Multi-Objective Optimisation of Passivhaus Buildings in a Social Housing Context
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
The housing crisis within the UK continues with growing private housing rental prices and increasing levels of homelessness. This situation has been driven by the homogeneous development of housing tenures under-supplying in-demand social and affordable homes. Previous work has seen the implementation of multi-objective optimisation within a broad range of building performance simulation software. The present work is novel in the implementation of a multi-objective decision support framework within software used for compliance with the low energy Passivhaus standard. This use of evidence-based decision support could enable local authorities to make better informed decision in relation to large development seeking Passivhaus compliance. Results indicate that different optimal solutions are present depending on the criteria used to meet the standard. This means that it is important to select early in the design process either the heating load, or annual heating demand criteria if optimisation techniques are to be applied based on the Passivhaus certification criteria to the design.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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