Detection of the stroboscopic effect by young adults varying in sensitivity
Bibliographic record
Abstract
The advent of LED lighting has renewed concern about the possible visual, neurobiological, and performance and cognition effects of cyclic variations in lighting system luminous flux (temporal light modulation). The stroboscopic visibility measure (SVM) characterises the temporal light modulation signal to predict the visibility of the stroboscopic effect, one of the visual perception effects of temporal light modulation. A SVM of 1 means that the average person would detect the phenomenon 50% of the time. There is little published data describing the population sensitivity to the stroboscopic effect in relation to the SVM, and none focusing on people subject to visual stress. This experiment, conducted in parallel in Canada and France, examined stroboscopic detection for horizontal and vertical moving targets when viewed under commercially available lamps varying in SVM conditions (SVM: ∼0; ∼0.4; ∼0.9; ∼1.4; ∼3.0). As expected, stroboscopic detection scores increased with increasing SVM. For the horizontal task, average scores were lower than the expected 4/8 at ∼0.90, but increased non-linearly with higher SVMs. Stroboscopic detection scores did not differ between people low and high in pattern glare sensitivity, but people in the high-pattern glare sensitivity group reported greater annoyance in the SVM ∼1.4 and ∼3.0 conditions.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".