Mad cow policy and management of grizzly bear incidents
Bibliographic record
Abstract
Abstract Protection of humans and livestock from disease has been used to justify many aggressive and costly wildlife control programs. Recent regulatory changes on livestock carcass disposal aimed at controlling the spread of bovine spongiform encephalopathy in Canada have led to substantial increases in exposed livestock carcass dumps. Such “boneyards” are known to attract grizzly bears ( Ursus arctos ), which leads to human–bear conflict. We compiled data on human–grizzly bear interactions in an agricultural landscape in southwestern Alberta over a 12‐year time period (1999–2010) overlapping regulatory changes. Boneyards increased markedly after regulations were enacted and grizzly bear incidents increased correspondingly, particularly those related to dead livestock. The high rate of conflict results in frequent management captures, relocations, and translocations that create a likely population sink. Although work is underway to reduce human–bear interactions, revisions are needed to recent regulatory changes, such that they take wildlife into account. When combined with programs aimed at ensuring proper storage of attractants, we believe that such policy reforms will make it possible for humans to coexist with grizzly bears in southwestern Alberta. © 2012 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".