Evaluating Spatial Attributes of Surface Colors Under Daylight and Electrical Lighting in Sustainable Architecture
Bibliographic record
Abstract
This paper investigates the spatial attributes of the color properties and brightness characteristics of sustainable architectural strategies including daylight, electrical lighting, and surface color in architecture, which could potentially impact users’ spatial experiences. Images of 48 spaces varying in surface color configurations, type of light source, and position of the lighting strategy were evaluated. The analyses included assessments of color palettes, descriptors based on saturation and brightness properties, and brightness distribution maps. The results indicate that lighting design and types of light source influence the saturation and brightness properties of the perceived hues evaluated in the same environment, leading to variations in color descriptors or adjectives. Furthermore, this study demonstrates that variations in brightness between bright and dark zones, the creation of focal points, and perceived spatial fragmentation depend on the reflectance of the colors applied in the surfaces, the position of the lighting, and the type of light source. This study does not aim to establish best practices for enhancing users’ emotions through architecture. Instead, it explores how variations in color and light influence perceptual descriptions that have been previously associated with emotional responses. This research recognizes the impact of sustainable strategies including surface colors under daylight and electrical lighting on users’ spatial experiences.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".