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Record W2037690520 · doi:10.4296/cwrj3204315

Urban Water Futures: A Multivariate Analysis of Population Growth and Climate Change Impacts on Urban Water Demand in the Okanagan Basin, BC

2007· article· en· W2037690520 on OpenAlexvenueaboutno aff
Tina Neale, Jeff Carmichael, Stewart Cohen

Bibliographic record

VenueCanadian Water Resources Journal / Revue canadienne des ressources hydriques · 2007
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsnot available
Fundersnot available
KeywordsClimate changeWater resourcesPopulation growthEnvironmental sciencePopulationWater resource managementWater supplyEnvironmental resource managementGeographyEnvironmental engineeringEcology

Abstract

fetched live from OpenAlex

Climate change is expected to have a significant impact on water availability and demand in many regions. In regions where significant population growth is expected additional pressure on water resources will likely result. This paper describes a scenario-based approach to integrating climate change impacts with other drivers for the purpose of bridging the gap between global climate change and local water management adaptation. Scenarios of future residential water demand account for population growth, climate change, housing type and demand side management, and compare these demands to licensed supply. Three case studies within the Okanagan Basin of British Columbia were chosen in order to assess regions of differing aridity, current water use, and community size: the Town of Oliver, City of Penticton and City of Kelowna water utility. In all cases, water demands are projected to increase, exceeding licensed supplies in high growth scenarios. However, these increases can be offset to a large degree by demand management measures.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.247
Threshold uncertainty score0.496

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.011
GPT teacher head0.197
Teacher spread0.186 · 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

Citations15
Published2007
Admission routes2
Has abstractyes

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