Effects of interior wood finishes on the lighting ambiance and materiality of architectural spaces
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
Wood is a material often used by architects to enhance the overall ambience of a space, but few researches have been reported to discuss its actual impact on visual impression and luminous effects. This research studies the influence of wood materiality in relation to creating specific lighting ambiances in architecture. In particular, it focuses on the impact of decorative wood indoor panels on the creation of daylighting diversity in interior space and the potential to improve daylighting quality and energy efficiency. The research uses scaled models for their accuracy in rendering complex daylighting ambiances. The photo-luminance meter enables the comparison between different settings of interior spaces created by a selection of wood type materiality: ratio (percentage), colour (Oak, Cape Cod Grey and Dark Walnut coatings) and gloss concerning illuminance patterns obtained from Ecotect software. The CIE L*a*b* colour space is used to classify luminous ambiances. Results indicate that bright colour Oak favours a deeper daylighting penetration and increases the colour temperature of the space by about 300% when applied on the floor. Cape Cod Grey coating provided a neutral colour balance even under sunlighting. High gloss Dark Walnut located on the ceiling produces the highest luminance values, enlarging the window-lighting pattern. The research underlines the role of wood materiality in achieving luminous diversity and creating visually comfortable interior ambiances.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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