Climate change and the forest sector: Perception of principal impacts and of potential options for adaptation
Bibliographic record
Abstract
As evidence points to the importance of climate change (CC) impacts on forests, it is critical to understand how forestry and forest-dependent communities will be affected. People active in the Quebec forest sector were consulted about their perceptions on the most important potential impacts and adaptation measures. Preoccupations covered many aspects of natural ecosystems, forest-based communities, and industries. Expected impacts and adaptation measures were grouped according to biomes and sectors. Prioritized impacts included increases in extreme meteorological events and natural disturbances. Impacts were also expected for human or economic systems such as reductions in wood volume and quality, difficulties in accessing forests, and additional costs for forest operations. Adaptation was perceived to come from new policies, a greater awareness, and local and regional adjustments to forest operations and management. Identified barriers to adaptation included lack of knowledge or understanding of CC impacts, lack of scientific support and knowledge transfer, and lack of leadership in CC issues at a regional scale. This synthesis will help orient future needs in climate-sensitive forest management planning and identify ways to increase adaptive capacity of the forest sector.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".