Proceedings of the 8th Eastern CANUSA Forest Science Conference: Understanding and Managing ECANUSA Forests in a Changing Environment
Bibliographic record
Abstract
While land managers are facing the great challenge of managing forests over the next decades in a changing environment, one of the crucial question remains "What can we do now to manage forests and help them to face future conditions?"This presentation aims to provide insights about how silviculture can help forests cope with a changing environment, using examples drawn from Québec's temperate mixedwood forest.One striking realization when we start thinking about this question from a land manager's perspective is the high level of uncertainty.Forest conditions are expected to change because of unsuitable climatic conditions, insects and pathogens and new animal species (reviewed by Gauthier et al. 2014).Effects of global change on tree species and forest ecosystems could be positive or negative.These changes directly affect processes such as seed germination, seedling establishment, and forest tree growth and survival; in the long term, then can alter forest composition, structure and productivity.Global change will also have indirect effects on natural disturbance regimes by modifying the frequency of fires, windstorms, and other extreme events.Moreover, the expected rate of these changes will be faster than the capacity of trees to adapt.Animal species limited by cold temperatures or snow depth could increase their population size or expand their range toward new suitable habitats.Drought could also increase the risk of insect outbreaks.These new interactions could affect processes in forest ecosystems (Frelich et al. 2012).For KEY FINDINGS Diversity will decrease the risk of negative interactions with stressors. Resilience will help the most complex stands to cope with and recover from stressors. Transition options should help new stands evolve under changing conditions. A general strategy of adaptation should focus on integrating options that favor resilience in older stands, adaptation in intermediate stands and transition in younger stands.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.003 |
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".