Continuing wetland drainage: drivers, effects, and the role of science-based partnerships and understanding
Bibliographic record
Abstract
The prairie pothole region of North America has been described as a breadbasket for the world, and a ‘duck factory’ for North America, reflecting the tremendous ecosystem services associated with the vast agricultural lands, and millions of pothole wetlands in the region. Pressure to increase agricultural outputs and profitability has led to accelerating wetland drainage, leading to a wicked problem worsened by the lack of enforcement of existing policy and vast numbers of unlicensed drainage projects.Responsive to questions from partners, we embarked on a multi-dimensional research program to understand options for managing the drainage of prairie wetlands. Novel ecosystem service models, based upon the unique hydrology of the region demonstrate important threats of drainage, including flooding, increased nutrient export, and profound impacts on habitat and biodiversity. Expert-driven scenario development also shows potential for dire changes in the region associated with climate and land use change. Importantly, there are fundamental differences among stakeholders in their understanding of how the system works, leading to divergent interpretations of the benefits, and consequences of drainage. Not surprisingly, wetland drainage has led to conflict, as power dynamics and the effort to coordinate drainage approvals have contributed to winners, losers, and those without voice. However, in some cases drainage conflicts may simply be a resurgence of long-standing disputes over varied issues. While interdisciplinary and transdisciplinary work has helped understand the context of wetland drainage in this vast geographic area, problems, and possible ways forward, a weak policy environment is expected to persist because of local ideologies for limited government intervention, a highly politicised environment with strong power imbalances and strong government support of the agriculture sector. Our work, guided by stakeholders since project inception to inform decision-making, demonstrates significant impacts of drainage with tangible policy implications, yet concerns have emerged about the role of science and representation of science in the policy process. While transdisciplinary research has clear benefits, it is not a panacea in complex, multi-sector, and conflict-prone arenas such as this.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".