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.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".