Early Design Clustering Method Considering Equitable Daylight Distribution in The Adaptive Re-Use of Heritage Buildings
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.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".