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Record W6929189621 · doi:10.4224/20857897

Do green buildings outperform conventional buildings? Indoor environment and energy performance in North American offices

2012· report· en· W6929189621 on OpenAlexaffvenueabout

Bibliographic record

VenueNPARC · 2012
Typereport
Languageen
FieldBusiness, Management and Accounting
TopicOptics and Image Analysis
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsCeiling (cloud)Energy performanceThermal comfortSample (material)WorkstationEfficient energy useEnergy consumptionPost-occupancy evaluationOffice workers

Abstract

fetched live from OpenAlex

A comprehensive post-occupancy investigation of the performance of “green” and “conventional” office buildings has been completed. The study included occupant surveys and physical building and energy use data collected from 24 buildings (12 green, 12 conventional) across Canada and the northern US. Occupants completed a questionnaire with items related to environmental satisfaction, job satisfaction and organizational commitment, health and well-being, environmental attitudes, and commuting behaviour. In total we recorded valid surveys from 2545 occupants. In addition, we conducted on-site physical measurements at each building. At a sample of workstations we collected data on prevailing thermal conditions, air quality, acoustics, and lighting. In addition, we recorded workstation size, ceiling height, window access and shading, electric lighting system, and surface finishes. In total we recorded valid data from 974 workstations.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.085
Threshold uncertainty score0.169

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.017
GPT teacher head0.217
Teacher spread0.200 · 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 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

Citations10
Published2012
Admission routes3
Has abstractyes

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