Building Circadian Effective Spectra: An Open Source C Language Toolkit
Bibliographic record
Abstract
Intrinsic circadian clocks control the sleep patterns of most species on the earth. Having a natural period ~10’ longer than 24 h, they must be reset to the natural day/night cycle daily. The critical input (Zeitgeber) “resetting” this internal clock is the temporal variation of the spectrum and intensity of light across the 24-h day. To develop artificial lighting that mirrors sunlight in an individually tailored, dynamic way to control melatonin suppression correctly and provide adequate vision on a 24-h circadian cycle, open-source code is needed to adjust and optimize the weighting of the various LEDs chosen for the system. Spectral differences between batches of LEDs and differences in room layout require code to fine tune the weightings of the LEDs. Making use of existing spreadsheets (CIE α-opic, CIE1931, CCT, Duv Tolerance (Duv (T)) (CIE 2017)) and the data concerning the LEDs spectral power distribution (SPD), power consumption, cost and spectra, open-source C-code was written to build spectra, calculate a spectrum’s visual and nonvisual optical parameters along with power consumption (a key practical concern). An evaluation function, suitable for use in either brute-force or AI-assisted optimization, was written. As an example, this was used to optimize parameters to aid in establishing tailored 24-h dynamic white lighting systems. Hardware was developed to implement the optimized spectra, and the lighting system was deployed in a long-term care environment.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.045 | 0.039 |
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".