Towards a framework for light-dosimetry studies: Methodological considerations
Bibliographic record
Abstract
For field research of non-visual effects of light, accurate measurement of personal light exposure is required. A consensus framework for light-dosimetry could improve non-visual field research and ensure comparability between studies. Here, we present a review of methodologies used in non-visual light-dosimetry studies published to date, focussing on considerations regarding the measurement and preparation of personal light exposure data. Overall, a large variability in the studies’ methodologies is observed, highlighting the need for a consensus framework. We propose methodological considerations that should be included in such a framework and that can guide future studies. Furthermore, we highlight important points that should be addressed in future research to ensure compatibility between different dosimetry studies. Taken together, this review effort underlines the importance of a systematic approach to light-dosimetry in order to harness all the power of integrative lighting research in real life.
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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 | 0.584 | 0.575 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.016 | 0.011 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.014 | 0.013 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".