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Record W2068615949 · doi:10.1081/ge-120027882

Energy Considerations in Design for Environment: Appropriate Energy Selection and Energy Efficiency

2004· article· en· W2068615949 on OpenAlexafffundabout
Marc A. Rosen

Bibliographic record

VenueInternational Journal of Green Energy · 2004
Typearticle
Languageen
FieldEngineering
TopicSustainable Building Design and Assessment
Canadian institutionsUniversity of Ontario Institute of Technology
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEfficient energy useEnergy engineeringWork (physics)Energy (signal processing)Energy accountingEnvironmental economicsEnergy conservationEnergy consumptionEnergy transformationSelection (genetic algorithm)EngineeringSystems engineeringComputer scienceMechanical engineeringEconomics

Abstract

fetched live from OpenAlex

The design aspects of energy selection and energy efficiency are described. These topics are key components of Design for Environment, and thus are important considerations in industrial ecology. In the article, background material is presented on energy forms, sources, carriers and conversion technologies. Then, energy use in countries, regions and sectors is discussed, and the impact of energy use on the environment described. Efficiencies and other measures of merit for energy use are presented, and design for energy selection and for energy efficiency are described, highlighting energy-related design factors for pollution prevention. Finally sustainable development and its relation to energy and efficiency are discussed, and a case study is presented.

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)0.004
metaresearch head score (Gemma)0.005
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: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.001
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 head0.011
GPT teacher head0.221
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 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
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

Citations14
Published2004
Admission routes3
Has abstractyes

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