Wetlands In a Changing Climate: Science, Policy and Management
Bibliographic record
Abstract
Part 1 of this review synthesizes recent research on status and climate vulnerability of freshwater and saltwater wetlands, and their contribution to addressing climate change (carbon cycle, adaptation, resilience). Peatlands and vegetated coastal wetlands are among the most carbon rich sinks on the planet sequestering approximately as much carbon as do global forest ecosystems. Estimates of the consequences of rising temperature on current wetland carbon storage and future carbon sequestration potential are summarized. We also demonstrate the need to prevent drying of wetlands and thawing of permafrost by disturbances and rising temperatures to protect wetland carbon stores and climate adaptation/resiliency ecosystem services. Preventing further wetland loss is found to be important in limiting future emissions to meet climate goals, but is seldom considered. In Part 2, the paper explores the policy and management realm from international to national, subnational and local levels to identify strategies and policies reflecting an integrated understanding of both wetland and climate change science. Specific recommendations are made to capture synergies between wetlands and carbon cycle management, adaptation and resiliency to further enable researchers, policy makers and practitioners to protect wetland carbon and climate adaptation/resiliency ecosystem services.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".