Impact of model detail on daylighting metrics in residential buildings
Bibliographic record
Abstract
Abstract Daylight metrics that shall guide performance-driven design must be applicable to models that reflect the limited knowledge at the time of planning decisions. In particular in residential buildings, the furnishing is beyond the control of such decisions and therefore unknown in the planning phase. The impact of model detail on illuminance- and luminance-based metrics is tested on a residential unit in Singapore. At two elevations, representing a work-plane and the eye level of a standing inhabitant, the metrics are computed from a) a detailed model incorporating furniture and shading devices, b) a model with only shading devices, and c) a minimalist model representing only the design decisions of the architect. As a potential approximation to the detailed model, d) a variant of the minimalist model with wall reflectance reduced to 0.20 is tested. The results for the three levels of detail are compared and indicate the significant impact of detail on all assessed daylight metrics.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".