The benefits of light shelves over the daylight illuminance in office buildings in Toronto
Bibliographic record
Abstract
Modern envelope technologies and architectural trends often encourage the adoption of large glazing surfaces. Light shelves are then proposed to reduce glare complaints, while providing better indoor daylight distribution. In this paper, the benefits of light shelves over the illuminance levels in office buildings in Toronto are evaluated. The useful daylight illuminance was used as the metric of analysis in this study. Annual simulations for buildings with different window-to-wall ratios were compared. Moreover, the effects of different window shapes, façade orientation and external obstructing elements were investigated. Results show that in the context of analysis, light shelves increase the useful daylight illuminance values mainly in the first 6 m from the windows and provide a more homogeneous distribution of the daylight. Window-to-wall ratios above 35% consistently result in increasing glare risks. This study indicates that narrow full-height windows provide better daylighting compared to shorter but wider windows. The west orientation shows higher useful daylight illuminance compared to the south-facing ones, although light shelves are far less beneficial when applied to windows but not facing south. Finally, the illuminance levels in buildings with different obstruction angles of the façade are presented in order to provide a comprehensive analysis about the benefits of adopting light shelves in office buildings in the urban context of Toronto, Canada.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".