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Record W2058049988 · doi:10.2166/ws.2010.137

Key decisions for sustainable utility energy management

2010· article· en· W2058049988 on OpenAlexaff
Steven A. Conrad, Murray Hall, Stephen Cook, Jack Geisenhoff

Bibliographic record

VenueWater Science & Technology Water Supply · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicWater-Energy-Food Nexus Studies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsWork (physics)Key (lock)Energy managementEnvironmental economicsDecision support systemSustainabilitySustainable energyBusinessProcess managementEnvironmental resource managementEnergy (signal processing)Computer scienceRisk analysis (engineering)EngineeringRenewable energyEnvironmental scienceEconomics

Abstract

fetched live from OpenAlex

This paper summarizes the outcomes of the international sustainable energy management workshop as part of the Water Research Foundation project “Decision Support System for Sustainable Energy Management.” This paper provides a review of key decisions and barriers to water utility energy management. This paper also provides a discussion of a decision framework to address sustainable energy management in the water/wastewater industry. This work represents a 17 utility, international cooperative project, aimed at creating a Decision Support System (DSS). When concluded, the assembled tool is expected to help identify energy, greenhouse, and cost-saving approaches that will be of wide practical benefit to water utilities.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
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 score0.202
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.004
Scholarly communication0.0000.001
Open science0.0020.005
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.211
Teacher spread0.204 · 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; both teacher heads agree on what is shown here.

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

Citations8
Published2010
Admission routes1
Has abstractyes

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