Daylight availability in top-lit atria: prediction of skylight transmittance and daylight factor
Bibliographic record
Abstract
Atrium and skylight shapes are important architectural design elements that influence daylight availability within the space and, therefore, lighting energy consumption. There is a lack of prediction models for skylight transmittance. and daylight availability in atria. A new concept was developed to predict the diffuse transmittance of skylights. A skylight shape is converted into a representative shape through a shape parameter. Generic formulae for the skylight diffuse transmittance were developed under different sky conditions. A zonal model combined with the flux transfer method was developed to predict daylight availability in top-lit atria through the predictions of the average daylight factor (DF) at the atrium floor and ceiling (non-glazed portion of the roof), and the local DF normal to walls. The DF model was compared with currently available models derived from theory and experiments under artificial skies. The results showed that the computed e transmittance for translucent skylights. under real partly cloudy or dear skies may reach up to 33% in summer and 56% in winter higher than that under CIE overcast skies. The developed zonal model yielded very dose results to the models based on the nnite-dement method. However, models based on physical scale measurements lack general consensus among themselves, and may produce average DF values at floor level up to 43% higher than those produced by the zonal model. Physical scale models may also yield local DF values normal to walls up to 50% lower than those predicted by the zonal model.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".