Hierarchical forest management planning and sustainable forest management in the boreal forest
Bibliographic record
Abstract
In keeping with international efforts to encourage sustainable forest management, new legislation, regulations, and certification criteria have been brought into effect across boreal regions of the world in the past decade or less. These initiatives have established hierarchical systems of forest management planning that consider multiple uses of the forest and various aspects of sustainable forest management at different scales. We describe the systems established in Quebec, Ontario, Saskatchewan, Alberta, and British Columbia, Russia, Finland, and Sweden. Most jurisdictions employ some form of three-level planning framework, in which strategic, tactical, and operational plans and considerations are presented with successively greater detail and spatial explicitness. However, planning scales and time horizons vary considerably, as does the level of consideration given to biodiversity and social concerns. We examine these systems in the context of sustainable forest management, raising a number of questions to be addressed in future research, adaptive management, and policy reform. In particular, we note (1) a need of new landscape and regional planning tools to evaluate the long-term and large-scale impacts of various land uses and (2) a general lack of responsiveness to global carbon and climate change concerns. Key words: forest management planning, sustainable forest management, boreal forest, forest policy, planning hierarchies, hierarchical planning
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".