An ipRGC-influenced/Non-Visual Spectral Occupant Model for lighting design, Part 2: Photobiological model implementation
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.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".