Analysis of diurnal changes in pupil dilation and eyelid aperture
Bibliographic record
Abstract
This work is inspired by the observation of surprising daily fluctuations in the number of valid iris code bits used to match irises in the NEXUS program operated by the Canadian Border Security Agency. These fluctuations have an impact on iris comparison scores but cannot be simply explained by pupil dilation, which does not have a clear pattern that would generalise to a population. To check if fluctuations in the number of valid iris code bits may be explained by eyelid aperture observed in a controlled, laboratory environment, the eyelid aperture was measured for 18 subjects participating in an acquisition every 2 h during the day. Simultaneously, the pupil dilation was measured to check the existence of a daily pattern for a population and for single subjects. There are two interesting outcomes of this work. First, there are statistically significant changes during the day in both pupil dilation and eyelid opening observed for individual subjects. Second, these changes do not generalise well into a common pattern for the group. Consequently, the diurnal fluctuations in the number of bits compared and the comparison score observed in the NEXUS program cannot be explained by changes in pupil dilation nor by eyelid aperture.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".