A rules of thumb-based design sequence for diffuse daylight
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it