Dealing With Deadstock: A Case Study of Carnivore Conflict Mitigation From Southwestern Alberta
Bibliographic record
Abstract
Livestock deaths are an unfortunate reality for livestock producers and dead livestock (i.e., deadstock) disposal options can have implications beyond the ranch itself. In Alberta, Canada, natural disposal (i.e., disposing of the carcass in a manner that allows for scavenging) has increased since the 2003 detection of bovine spongiform encephalopathy (BSE) in Canadian cattle. Prior to BSE, rendering companies removed deadstock for free. However, rendering companies started charging producers to remove deadstock to offset costs associated with new regulatory requirements enacted by the Canadian Food Inspection Agency, which has resulted in increased on-farm natural disposal of deadstock. This increase has ecological implications because deadstock are a major attractant for large carnivores. Carnivores feeding on deadstock are often near other agricultural attractants such as stored grain and feed, silage, and living livestock, which can exacerbate conflict potential and pose a risk to human safety. To help mitigate conflicts associated with deadstock, the Waterton Biosphere Reserve's (a local non-profit) Carnivores and Communities Program (CACP) supported expansion of community deadstock removal efforts beginning in 2009, including reimbursement of on-farm removal costs, bear-resistant deadstock bins, and a livestock compost facility (operational 2013–2014). Here, we present an evaluative case study describing the development, implementation, and results of the deadstock removal program, including the compost facility. We tracked the number of head of livestock removed each year, the number of participating landowners, the average cost per head, and total program costs. We also used an online survey to assess participants' perspectives of the deadstock removal program and future needs. To date, the CACP has removed >5,400 livestock carcasses, representing between 15.1 and 22.6% of available carcasses in the program area, and 67.3% of livestock owners indicated they currently use the deadstock removal program to dispose of deadstock. Average cost to compost an animal was significantly less than other removal methods ($36.89 composting vs. $79.59 non-composting, one-tailed t-test, unequal sampling variances: t = 4.08, df = 5.87, p = 0.003). We conclude by discussing both ecological and social implications for deadstock removal as a conflict mitigation measure and make suggestions for future management considerations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".