Risk assessment for LED lighting flicker
Bibliographic record
Abstract
Background LED (Light Emitting Diode) based lighting has been predicted to reach as much as 60% share of the global lighting market in the next 10 years. It is characterized by exceptional lifetime and excellent energy efficiency. However potential health concerns have been associated with flicker in some LED lighting technologies. Aims/Objectives/Purpose The IEEE PAR1789 Working Group has undertaken a risk assessment of potential hazards associated with flicker in LED lighting as part of an effort to develop Recommended Practices of Modulating Current in High Brightness LEDs for Mitigating Health Risks to Viewers. Methods Information on potential health effects of flicker was collected through an extensive literature review and consultation with experts. A risk assessment was conducted following the Eurosafe framework model of risk assessment. Results/Outcome Potential adverse effects of flicker include seizure, stroboscopic effects, migraine, exacerbation of repetitive behaviour in persons with autism, and asthenopic effects including eyestrain, fatigue, and reduced performance on visual tasks. Some health effects are well understood in terms of susceptible subgroups, prevalence and influential parameters while other potential hazards are less extensively studied. Therefore the risk assessment incorporates informational certainty as well as probability and severity of potential effects. Significance/Contribution to the Field Enable confidence in safe use and guidance for safe design of an environmentally important lighting 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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".