Opinions on strategies for forest adaptation to future climate conditions in western Canada: surveys of the general public and leaders of forest-dependent communities
Bibliographic record
Abstract
Two province-wide surveys of residents in Alberta and British Columbia were conducted to assess the acceptability of a range of reforestation strategies — many of which revolve around biotechnology — that could be used to aid western Canada’s forests in adapting to future climate change. The opinions of leaders of forest-dependent communities were also sought to evaluate how well they align with those of the public at large. Results show that the views of the general public and community leaders correspond. There is a low acceptance for a “do-nothing” strategy that allows climate change to run its course without any human intervention; high acceptance of replanting with local seeds; a decreasing acceptance of strategies that involve more manipulation such as breeding, using nonlocal seeds, and moving seeds outside of a species’ natural range; and a low acceptance of genetically engineered solutions. However, a high proportion of respondents changed their answers when told that a particular strategy would lead to either favourable or unfavourable outcomes related to socioeconomics of forest-dependent communities, forest aesthetics, and pest, disease, and fire outbreaks. We conclude that a meaningful and participatory dialogue on forest adaptation strategies in the face of climate change can only emerge if residents and other interested stakeholders have an adequate understanding of current forest management practices, proposed reforestation strategies, the role of technological interventions, and the values and services for which western Canada’s forests are to be managed.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".