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Record W2531621321 · doi:10.2166/wpt.2015.094

Water user survey on expectations of service in Guelph, ON, Canada

2015· article· en· W2531621321 on OpenAlexaffabout
Rebecca Dziedzic, Bryan Karney

Bibliographic record

VenueWater Practice & Technology · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Environmental Valuation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBusinessOpenness to experienceWater conservationWater utilityEnvironmental economicsService (business)ScarcityWater qualityMarketingQuality (philosophy)Environmental resource managementEnvironmental planningWater resourcesWater supplyEnvironmental scienceEnvironmental engineeringPsychologyEconomics

Abstract

fetched live from OpenAlex

A survey was developed and conducted with residential water users in the City of Guelph, ON, Canada, with the objective of assessing their awareness, preferences, concerns, motivations, and priorities. The overall goal of this data is to improve the water system on different fronts: infrastructure, conservation programs, communication with users, and long-term strategies. Results highlight the local concerns with water scarcity, currently addressed by conservation programs, as well as water quality, aging infrastructure, and costs. Correlations between user type and answers were seldom found, showing that different residential customer segments share concerns and motivations. Even so, feedback must be sought from all customer segments, residential as well as industrial, commercial, and institutional, through different channels. The findings will allow the utility to identify preferred solutions to current issues and openness to change, as well as gaps in user and utility knowledge.

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)0.003
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 score0.016
Threshold uncertainty score0.109

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.096
GPT teacher head0.239
Teacher spread0.143 · 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

Citations2
Published2015
Admission routes2
Has abstractyes

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