Large carnivores, moose, and humans: a changing paradigm of predator management in the 21st century.
Bibliographic record
Abstract
ABSTRACT: We compare and contrast the evolution of human attitudes toward large carnivores between Europe and North America. In general, persecution of large carnivores began much earlier in Europe than North America. Likewise, conservation programs directed at restoration and recovery appeared in European history well before they did in North America. Together, the pattern suggests there has been an evolution in how humans perceive large predators. Our early ancestors were physically vulnerable to large carnivores and developed corresponding attitudes of respect, avoidance, and acceptance. As civilization evolved and man developed weapons, the balance shifted. Early civilizations, in particular those with pastoral ways, attempted to eliminate large carnivores as threats to life and property. Brown bears (Ursus arctos) and wolves (Canis lupus) were consequently extirpated from much of their range in Europe and in North America south of Canada. Efforts to protect brown bears began in the late 1880s in some European countries and population reintroductions and augmentations are ongoing. They are less controversial than in North America. On the other hand, there are no wolf introductions, as has occurred in North America, and Europeans have a more negative attitude towards wolves. Control of predators to enhance ungulate harvest varies. In Western Europe, landowners own the hunting rights to
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".