Socioeconomic Benefits of Large Carnivore Recolonization Through Reduced Wildlife‐Vehicle Collisions
Bibliographic record
Abstract
Abstract The decline of top carnivores has released large herbivore populations around the world, incurring socioeconomic costs such as increased animal–vehicle collisions. Attempts to control overabundant deer in the Eastern United States have largely failed, and deer–vehicle collisions (DVCs) continue to rise at alarming rates. We present the first valuation of an ecosystem service provided by large carnivore recolonization, using DVC reduction by cougars as a case study. Our coupled deer population models and socioeconomic valuations revealed that cougars could reduce deer densities and DVCs by 22% in the Eastern United States, preventing 21,400 human injuries, 155 fatalities, and $2.13 billion in avoided costs within 30 years of establishment. Recently established cougars in South Dakota prevent $1.1 million in collision costs annually. Large carnivore restoration could provide valuable ecosystem services through such socio‐ecological cascades, and these benefits could offset the societal costs of coexistence.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".