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Record W7084205752

Insights Into Saskatchewan's Water Market Capacity: A Water Market Readiness Assessment for Irrigation Allocations in the South Saskatchewan River Basin

2025· article· en· W7084205752 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity Library (University of Saskatchewan) · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPotato Plant Research
Canadian institutionsnot available
Fundersnot available
KeywordsIrrigationLegislationWater scarcityDrainage basinWater resourcesResource (disambiguation)Water useIrrigation managementResource allocation
DOInot available

Abstract

fetched live from OpenAlex

This thesis examines Saskatchewan’s capacity to support market-based water allocation by applying the Water Market Readiness Assessment (WMRA) to water allocation for irrigation within the South Saskatchewan River Basin. By leveraging a regionally embedded case study approach, the research explores whether the legal, administrative, and technical foundations exist to facilitate voluntary water trading. The research highlights Saskatchewan’s centralized governance, satisfactory hydrological monitoring, and growing policy interest in adaptive water management as advantages for pursuing change. However, existing constraints present significant challenges, including legislation that restricts the separation of water licenses from their original allocation, limited infrastructure for water trade, and low appetite for market-based water management reforms. Saskatchewan provides a unique case study for the WMRA as it is also in relatively early stages of irrigation development and experiences less water scarcity than in other regions where markets have been implemented. This assessment identifies policy entry points for gradual institutional change such as the use of irrigation districts as pilot sites rather than immediate implementation. Recommendations for improving the WMRA are also presented, focusing on more effectively integrating complementary resource considerations and legacy issues in its assessment.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.188
Teacher spread0.177 · 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.

Study designQualitative
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 routes1
Has abstractyes

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