Exploring light and colour patterns for remote biophilic northern architecture
Bibliographic record
Abstract
This research explores the effects of light in terms of colour, surface colour configuration and finishes using simple and advanced methods in the development of biophilic lighting ambiances for remote northern architecture. Biophilic light and colour design can benefit inhabitants of subarctic regions, where drastic changes in the natural photoperiod can impact the mind and body. To predict the outcomes of light and colour, this research used reduced-scale models that replicate a north-oriented room and a specially designed mirror-box sky simulator, which emulates the lighting conditions and correlated colour temperature (CCT) of a northern sky. Physical models with distinct surface colour properties and the use of high dynamic range imagery (HDRi) techniques allow the recognition of quantitative effects and lighting attributes of main hue families such as red, green, blue and yellow. The results reveal that the colour and the surface colour configuration significantly modify the spectral properties of a lit ambiance measured in Equivalent Melanopic Lux (EML) and CCT. Surface colour configuration and finishes produce variations in the luminous attributes measured in intensity contrast. This combination of simple and innovative tools could predict light and colour effects in early design stages for responsive architecture in subarctic territories.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".