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Record W6884628429 · doi:10.11575/prism/36021

Development Of A Feasibility Framework For Trickle Fill Water Distribution Projects

2018· other· en· W6884628429 on OpenAlexaboutno aff

Bibliographic record

VenuePRISM (University of Calgary) · 2018
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsInvestment (military)SubsidySustainable developmentGovernment (linguistics)Water supplyDistribution (mathematics)Sustainable regional developmentSustainability

Abstract

fetched live from OpenAlex

Lacking economies of scale, most Canadian small water systems do not have the financial capability to maintain sustainable operations, resulting in hundreds of drinking water advisories every year. In Alberta, an alternative to small rural systems has been emerging due to government investment in regionalization. With the “trickle fill” option, treated water is provided from regional supplies via small-diameter piping. This study outlines a feasibility assessment framework for trickle fill regionalization and conducts a case study for an area in Rocky View, Alberta. The case study develops a concept design and calculates the corresponding average end-user cost to be $165.03/month/home with ~80% of the cost due to regional system owner rates. While the study finds that the option is more sustainable than existing systems in the case area, either subsidies or a reduction in connection fees would be required to make it affordable for homeowners.

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)0.015
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.034
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0060.003
Science and technology studies0.0030.003
Scholarly communication0.0070.006
Open science0.0030.005
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0230.002

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 head0.027
GPT teacher head0.242
Teacher spread0.215 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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
Published2018
Admission routes1
Has abstractyes

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