PERFORMANCE OF SUSTAINABLE BUILDINGS IN COLDER CLIMATES
Bibliographic record
Abstract
ABSTRACT Building performance evaluations (BPEs) were carried out for nine Canadian green buildings using a standardised assessment framework. The aim was to explore and measure the discrepancies between the operational performance of the buildings and their predicted performance, as well as to identify lessons for their owners, design teams and the construction industry. The objective of this paper is not to report individual buildings in detail (we refer the reader to the individual building reports) but to report on some general lessons that came from doing this study. Overall these buildings performed well compared to benchmarks. However, the findings suggest that occupancy is not well understood and often incorrectly predicted during design, and that this affects various aspects of performance, including energy and water use. Also energy and water use modelling is often undertaken principally for building code/green rating compliance purposes and does not necessarily represent an accurate prediction of likely operational use. Combined with variations in occupancy this can lead to considerable discrepancies in performance from the modelled values. This may be understood by experts but is often misleading to building owners and others. Water use is often not well predicted and also not carefully managed in buildings and there is a lack of understanding of what constitutes good water performance. Overall, it is important to recognise that each building has its own individual “story” that provides necessary context for effective management and improvement of the building during its ongoing life. It is proposed that a BPE process allows that context to be better understood, and enables more effective decision making about building management, improvements, occupant satisfaction, energy use, etc.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".