Forests in an uncertain context: comparing contrasting strategies of risk management at the local and regional scales
Bibliographic record
Abstract
Under the increasing uncertainties of forest management conditions, this project aims to test the hypothesis that managing diversity and functional redundancy at landscape-level maximizes the resilience and multi-functionality of forests. This hypothesis will be confronted, in particular, to an adaptation strategy using a limited number of genotypes, chosen according to some expected climate changes. Based on local and regional simulations, a first axis will evaluate the response of two forest regions (in Quebec and Wallonia) to these distinct strategies for adapting to climate change, taking into account the uncertainties in climate projections and in modelling the response of forests to global changes. A second axis will identify the socio-economic constraints limiting the implementation of these strategies, through a detailed analysis of governance at different scales. The results of this research will allow to understand the mechanisms involved in the resilience of forests to global changes, and to identify the best combinations of forest management and policies for contrasting scenarios of climate and global change.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".