Experiencing Wooden Ambiences with Nordic Light: Scale Model Comparative Studies under Real Skies
Bibliographic record
Abstract
This study explored the potential of natural light to enhance wooden interior environments. Under Nordic light, natural materials such as wood finishes present an opportunity to create warm, bright, and pleasant atmospheres, enhancing psychological well-being and comfort. The objectives of this project were twofold: first, to study the diversity of northern sky conditions in terms of cloud cover and thickness, and, second, to evaluate the impacts of the diversity of natural light on five wooden scale models. The methodology involved weather data collection that took place during the spring equinox in Quebec City. In order to create a cloudiness scale, sky condition data and photometric measurements were collected. A photographic survey occurred in five scale models made with interior wooden finishes of varying color combinations, documenting the impact of sky diversity on brightness, hue, and contrast. Simultaneous scale model studies under a real sky allowed direct comparison under the same lighting conditions. There was a remarkable diversity of visual ambiences for a southeast-oriented space depending on the position of the sun and sky conditions. Gray-dyed wooden finishes created dull and unchanging atmospheres, while yellow oaked surfaces allowed various dynamic ambiences.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".