Developing Policy Alternatives for the Management of Wood Bison (Bison bison athabascae) in Kluane National Park and Reserve of Canada
Bibliographic record
Abstract
A reintroduced population of wood bison (Bison bison athabascae) in the southwest Yukon has been growing and expanding its range; without intervention these bison are expected to soon migrate into Kluane National Park and Reserve of Canada. In order to enable a proactive response, we identify the key historical, social, ecological, and legal issues faced in the development of a wood bison management strategy for the park, including the critical question of whether they should be considered a native or an exotic species in the park. We believe wood bison should be considered native there. It is unclear what impact—positive or negative—bison will have on the ecological integrity of the park, since ecological integrity is a sufficiently plastic concept that it can be interpreted as including or excluding bison. We identify a range of alternative management strategies and the largely normative trade-offs associated with each, plus a set of actions that would be useful regardless of the alternative ultimately selected. A rational, feasible, and justifiable decision about the future of bison in the park will require a high-functioning and open co-management process so that participants with different values, knowledge, strategies, and interests can articulate and achieve their common interests.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.003 | 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".