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Record W2614755838 · doi:10.5942/jawwa.2017.109.0100

Value Propositions of the Water Footprint Concept for Sustainable Water Utilities

2017· article· en· W2614755838 on OpenAlexaff
Mohammad Badruzzaman, Tim Hess, Heather Smith, Sophie Upson, Joseph G. Jacangelo

Bibliographic record

VenueAmerican Water Works Association · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicWater-Energy-Food Nexus Studies
Canadian institutionsStantec (Canada)
Fundersnot available
KeywordsWater useBenchmarkingWater resourcesIntegrated water resources managementWater conservationEnvironmental economicsBusinessSustainable developmentEnvironmental resource managementStakeholderEcological footprintSustainabilityFootprintEnvironmental planningEnvironmental scienceWater resource managementEconomics

Abstract

fetched live from OpenAlex

The water footprint concept has been used by agricultural, commercial, and industrial water users to measure and report their water consumption, assess the magnitude of potential environmental impacts arising from this consumption, and identify opportunities for risk mitigation strategies that promote sustainable water use. However, water and wastewater utilities have not studied and documented the application of this concept in the same manner that other industries have. This article summarizes the growing body of information on the water footprint concept and the opportunities for integrating the concept into water utility planning efforts as a broader means of achieving and maintaining sustainable communities. The application of the water footprint concept for capital improvement planning, water resources decision‐making, operational benchmarking, and stakeholder communications is discussed, as is how the methodology, developed by the International Organization for Standardization, can be used for a water utility.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.524
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.006
GPT teacher head0.215
Teacher spread0.210 · 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.

Study designBench or experimental
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
Published2017
Admission routes1
Has abstractyes

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