A Historical-Ecological Approach to Understanding the Effects of Timber Harvest on Wa’uumst (Devil's Club, Oplopanax horridus) in Lax'yip Madii Lii
Bibliographic record
Abstract
Industrial land-use has had profound impacts on Indigenous peoples’ homelands throughout Canada. Over the last century, logging practices in British Columbia have severed peoples’ connections to the land-base, creating access challenges and disrupting the availability of important plant resources. Devil’s club (Oplopanax horridus) is an important medicinal plant to communities in northwestern British Columbia, and there are mounting concerns about the impacts of logging on this ethnobotanically salient species. Over the last 70 years, swathes of productive forests throughout Gitxsan homelands have been impacted by the logging industry. Wilp (house) Luutkudziiwus has seen ~12% of their territory of Madii Lii altered by industrial-scale clearcut logging, which has left a mosaic of even-aged cutblocks that have potentially altered devil’s club habitat. To investigate and detail the potential impacts of logging on devil’s club in Madii Lii territory, this research measured devil’s club health and vigour across a chrono-sequence of clearcut-logged sites compared with traditionally managed and unlogged sites in the territory. In addition, Species Distribution Models in conjunction with two different climate change scenarios (SSP245 moderate GHGs and SSP585 high GHGs) were used to predict habitat suitability for devil’s club in Madii Lii and throughout British Columbia to the end of the 21st century. Results suggest that, compared to traditionally managed areas (and controls), extensive logging in the territory negatively impacted the health and vigour of devil’s club, especially the most desirable individuals (with the largest and thickest stems). In addition, the predictive modelling for both climate scenarios suggest that climate change will increasingly impact highquality devil’s club habitat.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".