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Residents’ Assessment of an Urban Outdoor Water Conservation Program in Guelph, Ontario<sup>1</sup>

2007· article· en· W2107080366 on OpenAlexaffabout
Christine Atwood, Reid Kreutzwiser, Rob de Loë

Bibliographic record

VenueJAWRA Journal of the American Water Resources Association · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsLawnEnforcementWater conservationEnvironmental planningPromotion (chess)BusinessEnvironmental resource managementProgram Design LanguageEnvironmental scienceEnvironmental protectionEnvironmental healthGeographyWater resourcesEngineeringPolitical scienceMedicine

Abstract

fetched live from OpenAlex

Abstract: Many municipalities have implemented demand management of outdoor water use. Measures such as restrictions on lawn watering and promotion of xeriscaping are effective in reducing water demand during summer months, especially during dry spells. However, little research examines a key factor shaping the success of these programs: residents’ perceptions of and satisfaction with such conservation measures. This article describes an urban outdoor water conservation program in Guelph, Ontario, assesses that program from the perspective of residents, and explores socio‐economic, attitudinal and other factors associated with residents’ assessment of the program. A survey of Guelph residents revealed broad support for the program, which includes restrictions on various outdoor water uses and, under certain circumstances, a ban on lawn watering. However, there was much uncertainty among residents about the effectiveness of the program in reducing water use and the effectiveness of program enforcement. Key factors influencing residents’ assessment of the program were neighborhood, gender and environmental attitude. Implications for the design and implementation of outdoor water conservation programs are discussed, including the importance of better communication of information on program effectiveness and enforcement.

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)0.003
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.010
GPT teacher head0.252
Teacher spread0.242 · 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 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

Citations24
Published2007
Admission routes2
Has abstractyes

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