Heterogeneous changes to North America prairie pothole wetlands under future climate
Bibliographic record
Abstract
Numerous wetlands in North America’s Prairie Pothole Region (PPR) provide important ecosystem services to surrounding areas, yet are threatened by climate and land-use changes. Understanding the impacts of climate change on prairie wetlands is critical to effective conservation planning. In this paper, we construct a wetland model with surface water balance (soil water content) and ecoregions and apply it to predict future wetland distribution under a climate change scenario. The future climate forcing is from a dynamical downscaling approach of a high-resolution convection-permitting regional climate model. The results show that the impacts of climate change on wetland extent are spatially heterogeneous and seasonally varied. The future wetter climate in the western PPR will favor increased wetland abundance in both spring and summer. In the eastern PPR, particularly in the moist mixed grassland and aspen parkland, the wetland area will increase in spring but experience enhanced declines in the summer due to strong evapotranspiration. When combined with historical patterns of anthropogenic drainage, results suggest that diverse conservation and restoration strategies will be needed. The outcomes of this study will be useful to conservation agencies to ensure that current investments will continue to provide good conservation returns in the future.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".