A hypothetical comparative evaluation system for arctic indoors
Bibliographic record
Abstract
This research presents an innovative approach to evaluating indoor spaces, combining qualitative attributes with numerical architectural metrics. A hypothetical comparative visualization system is introduced, utilizing HDR visual imaging and thermal imaging in 360° field of view across multiple indoor environments. The study aims to provide architects and occupants with a user-friendly tool informing them about the primary considerations of their built spaces, with a specific focus on indoor environmental qualities in remote Arctic regions. Key inquiries delve into the efficacy of the spherical approach and the capacity of comparative visualization to offer insights into space quality. Preliminary experiments contrast indoor environments in terms of circadian lighting, thermal uniformity, and view access to outside in the 360° field of view (VAR360). The resulting visualizations hold significance in introducing an immersive approach for depicting specific non-visible environmental qualities, particularly in relation to the window characteristics of spaces. It demonstrates the integration of multiple environmental variables, both steady-state and temporal, from central points within spaces, providing a comprehensive view over their non-visible qualities. These results should be useful for researchers and practitioners within building sciences, computer vision, and photobiology, showcasing an out-of-the-box approach for categorizing indoor spaces based on standards and human-environmental qualifications.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.004 |
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".