Managing Forests for Water in the Anthropocene—The Best Kept Secret Services of Forest Ecosystems
Bibliographic record
Abstract
Water and forests are inextricably linked. Pressures on forests from population growth and climate change are increasing risks to forests and their aquatic ecosystem services (AES). There is a need to incorporate AES in forest management but there is considerable uncertainty about how to do so. Approaches that manage forest ecosystem services such as fiber, water and carbon sequestration independently ignore the inherent complexities of ecosystem services and their responses to management actions, with the potential for unintended consequences that are difficult to predict. The ISO 31000 Risk Management Standard is a standardized framework to assess risks to forest AES and to prioritize management strategies to manage risks within tolerable ranges. The framework consists of five steps: establishing the management context, identifying, analyzing, evaluating and treating the risks. Challenges to implementing the framework include the need for novel models and indicators to assess forest change and resilience, quantification of linkages between forest practice and AES, and the need for an integrated systems approach to assess cumulative effects and stressors on forest ecosystems and AES. In the face of recent international agreements to protect forests, there are emerging opportunities for international leadership to address these challenges in order to protect both forests and AES.
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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 | 0.004 |
| 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 | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".