Modeling outcomes of approaches to sustained human and snow leopard coexistence
Bibliographic record
Abstract
The snow leopard (Uncia uncia) is in danger of extinction. Killing to protect livestock is among the primary causes of its decline. Efforts to mitigate this threat have focused on balancing the need to conserve the snow leopard with the needs of local people in snow leopard habitat, many of whom rely on raising livestock for their livelihoods. Conservation of the snow leopard has the characteristics of a public good, and outside funding is required to support conservation efforts. There are 5 commonly discussed approaches to resolving this issue: (1) direct payments for conservation, (2) investments in protection from predation, (3) damage compensation payments, (4) investments in better livestock husbandry, and (5) leases of pastureland for wild prey. After a review of these 5 conservation strategies, an economic-ecologic model, which includes the interactions between the snow leopard, its wild prey, and livestock, is used to evaluate the 2 most promising conservation strategies. The model reveals that investments in protection from predation and leases of pastureland for wild prey are effective but only in delaying the eventual extinction of the snow leopard. To preserve the snow leopard, these approaches must be applied more aggressively and new ones explored.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".