Representing the photobiological dimension of light in northern architecture
Bibliographic record
Abstract
Daylight can enhance the quality and inhabitability of architecture through a better relationship with the exterior environment, especially by its intensity, chromaticity and ability to synchronize the human circadian clock. Daylight integration in architecture remains a challenge in Nunavik (Quebec, Canada) due to its subarctic climate, photoperiod and solar geometry. The objective of this research is to implement photobiological metrics of light in architectural representations by isolating the photopic (daytime vision) and melanopic (circadian clock) portions of the electromagnetic spectrum, and to spatialize daylight and artificial light in relation to landscapes and indoor architectural spaces. An automated and low-cost capture tool based on Raspberry Pi microcomputers and Camera Modules (RPiCM) captures high dynamic range images, which accurately measure luminance to render human perception. Absolute photopic luminance maps (cd/m 2 ) are supplemented with false colour displays of photopic/melanopic contents of light regarding building surface materials. The research develops photometric captures of absolute photopic and melanopic illuminance (lux, EML). Photobiological metrics of light are integrated into a set of physical properties of lighting patterns to perform light assessments and are ultimately represented as a graphical display to help designers and researchers to evaluate architectural interior–exterior relationships through daylight qualities.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".