Bibliographic record
Abstract
This paper proposes and validates a daylighting design sequence for sidelit spaces. Since the design sequence uses the daylight factor as a performance metric, it is aimed towards spaces that primarily receive diffuse daylight. It should be complemented by a design analysis that looks at direct sunlight for glare and energy considerations. The sequence interconnects and refines earlier proposed rules of thumb and is intended to be used during the earliest design stages when concepts regarding programming, floor plans, massing and window areas are initially explored. All steps within the sequence were ‘validated’ using Radiance simulations of over 2300 sidelit spaces. During step one of the sequence the effective sky angles are calculated and target daylight factors are defined for all potential daylit zones within a building. In step two a refined version of the ‘daylight feasibility study’ is used to help the design team to identify building zones with high daylighting potential based on a target mean daylight factor criterion. During step three suitable interior room dimensions and surface reflectances are determined using a combination of the Lynes’ limiting depth, ‘no sky line’, and window-head-height rules of thumb. Step four provides a more accurate estimate of the required glazing area for each zone based on the Lynes daylight factor formula which is also validated as part of this work. The effect of external obstructions is considered throughout the process. The paper closes with a discussion of the merits of the design sequence compared to the glazing factor spreadsheet calculation method promoted by LEED-NC 2.2.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".