Biodiversity offsets and caribou conservation in Alberta: opportunities and challenges
Bibliographic record
Abstract
The federal recovery strategy for boreal woodland caribou (Rangifer tarandus caribou) sets a goal of self-sustaining populations for all caribou ranges across Canada. All caribou herds in Alberta are currently designated as not self-sustaining and the recovery strategy requires an action plan to achieve self-sustaining status. At the same time, continued natural resource extraction in caribou ranges may be worth hundreds of billions of dollars. Some regulatory bodies have recognized an opportunity for biodiversity offsets to help meet the caribou recovery strategy’s goals while still permitting economic benefits of development. In this review, we evaluate offset opportunities for caribou in Alberta and practical impediments for implementation. We conclude that a number of actions to offset impacts of development and achieve no net loss or net positive impact for caribou are theoretically feasible (i.e., if implemented they should work), including habitat restoration and manipulations of the large mammal predator-prey system. However, implementation challenges are substantial and include a lack of mechanisms for setting aside some resources for long periods of time, public opposition to predator control, and uncertainty associated with loss-gain calculations. A framework and related policy for offsets are currently lacking in Alberta and their development is urgently needed to guide successful design and implementation of offsets for caribou.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".