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Record W3037300151 · doi:10.22215/etd/2020-14012

Water Allocation in Southern Alberta

2020· article· en· W3037300151 on OpenAlexaffabout
Anteneh Belayneh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsCarleton University
Fundersnot available
KeywordsFungibilityWater tradingNatural resource economicsFarm waterWater resource managementWater resourcesBusinessWater conservationEnvironmental scienceEconomicsEcologyFinance

Abstract

fetched live from OpenAlex

This dissertation examines water allocation in southern Alberta and develops a fungibility framework for water rights and examines a range of characteristics impacting the transferability of water rights in southern Alberta.Fungibility is the degree to which water rights are homogenous.The fungibility of water rights is impacted by the number of different water rights within a given basin, the way these rights are defined, limitations on their transferability and how secure they are.Recognizing the characteristics that impact fungibility and ensuring water rights are as homogenous as possible makes these water rights easier to transfer between water users and may ensure a more effective water transfer system in southern Alberta.Even though water license transfers have been limited in terms of amount and volume, the total economic benefit from water use in the South Saskatchewan River Basin can increase through water reallocation.This dissertation illustrates how, without institutional constraints, basin-wide economic benefits could increase and shows what a potential reallocation of water allocations in the region looks like.This reallocation could see more water diverted to municipal users at the expense of agricultural users.Finally, the political influence of irrigators is examined.Efforts at reallocating water away from irrigation will be limited unless coalitions develop that can minimize the influence of irrigation's proponents.First and foremost, I would like to thank my thesis supervisor, Prof. Stephan Schott, for all his support and guidance.His insights, encouragement and ability to keep me on track were instrumental throughout the duration of this work.He always provided me with useful advice and comments on how to best improve my work.Thank you for your patience

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.211

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.009
GPT teacher head0.162
Teacher spread0.153 · 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 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
Published2020
Admission routes2
Has abstractyes

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