The future Prairie Pothole Region: scenarios of change
Bibliographic record
Abstract
Agriculture has driven important changes within the Prairie Pothole Region, for example, with major yield increases in recent decades. Changes are ongoing, with widespread wetland drainage in areas, and evidence of growing rural conflict over drainage and other issues. Research to develop common understandings of change can help address conflict and develop shared vision. Here, we used expert elicitation via a Delphi process to develop scenarios of the future, understand potential trajectories of change, drivers, and effects, and develop boundary objects that can be used in building dialogue. Using a grounded and inductive approach we identified three organizing principles that drove scenarios—future agricultural growth, the future regulatory environment, and climate change. Although six scenarios were developed, only two achieved consensus as credible (i.e., ≥75% indicating this was supported by current understanding of the system and its changes). Both scenarios ( Agriculture as Usual and Unmitigated Climate Change) are typified by limited regulation. All scenarios suggest rural population will continue to decrease (or “decrease or stay the same”; consensus reached in four of six scenarios). Relatively high agreement was also seen for changes in social license to farm, flood risk, wetland extent, and biodiversity, with lesser agreement on economic indicators.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".