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Record W7083185488 · doi:10.1139/facets-2024-0273

Learning from school buildings: energy efficiency and user comfort in Metro Vancouver elementary schools

2025· article· en· W7083185488 on OpenAlexfundvenueaboutno aff

Bibliographic record

VenueFACETS · 2025
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsnot available
FundersReal Estate Foundation of British Columbia
KeywordsEfficient energy useBuilding envelopeEnergy conservationProject commissioningHeat recovery ventilationGlazingGreenhouse gasVentilation (architecture)

Abstract

fetched live from OpenAlex

In this study, schools built after 2000 in Metro Vancouver, Canada, showed an encouraging trend towards lower carbon emissions and energy use intensity. In 17 case studies, heat pumps, decentralized ventilation systems, and higher glazing-to-wall ratios were positively associated with elementary school energy performance. Schools with heat recovery ventilators produced fewer carbon emissions than those without but had similar energy performance. Interviews with school staff highlighted challenges with heat pumps and commissioning, user coping strategies for overheating, and the important role of “energy champions”. Recommendations include using energy modelling to consider the impact of glazing area and envelope U-value; installing decentralized or zoned ventilation systems, heat pumps, blinds, shutters, dimmable lights, operable windows, fans, and heat recovery ventilators; providing regular commissioning of mechanical systems; and training building operators and building users. Further research is required to determine the impact of onsite building operators on energy consumption. This study is the first to examine associations between design, building operations, and energy performance of schools in Western Canada.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.830
Threshold uncertainty score0.531

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 communication0.0000.000
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 head0.006
GPT teacher head0.217
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.

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

Citations0
Published2025
Admission routes3
Has abstractyes

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