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Record W3189929955 · doi:10.1061/9780784483619.048

The Story behind Alberta’s Longest Regional Water System

2021· article· en· W3189929955 on OpenAlexaffabout
Stephan Weninger

Bibliographic record

VenuePipelines 2021 · 2021
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsStantec (Canada)
Fundersnot available
KeywordsComputer scienceEnvironmental science

Abstract

fetched live from OpenAlex

Established fewer than 15 years ago, the Shirley McClellan Regional Water Services Commission (SMRWSC) is a municipal organization established by the Province of Alberta, and it has developed the longest regional water system in Alberta. The 150 mi, or 250 km, transmission system was constructed in five phases and services over a dozen communities, with a Phase 6 expansion scheduled for completion in September 2021. Rural water distribution systems developed by the Commission’s various member communities have extended over 100 mi (160 km) in that time to create an extensive water network that provides sustainable water servicing to a large geographical area. This paper provides a history of the system from the early days of planning the full network to the buildout of the various phases, the unique pumping station at the heart of the system that services four independent pressure zones, and it discusses how design approaches and construction methodologies have changed over that time based on new and improved materials, the emergence of horizontal directional drilling as a cost effective competitor to open cut installations, and changes to the basic design philosophies behind control networks.

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)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.064
Threshold uncertainty score0.461

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0120.009
Scholarly communication0.0110.003
Open science0.0020.003
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0090.001

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 head0.008
GPT teacher head0.173
Teacher spread0.166 · 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 designNot applicable
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
Published2021
Admission routes2
Has abstractyes

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