A complex systems approach for multiobjective water quality regulation on managed wetland landscapes
Bibliographic record
Abstract
Abstract Management of wetland ecosystems that are tightly coupled with human systems typically requires balancing multiple objectives to ensure that a range of ecosystem services are provided for the benefit of society. We describe how adopting a complex systems approach may provide managers with the appropriate conceptual tools to achieve social and ecological objectives in a multifunctional wetland landscape. We illustrate the applicability of the approach using the Grasslands Ecological Area ( GEA ) in California as a case study. Human intervention has shaped and reshaped the GEA over the past century, affecting the ability of the landscape to provide ecosystem services. Ecological disaster in the 1980s precipitated transformative change in the management system toward an approach that adopts many of the recommended actions for complexity. Present‐day management, which balances multiple social and ecological objectives, has led to improved water quality, restoration of wetland habitats, and a general increase in system complexity at the landscape scale. New research and real‐time monitoring systems facilitate adaptive management and heterogeneous responses of wetland management entities. We argue that taking a complex systems approach to management in the GEA provides a common, and inclusive, conceptual model for all stakeholders and may lead to a more sustainable and ecologically resilient landscape over the long term.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".