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Record W2151039274 · doi:10.1061/40753(171)135

Sustainable Infrastructure: Reconstruction of the Little Mountain Reservoir

2005· article· en· W2151039274 on OpenAlexaffabout
Arun Sukumar, Frank Hüber

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsCapital Regional District
Fundersnot available
KeywordsDemolitionConstructabilitySustainabilityScheduleMetropolitan areaCivil engineeringGeographyEnvironmental planningEnvironmental scienceEngineeringArchaeologyComputer science

Abstract

fetched live from OpenAlex

The Greater Vancouver Water District (GVWD) owns and operates the water transmission system that serves approximately 2 million residents in the Greater Vancouver area, located in British Columbia, Canada with an average water consumption of 1.2 million cu. m per day, making the system one of the largest in Canada. The Little Mountain Reservoir located in Queen Elizabeth Park, in the City of Vancouver is the largest and most important of the 22 reservoirs in the GVWD system. The reservoir was originally constructed as an open basin in 1910 and a roof structure was added in the mid 1960s. Because of seismic and structural deficiencies of the structure, it was decided to demolish the reservoir and construct a new one on the same site with increased capacity and enhanced operational flexibility. Demolition began in September 2002 and construction of the new facility was completed by December 2003 on schedule, within budget. This paper summarizes the options evaluated before deciding to reconstruct the reservoir and details of GVWD's seismic performance criteria. Design features of this 175 million litres (38.5 million gallons) capacity reservoir will be presented with the challenges and constructability issues faced by the project team in building a major infrastructure in an urban park setting. The paper will show how the principles of sustainability were applied effectively by addressing social, environmental and economic issues in a balanced manner.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.297
Threshold uncertainty score0.137

Codex and Gemma teacher scores by category

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

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 head0.003
GPT teacher head0.171
Teacher spread0.168 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations2
Published2005
Admission routes2
Has abstractyes

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