Learning How Buildings Work Is Crucial to Better Green Design
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 designers need far better feedback on how well their buildings work. Existing buildings offer a wealth of opportunities for designers to learn, and to improve future designs. A more comprehensive understanding of how existing buildings develop and change over time, and meet, or fail to meet, user expectations offers designers the opportunity to learn from existing buildings. Also, feedback loops are needed to ensure that designers learn lessons from built projects and apply them to future designs. In addition, there is a particular need to understand whether claimed “green buildings” really do meet the needs of occupants and reduce their environmental impacts. Assessing real building performance from both a technical and social perspective is one way of both raising the profile of issues that are important to building occupants, and of improving understanding of real building performance. Several new mechanisms have been proposed in recent years that offer the opportunity to re-establish some of the missing feedback mechanisms for designers. These can provide direct information on the performance of their designs potentially leading to better performing buildings environmentally, economically and socially. This can minimise problems and utilise those design features that work successfully, applying the laws of survival of the fittest. This paper reviews some of the recent initiatives to establish better feedback mechanisms.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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