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Record W2218867822

Irrigators’ Perception and Intention towards Water Re-allocation Policies in Southern Alberta

2014· dissertation· en· W2218867822 on OpenAlexaboutno aff
Mathew Peter Hall

Bibliographic record

VenueOpen ULeth Scholarship (OPUS) (University of Lethbridge) · 2014
Typedissertation
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)SkepticismOrder (exchange)PerceptionAgricultureNatural resource economicsBusinessWater useWater conservationIrrigationEnvironmental resource managementEnvironmental planningWater resource managementEnvironmental economicsEconomicsGeographyEnvironmental scienceFinanceEcology
DOInot available

Abstract

fetched live from OpenAlex

A necessary precursor to irrigated agriculture in southern Alberta is the availability of massive quantities of water. Many suggest that water demand will rise substantially in the decades to come. These projections also come at a time of increasing environmental awareness in Alberta, leading some to advocate that more water should be secured for environmental purposes. The Alberta government enabled inter-sectoral water transfers as a way to re-allocate water in order to satisfy growing demand. This has raised skepticism among the irrigation community over the use of water transfers as a way to satisfy future demand. The research presented in this thesis approaches this issue by examining the factors that influence irrigators’ perceptions towards using water transfers as a way to re-allocate water to other uses. The findings reveal that if the government is relying on water transfers as the primary way to re-allocate water in the future, it must address irrigators’ skepticism, and create conditions that promote transfers as a preferable option.

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)0.002
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.187
Threshold uncertainty score0.376

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.003
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.016
GPT teacher head0.221
Teacher spread0.205 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

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