MétaCan
Menu
Back to cohort
Record W1479806137

Energy Efficiency Standards and Labels in North America: Opportunities for Harmonization

2008· article· en· W1479806137 on OpenAlexfundaboutno aff
Stephen Wiel

Bibliographic record

VenueeScholarship (California Digital Library) · 2008
Typearticle
Languageen
FieldEngineering
TopicSustainable Building Design and Assessment
Canadian institutionsnot available
FundersNatural Resources CanadaComisión Nacional de Actividades EspacialesU.S. Department of Energy
KeywordsHarmonizationEfficient energy useConditionersTest (biology)Resource (disambiguation)BusinessEnvironmental economicsOperations managementComputer scienceEnvironmental scienceEngineeringEconomicsEnvironmental engineering
DOInot available

Abstract

fetched live from OpenAlex

To support the North American Energy Working Group's Expert Group on Energy Efficiency (NAEWG-EE), USDOE commissioned the Collaborative Labeling and Appliance Standards Program (CLASP) to prepare a resource document comparing current standards, labels, and test procedure regulations in Canada, Mexico, and the United States. The resulting document reached the following conclusions: Out of 24 energy-using products for which at least one of the three countries has energy efficiency regulations, three products -- refrigerators/freezers, split system central air conditioners, and room air conditioners -- have similar or identical minimum energy performance standards (MEPS) in the three countries. These same three products, as well as three-phase motors, have similar or identical test procedures throughout the region. There are 10 products with different MEPS and test procedures, but which have the short-term potential to develop common test procedures, MEPS, and/or labels. Three other noteworthy areas where possible energy efficiency initiatives have potential for harmonization are standby losses, uniform endorsement labels, and a new standard or label on windows. This paper explains these conclusions and presents the underlying comparative data.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.911
Threshold uncertainty score0.817

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 studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
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 head0.021
GPT teacher head0.212
Teacher spread0.190 · 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 designNot applicable
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

Citations1
Published2008
Admission routes2
Has abstractyes

Explore more

Same venueeScholarship (California Digital Library)Same topicSustainable Building Design and AssessmentFrench-language works237,207