Effects of interior wood finishes on the lighting ambiance and materiality of architectural spaces
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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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".