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Record W1585588580 · doi:10.1080/09613218.2015.1036227

Learning from failure: understanding the anticipated–achieved building energy performance gap

2015· article· en· W1585588580 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueBuilding Research & Information · 2015
Typearticle
Languageen
FieldEngineering
TopicSustainable Building Design and Assessment
Canadian institutionsUniversity of British Columbia HospitalUniversity of British Columbia
Fundersnot available
KeywordsSustainabilityKey (lock)Energy (signal processing)Architectural engineeringEnergy performanceBuilding scienceEfficient energy useBuilding designComputer scienceProcess managementBusinessEnvironmental economicsEngineeringEconomicsComputer security

Abstract

fetched live from OpenAlex

Over the past 20 years a number of studies have identified and provided explanations for a significant ‘performance gap' between designed and actual energy performance of buildings. The anticipated and achieved energy performance of an advanced, innovative building that aspired to net-positive energy performance is studied: the Centre for Interactive Research on Sustainability (CIRS) building at the University of British Columbia in Vancouver, Canada. Selected performance ‘failures’ that became evident during operation of CIRS are studied for how they were discovered and the efforts required for their resolution: the energy systems and associated controls and monitoring. The key findings show the barriers were neither economic nor technical. Instead, the primary impediments were institutional regimes – arising from the ways that various life-cycle stages were specified, contracted and implemented. The key issues emphasize the importance of having meaningful and effective building energy monitoring capabilities, an understanding of energy system boundaries in design and analysis, crossing the gaps between different stages of a building life cycle, and feedback processes throughout design and operation. The disclosure of ‘failure’ and lessons learned is a valuable contribution to subsequent advancement for the building stakeholders and the wider professional and research communities.

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)0.003
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.322
Threshold uncertainty score0.821

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0000.001
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 head0.108
GPT teacher head0.316
Teacher spread0.208 · 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