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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

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.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.696
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.017
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