Early Design Clustering Method Considering Equitable Daylight Distribution in The Adaptive Re-Use of Heritage Buildings
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 re-use of existing buildings is gaining importance worldwide in the context of the carbon reduction efforts. In the case of Québec City there is a large number of heritage buildings that are currently unused. There are ongoing projects to breathe new life in these buildings, mainly by converting them in residential units. At the same time there is a growing preoccupation in Québec province towards energy efficiency and proper daylighting in both new and existing buildings. This is reflected in the emergence of new regulations concerning new buildings. In relation to existing buildings there are no regulations, but optimal daylight is a desired feature that can contribute significantly to the quality and attractiveness of newly designed spaces in the existing premises. In the case of heritage buildings, the additional conceptual challenge is to create properly daylit spaces while maintaining the character defining elements of the building, including facades and openings. Therefore, a digital workflow was developed to be integrated in the earliest schematic phase of design to ensure an equitable distribution of existing daylight in the newly created spatial units of heritage buildings. The method is based on an adapted constrained K-means clustering algorithm that works on daylight simulation data.
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 | 0.000 |
| 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