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Record W3189289632 · doi:10.1061/9780784483619.050

Welcome Neighbors! Upgrading an Existing Regional Water System to Extend the Service Footprint

2021· article· en· W3189289632 on OpenAlexaffabout
Breagh Peel, Stephan Weninger, Johnny Ke

Bibliographic record

VenuePipelines 2021 · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Reuse
Canadian institutionsStantec (Canada)Red Deer Polytechnic
Fundersnot available
KeywordsPotable waterFootprintRecreationService (business)TruckTransport engineeringEnvironmental scienceService systemComputer scienceWater resource managementBusinessEnvironmental engineeringEngineeringGeographyAutomotive engineering

Abstract

fetched live from OpenAlex

Originally designed to service a defined set of communities in Parkland and Lac Ste Anne Counties west of Edmonton, AB, the service footprint of the West Interlake District (WILD) Regional Water System has been expanded multiple times in under 10 years to service additional communities, First Nations, and recreational developments in need of an assured, high quality source of water. From an original system length of 35 mi (56 km), the system length has more than doubled, and capacity doubled with construction expected to be completed in 2021. The paper outlines how the system was originally planned and how changes to demands changed the system hydraulics and necessitated additional potable water reservoirs, booster stations, as well as the retrofit of existing facilities. The paper will also discuss how truck fill stations were incorporated throughout the system design to enable the affordable provision of potable water servicing to area farms throughout the region.

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)0.001
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.154
Threshold uncertainty score0.516

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.1540.038

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 head0.037
GPT teacher head0.259
Teacher spread0.222 · 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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