Imagery dataset of interior and exterior northern architectural spaces for photobiological lighting analyses
Bibliographic record
Abstract
This dataset presents images of interior and exterior ambiances to calculate and represent the photobiological responses of existing architecture in northern territories. The images were obtained using a Raspberry Pi Camera Module (RPiCM) mounted in a holder designed to position the camera at a fixed point. This holder allows to rotate the camera by 30° and take 12 high dynamic range (HDR) images to create a panoramic image. The HDR images facilitated the analysis of photobiological effects concerning photopic light intensity for vision, and the spectral dominance regarding vision and circadian stimulation. This dataset consists of 13 captures in 7 interior spaces and 6 exterior spaces, each divided into 4 subfolders containing the photographic data: the sequence of low-dynamic range images (LDR) to form the high-dynamic range images (HDR), the tone-mapped images obtained from the HDR, 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 which contains lux and EML units obtained through a radiometer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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) | 0.021 | 0.033 |
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".