A post-occupancy evaluation of the influence of wood on environmental comfort
Bibliographic record
Abstract
Emerging research on the impact of interior finishes, more specifically wood, is beginning to shed light on informed design opportunities. As a natural building material with unique features, wood creates warm and pleasant atmospheres and has the potential of enhancing the well-being of occupants. This research attempted to better understand how occupants perceive wood in built environments and whether its indoor use influenced the satisfaction of occupants. The comfort of occupants may have been influenced by factors that were unrelated to the indoor environmental quality, which caused difficulties in comparing interior finishes in existing environments and limited research in this field. An exploratory comparative study, within a post-occupancy evaluation framework, investigated the subjective perception of occupants in relation to physical comfort factors. Thirty-six occupants completed a questionnaire to examine comfort satisfaction in a multifunctional room with extensive wooden interior finishes in comparison with a similar space without wood surfaces. The results indicated that occupants were more satisfied in the extensive wood surfaced room in terms of lighting, noise, and temperature, despite the similar environmental conditions in both spaces. Adjectives often used to describe the wood room included bright, pleasant, modern, and warm. Architects should consider the subjective qualities of wood when designing comfortable buildings.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".