poor or nonexistent communication, and, until recently, limited attempts to mitigate the impacts of elk. Future programs to address these conflicts should focus on collaboration and communication to develop mutually acceptable long-term solutions that are regularly evaluated using both local knowledge and scientific study.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.043 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".