PERFORMANCE OF SUSTAINABLE BUILDINGS IN COLDER CLIMATES
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.
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it