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Record W4411346161 · doi:10.1139/facets-2024-0278

The future Prairie Pothole Region: scenarios of change

2025· article· en· W4411346161 on OpenAlexafffundvenue
Donald Selby, Philip A. Loring, Helen M. Baulch

Bibliographic record

VenueFACETS · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsUniversity of GuelphUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Saskatchewan
KeywordsPothole (geology)GeographyEnvironmental scienceEnvironmental resource managementEnvironmental planningPhysical geographyGeology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.207
Threshold uncertainty score0.220

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.283
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes3
Has abstractyes

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