Compensation Programs in Wyoming for Livestock Depredation by Large Carnivores
Bibliographic record
Abstract
Common law in America, which has been continually reinforced in the courts of the United States, holds that the people of the state own the wildlife within its boundaries. No person or entity holds absolute property rights to wildlife regardless of the ownership of the land on which the animal is found. The courts have construed that since wildlife belongs to everyone, everyone must share in its keep. As a result of this interpretation, courts have ruled the government, both state and federal, is immune from liability for damage caused by wild animals, unless the government waives its sovereign immunity and voluntarily assumes liability. The federal government has long invoked its sovereign immunity from liability for damage caused by species managed under federal law, such as migratory waterfowl, passerine birds, and those species listed as threatened or endangered, such as grizzly bears and gray wolves. In addition, many states have traditionally invoked their sovereign immunity from liability for damage caused by wild animals. As an example, the state of South Dakota does not accept monetary liability for damage done by wildlife. Conversely, some states, such as Wyoming, Utah, Washington and Idaho, have waived their sovereign immunity to a limited degree and assumed liability for some types of damage caused by some types of wild animals. After a century of persecution that resulted in large scale population reductions, large predator numbers have increased over much of their former ranges in North America. Predators such as wolves, cougars and grizzly bears are making a comeback in parts of the West. The comeback is largely due to a variety of changing societal values about predators that have resulted in reduced control campaigns. Along with the increase in predators, predator compensation programs have evolved in some jurisdictions. Currently, fourteen states and four Canadian provinces have government administered programs to reimburse livestock owners for losses caused by some predators. In addition, Defenders of Wildlife, a private conservation group, reimburses livestock producers for losses caused by grizzly bears in two western states and wolves in three western states. Most programs pay for losses caused by only the large predators (black bears, grizzly bears, cougars, and wolves) even though in most states smaller predators, such as coyotes or golden eagles, cause a far more significant monetary loss to the livestock industry. This industry is important, and in some instances critical, to the rural infrastructure and local economies of Wyoming.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".