Towards a framework for light-dosimetry studies: Methodological considerations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 it