Dataset of images for visual and non-visual analysis of colour applications in architecture
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper describes three datasets which include 443 folders and approximately 4430 images. The images were obtained from the interior of a 1:50 scale model using a fisheye camera connected to a Raspberry Pi microcomputer. This dataset aims to analyze the photobiological effects (visual and non-visual) of the interplay between coloured surfaces and different types of lighting strategies. The experiments were conducted under three types of light sources: simulated daylight through a mirror-box artificial sky simulator, direct daylight, and an electric lighting system that allows for colour temperature modification. This dataset includes low dynamic range images to generate high dynamic range images, which in turn can be used to plot false colour maps concerning photopic luminance, melanopic luminance, CCT of an image, M/P ratio, and brightness distribution maps. This dataset can be useful for architects, interior designers, and building engineers to integrate lighting and colour strategies according to the visual and non-visual needs of the users. This research was partially used in the research of Espinoza-Sanhueza et al. [1,2]. The datasets are published and shared through a Mendeley repository [3].
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it