An ipRGC-influenced/Non-Visual Spectral Occupant Model for lighting design, Part 2: Photobiological model implementation
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 pilots a method for the assessment of non-visual lighting effects based upon annual full-spectrum lighting calculations termed an ipRGC-influenced/Non-Visual Spectral Occupant Model (iNSOM). iNSOM calculates annualized melanopic irradiance, described in our previous paper, and derives seasonal and time-of-day metrics based on a collection of photobiological models from Postnova et al ., Abeysuria et al . and Tekieh et al . to predict circadian dynamics, alertness and melatonin levels due to light exposure. Quantitative outputs of these metrics and novel spatial visualizations are then used to evaluate lighting design based on the predicted intrinsically photosensitive retinal ganglion cell (ipRGC)-influenced effect on occupants. The model is demonstrated using an example hospital ward model and tested under three daylight, electric light and screen device operational scenarios and two types of sleep quality. A comparative analysis between iNSOM and existing ipRGC-influenced lighting design metrics and standards demonstrates how ipRGC-influenced alertness and health metrics differ from existing saturation-based ipRGC-influenced lighting metrics.
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 | 0.003 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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