Solid state lighting annex: visual perception under energy-efficient light sources: detection of the stroboscopic effect under low levels of SVM: final report
Bibliographic record
Abstract
Temporal Light Modulation (TLM) or “flicker” as it is colloquially known can have visual, neurobiological, performance, and cognitive effects on viewers. The International Electrotechnical Commission and the Commission Internationale de l’Eclairage have identified two metrics that may be used to characterize lighting systems’ TLM: PstLM, to predict visible flicker at frequencies below 80 Hz; and SVM, Stroboscopic Visibility Measure, for the higher frequency stroboscopic effect. While scientific development of these metrics and their associated measurement protocols continue, there is a parallel discussion taking place concerning the appropriate levels for these metrics in regulations. The SSL Annex commissioned a study from researchers at the National Research Council of Canada (NRC) and the Centre Scientifique et Technique du Bâtiment (CSTB) in France, to investigate detection rates for the stroboscopic effect in response to commercially-available light emitting diode (LED) light sources characterized using the SVM, and particularly to assess the population variability in this perception among people sensitive to visual stress. The final report on this work provides insight into both stroboscopic visibility and the acceptability of the conditions for the full sample of 85 people and for the more sensitive individuals. The report follows the publication of a peer-reviewed journal article in Lighting Research and Technology.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.028 |
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".