The Wolverine Project: Evaluating Cumulative Effects Within the Land of Ktunaxa Using the One Heart Method
Bibliographic record
Abstract
This study evaluated the cumulative effects of existing and proposed land use activities and climate change within a narrow and vital wildlife corridor considered of high value to Ktunaxa and non-Ktunaxa peoples. Cumulative alteration, degradation, and disturbance to habitat directly impacts Ktunaxa lands and waters on which the exercise of Ktunaxa rights depends. Increased access to backcountry areas are expected to substantially diminish habitat condition, increase wildlife displacement and mortality risk, and fragment wildlife populations with unavoidable population-level impacts. The effects of land use are likely to be exacerbated by accelerating climate change further limiting habitat suitability and creating additional human-wildlife interactions. This study is unique in that we applied Ktunaxa research methodology throughout all assessment stages. This iterative approach to knowledge gathering brings Ktunaxa and non-Ktunaxa together in concert with input from a diverse group of problem solvers. Further, we used this opportunity to refine our understanding of cumulative effects through a Ktunaxa linguistic perspective. Repeated interviews deepened research relationships and empowered use of Ktunaxa oral history in the evaluation of cumulative effects. Two species of key concern identified for this study were ʔaȼ̓pu (wolverine) and kɬawɬa (grizzly bear). We simulated current and future habitat condition for key values using a pre-contact baseline representing the Range of Natural Variability (RONV). The assessment presented herein considered past, current, and proposed future land use activities, with a focus on recreational interests. We found combined effects of proposed recreation developments with existing land use practices within the study area to substantially negatively impact both wolverine and grizzly bear habitat condition and population connectivity. We concluded that cumulative developments of past, current and any potential future land use that includes outdoor recreational activities in ʔamak̓is Ktunaxa must be informed by regional-scale and long-term land stewardship planning to prevent further adverse impacts and to ultimately improve the habitat conditions for ʔa·kxam̓is q̓api qapsin (All Living Things).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".