Consensus on Criteria for Potential Areas for Wolf Reintroduction in Mexico
Bibliographic record
Abstract
Given the conflict with human interests that in many cases results in the extirpation of large carnivores, acceptance of their reintroduction is a considerable challenge. By the 1980s Mexican wolves (Canis lupus) were extinct in the wild. In 1998 a population was reintroduced in the Blue Range Mountains of New Mexico (U.S.A.). Efforts to reintroduce the species in Mexico have been ongoing since the late 1980s. Four teams working independently identified 6 areas in northern Mexico in the historic range of Mexican wolves, where reintroductions could potentially be successful. Each team used different methods and criteria to identify the areas, which makes it difficult to prioritize among these areas. Therefore, members of the different teams worked together to devise criteria for use in identifying priority areas. They identified areas with high, intermediate, and low potential levels of conflict between wolves and humans. Areas with low potential conflict had larger buffers (i.e., distance from human settlement to areas suitable for wolves) around human settlements than high- and intermediate-conflict areas and thus were thought most appropriate for the first reintroduction. High-conflict areas contained habitat associated with wolf presence, but were closer to human activity. The first reintroduction of Mexican wolves to Mexico occurred in October 2011 in one of the identified low-conflict areas. The identification of suitable areas for reintroduction represents a crucial step in the process toward the restoration of large carnivores. Choice of the first reintroduction area can determine whether the reintroduction is successful or fails. A failure may preclude future reintroduction efforts in a region or country.
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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 | 0.040 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.013 | 0.005 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.009 | 0.008 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".