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Record W1547807862 · doi:10.1080/07900627.2011.621103

Changing Roles in Canadian Water Management: A Case Study of Agriculture and Water in Canada's South Saskatchewan River Basin

2011· article· en· W1547807862 on OpenAlexaffabout
Darrell R. Corkal, Harry Diaz, David Sauchyn

Bibliographic record

VenueInternational Journal of Water Resources Development · 2011
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsUniversity of ReginaAgriculture and Agri-Food Canada
Fundersnot available
KeywordsAgricultureStructural basinWater resource managementAdaptive managementDrainage basinIrrigated agricultureGeographyWater useWater resourcesEnvironmental resource managementEnvironmental planningBusinessEnvironmental scienceEcologyGeologyArchaeology

Abstract

fetched live from OpenAlex

This paper explores changing roles in Canadian water management, by focusing on a case study of agriculture and water in Western Canada. Challenges in water management include unequal adaptive capacity, gaps in water and climate data, locally relevant options, short- and long-term planning, among others. This empirical study offers insight for improved water management decision-making for all regions. There is a need for improving and integrating water management with climate scenarios, collecting more and better water/climate data, improving water governance and long-term planning, and developing strong communication channels between governance organizations and local communities. Positive trends towards effective and adaptive water management include the incorporation of watershed groups, basin planning, and the use of multidisciplinary approaches to guide decision-making.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.104
Threshold uncertainty score0.756

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0200.006
Scholarly communication0.0040.001
Open science0.0020.003
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.164
Teacher spread0.157 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations25
Published2011
Admission routes2
Has abstractyes

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