Imagery dataset of interior and exterior northern architectural spaces for photobiological lighting analyses
Bibliographic record
Abstract
This paper details an imagery dataset of interior and exterior ambiances to assess and represent photobiological outcomes of the built environment in northern territories. The images were obtained using a Raspberry Pi Camera Module (RPiCM) mounted in a holder that fixes the camera in place. This holder allows to rotate the camera by 30° and take 12 high dynamic range (HDR) images which are then combined to create a panoramic image. The HDR images enable the calculation of photobiological effects concerning photopic light intensity for vision, and the spectral dominance regarding vision and circadian stimulation. This dataset includes 13 captures in 7 interior and 6 exterior settings, each divided into 4 subfolders containing the photographic data: the sequence of low-dynamic range images (LDR), the tone-mapped images obtained from the HDR calculation, the analysis of photopic luminance and false color, and 360° panoramic images (tone-mapped HDR, false color luminance, and spectral dominance). Each space is also supplemented with photometric data presented as a .csv file containing lux and EML units obtained via a radiometer. This dataset is valuable for architects, designers, and neuroscientists to identify opportunities for enhancing human-centric lighting in existing architecture and landscape, as well as to propose solutions that promote vision and circadian stimulation in northern territories. This research was partially used in previous studies from [10]. The dataset is published and shared through a Mendeley repository [9].
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.015 |
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".