Spatiotemporal light exposure modeling for environmental circadian misalignment and solar jetlag
Bibliographic record
Abstract
Background: Light exposure is the most powerful resetting signal for circadian rhythms. The objective of this study was to develop and validate a high-resolution geospatial light exposure model that measures environmental circadian misalignment (or solar jetlag) as the mismatch between the social clock and sun clock, which occurs from geographic variation in light exposure leading to delayed circadian phase from relatively less morning light exposure and greater evening light exposure with increasing westward position within a time zone. Methods: The light exposure model (30 m 2 spatial resolution) incorporated geospatial data across the United States on time zones, elevation (using Google Earth Engine), sunrise time, and sunset time to estimate solar jetlag scores (higher values indicate higher environmental circadian misalignment). The validation study compared the light exposure model in 2022, which was linked with geocoded residential addresses of n = 20 participants in Boston, MA (eastern time zone position) and Seattle, WA (western time zone position) using a geographic information system, with illuminance values captured from wearable LYS light sensors and with sun times from the Solar Calculator. Results: Western versus eastern positions within a time zone were associated with higher solar jetlag scores from the light exposure model ( P < 0.01) and relatively larger differences in sunset time measured using light sensors (social clock) and the Solar Calculator (sun clock) ( P = 0.04). Conclusion: We developed and validated a geospatial light exposure model, enabling high spatiotemporal resolution and comprehensive characterization of geographic variation in light exposure potentially impacting circadian phase in epidemiologic studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".