Using climate vulnerability assessments to implement and mainstream adaptation by the forest industry into forest management in Canada
Bibliographic record
Abstract
Climate change is an increasing concern for forest managers and society as a whole. The impacts of climate change on forest ecosystems may limit the ability of forest managers to achieve sustainable forest management (SFM) objectives, and changes to management or practices may be required in response. While academic literature emphasizes the need for adaptation to climate change and proposes what kind of higher-level changes are required to facilitate that change, less attention has been paid to what forest managers need and their ability to implement adaptation. In this study, we describe a recent example of proactive climate change adaptation in Canada’s forest industry, the first instance in which a Canadian forest company operating within a publicly owned land base has undertaken a formal climate change adaptation planning process. We show how Mistik Management Ltd., a partnership between nine indigenous nations and a pulp and paper company, used a climate change vulnerability assessment framework to identify vulnerabilities and develop management strategies to mitigate climate risks while also changing management practices. We show how Mistik is mainstreaming climate change considerations into their management system and implementing it through changes in their management practices. At the institutional level, we found no substantive barriers to Canadian forestry firms seeking to incorporate adaptation into ongoing planning and management activities and suggest how the lessons from Mistik’s experiences can inform forest management adaptation policies and processes more generally, not only in Canada but elsewhere.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".