Reintroducing bison to Banff National Park – an ecocultural case study
Bibliographic record
Abstract
The reintroduction of extirpated species is a frequent tactic in rewilding projects because of the functional role species play in maintaining ecosystem health. Despite their potential to benefit both ecosystems and society, however, most well-known species reintroductions have adopted an eco-centric, “nature-in-people-out” approach. Rewilding theory and practitioners acknowledge that ignoring the role Indigenous people did and might once again play in shaping the distribution, abundance, movements, behavior, and health of wild species and ecosystems, is limiting. In this case study, we describe the technical steps we took and how Indigenous knowledge, ceremony, and cultural monitoring were woven into the recent reintroduction of plains bison to Canada’s Banff National Park. Six years later, the reintroduced bison herd has grown from 16 to >100 animals, ranges mostly within 30 km of the release site, and, if current growth continues, will likely be managed with Indigenous harvesting. Transboundary bison policy differences are shifting and may lead to bison being more sustainable. The ecocultural approach, therefore, has increased the resilience of our rewilding project.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".