Adapting to Climate Change in the Southwest Yukon: Locally Identified Research and Monitoring Needs to Support Decision Making on Sustainable Forest Management
Bibliographic record
Abstract
In a community-directed forest management context, research is needed that will help both the managers of forest resources and the community residents who set forest management directions to consider climate change in their decision making. Specific research needed in light of climate change to support implementation of the forest management plan for the Champagne and Aishihik Traditional Territory, southwest Yukon, was identified through 1) sessions with local forest practitioners and 2) a community climate change workshop. Local residents highlighted the importance of formalizing a monitoring network based on local knowledge as part of a broader adaptive management framework. They also wanted an important role in any discussion on adapting existing forest management plans, practices, and policies to incorporate climate change considerations. Forest practitioners expressed a need for research to identify forest management tactics that would enable them to achieve community-directed forest management objectives in light of climate change. Addressing these research needs will have benefits beyond just adapting forest management to climate change. Climate change is providing the impetus and a forum for discussing a broader issue: the need for a more comprehensive research and monitoring program to support the sustainable management of forest resources.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".